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From genes to dynamics: Examining brain cell types in action may reveal the logic of brain function

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Liset de la Prida is director of the Centro de Neurociencias Cajal in Madrid, Spain, where she runs the Laboratory of Neural Circuits. Today we discuss two main topics.

What drew me to invite Liset was her work on neural manifolds, which we've talked about a lot recently on this podcast. She studies how specific subtypes of neurons affect and control neural manifolds. More on that it in a second, because what drew her to study manifolds was her work on what are known as sharp wave ripples in the hippocampus. Sharp wave ripples are generally quick bursts of oscillatory activity as found in local field potential recordings that accompany little bursty sequences of action potentials fired off by sets of neurons. Those ripples have been associated with a quick replaying of some experience an organism has had, with the thinking that by replaying those sequences of neural activity associated with an event, it's helping to consolidate the memory for that event in the cortex. Like everything else, the story isn't so simple, and we talk about some of the findings that have added to the complexity of understanding what sharp wave ripples are doing, and the varieties of sharp wave ripples.

That varieties part is related to the second main thing we discuss, which is the varieties of neuron subtypes and their roles in shaping the manifolds we've discussed a lot recently. As a reminder, manifolds are dynamic structures along which populations of neural activity unfold over time, and they have proved to be one effective way of making sense of how large populations of neurons coordinate their activity to do useful things for our cognition. Liset is interested in the relation between sharp wave ripples and manifolds, and in how specific subtypes of neurons affect manifolds and cognition in general.

• Neural Circuits Lab
• @lmprida.bsky.social; @LMPrida 
• Book:
  • Brain, space and time: The neuroscience of how we navigate reality, memory, or the future
• Related
  • From genes to dynamics: Examining brain cell types in action may reveal the logic of brain function
  • Cell-type-specific manifold analysis discloses independent geometric transformations in the hippocampal spatial code
  • From cell types to population dynamics: Making hippocampal manifolds physiologically interpretable

0:00 - Intro 5:29 - Hippocampus 9:31 - Sharp wave ripples 27:30 - Oscillations and epiphenomena 33:37 - Sharp wave ripples to manifolds 43:54 - Manifolds and single neuron types 49:45 - Hippocampus and granularity of cell types 59:23 - Explanation across levels 1:19:38 - Manifolds and higher cognition 1:29:46 - Brain Space and Time

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Brains encode information in representations that perform computations to make predictions, right? No, no, no, and no. That's Romain Brette's response to those ill-conceived notions that neuroscience relies on to try to explain how cognition works. He uses more words to do that in his new book, The Brain, in Theory, which we discuss today. In the book Romain breaks down how many of the common metaphors we use don’t withstand scrutiny, and he offers alternative approaches more in line with what we know about how biological entities work. Along those lines, we discuss his ongoing work understanding the cognition of a single celled organism, the paramecium, and what his views might mean for artificial intelligence. This is a long episode, but there's a lot more to be explored in the book, so I recommend you read it. If you're a patreon supporter, I coaxed Romain back on for another 45 minutes to go deeper on his thoughts about how anticipation is the core of cognition, how predictive processing accounts like active inference miss the mark, and a few other topics.

• Romain's website. 
• The Brain, in Theory.

0:00 - Intro 4:01 - The Brain, In Theory 7:10 - Influences 13:11 - Process metaphysics 18:39 - Observer vs system perspective 21:24 - Information in the brain? 22:56 - Why this book? 29:52 - Computations in the brain 52:14 - Behavior is not a computation 1:07:20 - Paramecium cognition 1:22:02 - How should neuroscientists proceed? 1:29:09 - Cognition as collective behavior of autonomous cells 1:36:47 - Constraints, causes, and laws 1:52:36 - Hopes for the book to influence the field 1:55:04 - Thoughts about AI 2:02:13 - Computation and goals 2:08:17 - Anticipation vs prediction

The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.

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Check out this story: Neural manifolds: Latest buzzword or pathway to understand the brain?

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Juan Gallego runs the Neocybernetics Lab at the Champalimaud Centre for the Unknown in Lisbon, Portugal, affiliated with the neuroscience of disease and neuroscience programs, and the centre for restorative neurotechnology.

Juan has worked a lot on neural manifolds - the mathematical objects neuroscience is using more and more to describe how big populations of neurons coordinate their activity to do useful things. In fact, he recently gave a short talk that he titled The Manifold Manifesto, because he was asked to be provocative. And he was provocative, suggesting that manifolds are real - as real as chairs and tables are, that they have causal power, and they might be a target of evolution. Of course he talked about his own and others work to support those claims. So today we discuss many of those themes, through the lens of his own and others work, and we talk about what keeps him up at night about the possible limits of using manifolds to connect brain activity with behavior and mental phenomena.

He's not just a manifold person, though. Juan is more broadly interested in motor control and how brains do it.

We also discuss his work in patients with spinal cord injuries, who don't have enough nerve connections to their muscles to actually move, but have enough nerve connections that some signal gets through. Juan and his colleagues can detect that little bit getting through, and use it to infer what behaviors the patients intend to do, and they can use that information to control actions in a computer simulation. The hope is that this will translate to controlling prosthetics to give spinal cord injury patients their mobility again.

• Neocybernetics Lab.
• @juangallego.bsky.social
• Related papers
  • A neural manifold view of the brain.
  • A neural implementation model of feedback-based motor learning.
  • Conjoint specification of action by neocortex and striatum.
  • Integrating across behaviors and timescales to understand the neural control of movement.
  • Evolutionarily conserved neural dynamics across mice, monkeys, and humans.

Read the transcript.

0:00 - Intro 4:37 - Manifolds 14:30 - Strengths and weaknesses 24:32 - Conserved manifolds across animals and species 34:31 - Causality and manifolds 47:29 - Constraints and causes 51:05 - What to measure 58:55 - Complexity and manifolds 1:10:29 - Juan's background 1:14:08 - Prosthetics for spinal cord injuries 1:41:06 - Integrating across behaviors and timescales 1:46:56 - Conjoint specification of action by neocortex and striatum.

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Tom Griffiths directs both the Computational Cognitive Science Lab and the Princeton Laboratory for Artificial Intelligence at Princeton University. He's been on brain inspired before to talk about his previous book Algorithms to Live By: The Computer Science of Human Decisions, which he co-wrote with Brian Christian. Today he's here to talk about his new book, The Laws of Thought: The Quest for a Mathematical Theory of the Mind. In this book, Tom explains how the three pillars of logic, neural networks, and probability theory complement each other to explain cognition, arguing we are on the doorstep to settling what mathematical principles - the so-called "laws of thought" - underly our cognition. So we discuss a little bit about a lot of things, including the concepts themselves, the people who have generated and worked on those concepts. I should also mentioned, Tom recorded a bunch of his interviews with people he writes about, and he's edited and polished those into a podcast called the Cognition Project, which I have enjoyed after reading the book, and I think you'd enjoy it either before or after you read the book.

• Computational Cognitive Science Lab
• Princeton Laboratory for Artificial Intelligence
• Social: @cocosci_lab; @cocoscilab.bsky.social
• Book:
  • The Laws of Thought: The Quest for a Mathematical Theory of the Mind.
• Podcast: The Cognition Project

Read the transcript.

0:00 - Intro 3:20 - Tom's approach 7:19 - 3 pillars of the laws of thought 28:24 - Logic and formal systems strip away meaning 39:04 - Nature of thought 50:35 - Kahneman and Tversky 1:015:12 - Enabling constraints and inductive bias 1:12:51 - Hidden layers, probability, and hidden markov models 1:20:47 - Conscious vs nonconscious 1:23:43 - Feelings 1:31:26 - Personal

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How does brain activity explain your perceptions and your actions? That's what neuroscientists ask. How does the interaction between brain, body, and environment explain your perceptions and actions? That's what ecological psychologists ask… sometimes leaving the brain out of the equation altogether. These different approaches to perception and action come with different terms, concepts, underlying assumptions, and targets of explanations.

So what happens when neuroscientists are inspired by ecological psychology but don't necessarily want take on, or are ignorant of, the fundamental principles underlying ecological psychology?

This happens all the time, like how AI was "inspired" by the most rudimentary understanding of how brains work, and took terms from neuroscience like neuron, neural network, and so on, as stand-ins for their models. This has in some sense re-defined what people mean by neuron, and neural network, and how they function and how we should think of them.

Modern neuroscience, with better data collecting tools, has taken a turn toward more naturalistic experimental paradigms to study how brains operate in more ecologically valid situations than what has mostly been used in the history of neuroscience - highly controlled tasks and experimental setups that arguably have very little to do with how organisms evolved to interact with the world to do cognitive things.

One problem with this turn is that we neuroscientists don't have ready-made theoretical tools to deal with the less constrained massive amounts of data the new approach affords. This has led some neuroscientists to seek those theoretical concepts elsewhere. One of those places that offers those theoretical tools is ecological psychology, developed by James and Eleanor Gibson in the mid-20th century, and continued since then by many adherents of the concepts introduced by ecological psychology. Those concepts are very specific with regard to how and what to explain regarding perception and action.

Matthieu de Wit is an associate professor at Muhlenberg College in Pennsylvania, who runst the ECON Lab, as in Ecological Neuroscience. Luis Favela is an associate professor at Indiana University. He's been on before to talk about his book The Ecological Brain. And Vicente Raja is a research fellow at University of Murcia in Spain, and he's been on before to talk about ecological psychology and neuroscience.

With their deep expertise in ecological psychology, they are keenly interested in how neuroscience write large adopts various facets of ecological psychology. Do neuroscientists have it right? Do they need to have it right? Is there something being lost in translation? How should neuroscientists adopt ecological psychology for an ecological neuroscience? That's what we're discussing today.

More broadly, this is also a story about what it's like doing research that isn't part of the current mainstream approach, in this doing ecological psychology under the long shadow cast by the computational mechanistic neuro-centric dominant paradigm in neuroscience currently.

• Matthieu de Wit lab.
  • @dewitmm.bsky.social
• Luis Favela.
  • The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment
• Vicente Raja
  • @diovicen.bsky.social
  • MINT Lab.
  • Ecological psychology 
• Previous episodes:
  • BI 223 Vicente Raja: Ecological Psychology Motifs in Neuroscience
  • BI 190 Luis Favela: The Ecological Brain
  • BI 213 Representations in Minds and Brains

Read the transcript.

0:00 - Intro 8:23 - How Louie, Vicente, and Matthieu know each other 11:16 - Past present and future of relation between neuroscience and ecological psychology 17:02 - Why resistance to integrating neuroscience into ecological psychology? 28:26 - What counts as ecological psychology? 33:32 - Affordances properly understood 40:33 - Ecological information 47:58 - Importance of dynamics 48:59 - What's at stake? 58:27 - Environment intervention 1:16:21 - When ecological neuroscience publishes 1:31:25 - Neuroscientists escape hatch 1:38:04 - Is ecological psychology a theory of everything?

The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.

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Jaan Aru is a co-principal investigator of the Natural and Artificial Intelligence Lab at the University of Tartu in Estonia, where he is an associate professor. Jaan's name has kept popping up on papers I've read over the last few years, sometimes alongside other guests I've had on the podcast, like Matthew Larkum and Mac Shine. With those people and others, he has co-authored papers exploring how some of the pesky biological details of brains might be important for our subjective conscious experience, details like dendritic integration, and loops between the cortex and the thalamus. Turns out a recurring theme in his work is to connect lower-level nitty gritty biological details with higher level cognitive functioning. And he has some thoughts about what that might mean for the prospects of consciousness in  artificial systems. And we also touch on his more recent interest in understanding the brain basis of insight and creativity, connecting some of the more mundane kinds of insights during problem solving, for example, with some of the more profound kinds of insights during mystical and psychedelic experiences, for example.

• Natural & Artificial Intelligence Lab
• Social: @jaanaru.bsky.social
• Related papers
  • The feasibility of artificial consciousness through the lens of neuroscience
  • On biological and artificial consciousness: A case for biological computationalism
  • Cellular mechanisms of conscious processing.
  • Realization experiences: a convergent account of insight and mystical experiences.

0:00 - Intro 4:21 - Jaan's approach 8:51 - Likelihood of machine consciousness 18:58 - Across-levels understanding 30:23 - Intelligence vs consciousness 36:27 - Connecting low-level implementation to cognition 45:42 - Organization and constraints 52:28 - Thalamocortical loops 1:04:18 - Artificial consciousness 1:14:34 - Theories of consciousness 1:23:16 - Creativity and insight 1:37:26 - Science research in Estonia

The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.

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Michael Shadlen is a professor of neuroscience in the Department of Neuroscience at Columbia University, where he's the principle investigator of the Shadlen Lab. If you study the neural basis of decision making, you already know Shadlen's extensive research, because you are constantly referring to it if you're not already in his lab doing the work. The name Shadlen adorns many many papers relating the behavior and neural activity during decision-making to mathematical models in the drift diffusion family of models. That's not the only work he is known for,

As you may have gleaned from those little intro clips, Michael is with me today to discuss his account of what makes a thought conscious, in the hopes to inspire neuroscience research to eventually tackle the hard problem of consciousness - why and how we have subjective experience.

But Mike's account isn't an account of just consciousness. It's an account of nonconscious thought and conscious thought, and how thoughts go from non-conscious to conscious

His account is inspired by multiple sources and lines of reasoning.

Partly, Shadlen refers to philosophical accounts of cognition by people like Marleau-Ponty and James Gibson, appreciating the embodied and ecological aspects of cognition.

And much of his account derives from his own decades of research studying the neural basis of decision-making mostly using perceptual choice tasks where animals make eye movements to report their decisions.

So we discuss some of that, including what we continue to learn about neurobiological, neurophysiological, and anatomical details of brains, and the possibility of AI consciousness, given Shadlen's account.

• Shadlen Lab.
• Twitter: @shadlen.
• Decision Making and Consciousness (Chapter in upcoming Principles of Neuroscience textbook).
• Talk: Decision Making as a Model of thought

Read the transcript.

0:00 - Intro 7:05 - Overview of Mike's account 9:10 - Thought as interrogation 21:03 - Neurons and thoughts 27:05 - Why so many neurons? 36:21 - Evolution of Mike's thinking 39:48 - Marleau-Ponty, cognition, and meaning 44:54 - Naturalistic tasks 51:11 - Consciousness 58:01 - Martin Buber and relational consciousness 1:00:18 - Social and conscious phenomena correlated 1:04:17 - Function vs. nature of consciousness 1:06:05 - Did language evolve because of consciousness? 1:11:11 - Weak phenomenology and long-range feedback 1:22:02 - How does interrogation work in the brain? 1:26:18 - AI consciousness 1:35:49 - The hard problem of consciousness 1:39:34 - Meditation and flow

The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.

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Tomaso Poggio is the Eugene McDermott professor in the Department of Brain and Cognitive Sciences, an investigator at the McGovern Institute for Brain Research, a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and director of both the Center for Biological and Computational Learning at MIT and the Center for Brains, Minds, and Machines.

Tomaso believes we are in-between building and understanding useful AI That is, we are in between engineering and theory. He likens this stage to the period after Volta invented the battery and Maxwell developed the equations of electromagnetism. Tomaso has worked for decades on the theory and principles behind intelligence and learning in brains and machines. I first learned of him via his work with David Marr, in which they developed "Marr's levels" of analysis that frame explanation in terms of computation/function, algorithms, and implementation. Since then Tomaso has added "learning" as a crucial fourth level. I will refer to you his autobiography to learn more about the many influential people and projects he has worked with and on, the theorems he and others have proved to discover principles of intelligence, and his broader thoughts and reflections.

Right now, he is focused on the principles of compositional sparsity and genericity to explain how deep learning networks can (computationally) efficiently learn useful representations to solve tasks.

• Lab website.
• Tomaso's Autobiography 
• Related papers
  • Position: A Theory of Deep Learning Must Include Compositional Sparsity
  • The Levels of Understanding framework, revised
• Blog post:
  • Poggio lab blog.
  • The Missing Foundations of Intelligence

Read the transcript.

0:00 - Intro 9:04 - Learning as the fourth level of Marr's levels 12:34 - Engineering then theory (Volta to Maxwell) 19:23 - Does AI need theory? 26:29 - Learning as the door to intelligence 38:30 - Learning in the brain vs backpropagation 40:45 - Compositional sparsity 49:57 - Math vs computer science 56:50 - Generalizability 1:04:41 - Sparse compositionality in brains? 1:07:33 - Theory vs experiment 1:09:46 - Who needs deep learning theory? 1:19:51 - Does theory really help? Patreon 1:28:54 - Outlook

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Alex is an associate professor of psychology at Vanderbilt University where he heads the Maier Lab. His work in neuroscience spans vision, visual perception, and cognition, studying the neurophysiology of cortical columns, and other related topics. Today, he is here to discuss where his focus has shifted over the past few years, the neuroscience of consciousness. I should say shifted back, since that was his original love, which you'll hear about.

I've known Alex since my own time at Vanderbilt, where I was a postdoc and he was a new faculty member, and I remember being impressed with him then. I was at a talk he gave - job talk or early talk - where it was immediately obvious how passionate and articulate he is about what he does, and I remember he even showed off some of his telescope photography - good pictures of the moon, I remember. Anyway, we always had fun interactions, even if sometimes it was a quick hello as he ran up stairs and down hallways to get wherever he was going, always in a hurry.

Today we discuss why Alex sees integration information theory as the most viable current prospect for explaining consciousness. That is mainly because IIT has developed a formalized mathematical account that hopes to do for consciousness what other math has done for physics, that is, give us what we know as laws of nature. So basically our discussion revolves around everything related to that, like philosophy of science, distinguishing mathematics from "the mathematical", some of the tools he is finding valuable, like category theory, and some of his work measuring the level of consciousness IIT says a whole soccer team has, not just the individuals that comprise the team.

• Maier Lab
• Astonishing Hypothesis (Alex's youtube channel)
• Twitter: 
• Sensation and Perception textbook (in-the-making)
• Related papers
  • Linking the Structure of Neuronal Mechanisms to the Structure of Qualia
  • Information integration and the latent consciousness of human groups
  • Neural mechanisms of predictive processing: a collaborative community experiment through the OpenScope program

• Various things Alex mentioned:
  • “An Antiphilosophy of Mathematics,” Peter J. Freyd youtube video about "the mathematical".
  • David Kaiser's playlist on modern physics.
• Here's a link to the Integrated Information Theory Wiki.

Read the transcript.

0:00 - Intro 4:27 - Discovering consciousness science 11:23 - Laws of perception 15:48 - Integrated information theory and mathematical formalism 23:54 - Theories of consciousness without math 28:18 - Computation metaphor 34:44 - Formalized mathematics is the way 36:56 - Category theory 41:42 - Structuralism 51:09 - The mathematical 54:33 - Metaphysics of the mathematical 59:52 - Yoneda Lemma 1:12:05 - What's real 1:26:22 - Measuring consciousness of a soccer team 1:35:03 - Assumptions and approximations of IIT 1:43:13 - Open science

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Can you look at all the synaptic connections of a brain, and tell me one nontrivial memory from the organism that has that brain? If so, you shall win the $100,000 prize from the Aspirational Neuroscience group.

I was recently invited for the second time to chair a panel of experts to discuss that question and all the issues around that question - how to decode a non-trivial memory from a static map of synaptic connectivity.

Before I play that recording, let me set the stage a bit more.

Aspirational Neuroscience is a community of neuroscientists run by Kenneth Hayworth, with the goal, from their website, to "balance aspirational thinking with respect to the long-term implications of a successful neuroscience with practical realism about our current state of ignorance and knowledge." One of those aspirations is to decoding things - memories, learned behaviors, and so on - from static connectomes. They hold satellite events at the SfN conference, and invite experts in connectomics from academia and from industry to share their thoughts and progress that might advance that goal.

In this panel discussion, we touch on multiple relevant topics. One question is what is the right experimental design or designs that would answer whether we are decoding memory - what is a benchmark in various model organisms, and for various theoretical frameworks? We discuss some of the obstacles in the way, both technologically and conceptually. Like the fact that proofreading connectome connections - manually verifying and editing them - is a giant bottleneck, or like the very definition of memory, what counts as a memory, let alone a "nontrivial" memory, and so on. And they take lots of questions from the audience as well.

I apologize the audio is not crystal clear in this recording. I did my best to clean it up, and I take full blame for not setting up my audio recorder to capture the best sound. So, if you are a listener, I'd encourage you to check out the video version, which also has subtitles throughout for when the language isn't clear.

Anyway, this is a fun and smart group of people, and I look forward to another one next year I hope.

The last time I did this was episode 180, BI 180, which I link to in the show notes. Before that I had on Ken Hayworth, whom I mentioned runs Aspirational Neuroscience, and Randal Koene, who is on the panel this time. They were on to talk about the future possibility of uploading minds to computers based on connectomes. That was episode 103.

• Aspirational Neuroscience
• Panel
  • Michał Januszewski
    • @michalwj.bsky.social
    • Research scientist (connectomics) with Google Research, automated neural tracing expert
  • Sven Dorkenwald
    • @sdorkenw.bsky.social
    • Research fellow at the Allen Institute, first-author on first full Drosophila connectome paper
  • Helene Schmidt
    • @helenelab.bsky.social
    • Group leader at Ernst Strungmann Institute, hippocampus connectome & EM expert
  • Andrew Payne
    • @andrewcpayne.bsky.social
    • Founder of E11 Bio, expansion microscopy & viral tracing expert 
  • Randal Koene
    • Founder of the Carboncopies Foundation, computational neuroscientist dedicated to the problem of brain emulation.
• Related episodes:
  • BI 103 Randal Koene and Ken Hayworth: The Road to Mind Uploading
  • BI 180 Panel Discussion: Long-term Memory Encoding and Connectome Decoding

The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.

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Tatiana Engel runs the Engel lab at Princeton University in the Princeton Neuroscience Institute. She's also part of the International Brain Laboratory, a massive across-lab, across-world, collaboration which you'll hear more about. My main impetus for inviting Tatiana was to talk about two projects she's been working on. One of those is connecting the functional dynamics of cognition with the connectivity of the underlying neural networks on which those dynamics unfold. We know the brain is high-dimensional - it has lots of interacting connections, we know the activity of those networks can often be described by lower-dimensional entities called manifolds, and Tatiana and her lab work to connect those two processes with something they call latent circuits. So you'll hear about that, you'll also hear about how the timescales of neurons across the brain are different but the same, why this is cool and surprising, and we discuss many topics around those main topics. 

• Engel Lab.
• @engeltatiana.bsky.social.
• International Brain Laboratory.
• Related papers:
  • Latent circuit inference from heterogeneous neural responses during cognitive tasks
  • The dynamics and geometry of choice in the premotor cortex.
  • A unifying perspective on neural manifolds and circuits for cognition
  • Brain-wide organization of intrinsic timescales at single-neuron resolution
  • Single-unit activations confer inductive biases for emergent circuit solutions to cognitive tasks.

0:00 - Intro 3:03 - No central executive 5:01 - International brain lab 15:57 - Tatiana's background 24:49 - Dynamical systems 17:48 - Manifolds 33:10 - Latent task circuits 47:01 - Mixed selectivity 1:00:21 - Internal and external dynamics 1:03:47 - Modern vs classical modeling 1:14:30 - Intrinsic timescales 1:26:05 - Single trial dynamics 1:29:59 - Future of manifolds

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Henk de Regt is a professor of Philosophy of Science and the director of the Institute for Science in Society at Radboud University. Henk wrote the book on Understanding. Literally, he wrote what has become a classic in philosophy of science, Understanding Scientific Understanding.

Henks' account of understanding goes roughly like this, but you can learn more in his book and other writings. To claim you understand something in science requires that you can produce a theory-based explanation of whatever you claim to understand, and it depends on you having the right scientific skills to be able to work productively with that theory - for example, making qualitative predictions about it without performing calculations. So understanding is contextual and depends on the skills of the understander.

There's more nuance to it, so like I said you should read the book, but this account of understanding distinguishes it from explanation itself, and distinguishes it from other accounts of understanding, which take understanding to be either a personal subjective sense - that feeling of something clicking in your mind - or simply the addition of more facts about something.

In this conversation, we revisit Henk's work on understanding, and how it touches on many other topics, like realism, the use of metaphors, how public understanding differs from expert understanding, idealization and abstraction in science, and so on.

And, because Henk's kind of understanding doesn't depend on subjective awareness or things being true, he and his cohorts have begun working on whether there could be a benchmark for degrees of understanding, to possibly asses whether AI demonstrates understanding, and to use as a common benchmark for humans and machines.

• Google Scholar page
• Social: @henkderegt.bsky.social;  
• Book:
  • Understanding Scientific Understanding.
• Related papers
  • Towards a benchmark for scientific understanding in humans and machines
  • Metaphors as tools for understanding in science communication among experts and to the public
  • Two scientific perspectives on nerve signal propagation: how incompatible approaches jointly promote progress in explanatory understanding

0:00 - Intro 10:13 - Philosophy of explanation vs understanding 14:32 - Different accounts of understanding 20:29 - Henk's account of understanding 26:47 - What counts as intelligible? 34:09 - Hodgkin and Huxley alternative 37:54 - Familiarity vs understanding 44:42 - Measuring understanding 1:02:53 - Machine understanding 1:16:39 - Non-factive understanding 1:23:34 - Abstraction vs understanding 1:31:07 - Public understanding of science 1:41:35 - Reflections on the book

The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.

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My guest today is Dan Nicholson, Assistant Professor of Philosophy at George Mason University, here to talk about his little book, What Is Life? Revisited. Erwin Schrödinger's What Is Life is a famous book that people point to as having predicted DNA and influenced and inspired many well-known biologists ushering in the molecular biology revolution. But Schrödinger was a physicist, not a biologist, and he spent very little time and effort toward understanding biology.

What was he up to, why did he write this "famous little book"? Schrödinger had an agenda, a physics agenda. He wanted to save the older deterministic version of quantum physics from the new indeterministic version. When Dan was on the podcast a few years ago, we talked about the machine view of biological systems, how everything has become a "mechanism", and how that view fails to capture what modern science is actually telling us, that organisms are unlike machines in important ways. That work of Dan's led him down this path to Schrödinger's What Is Life, which he argues was a major contributor to that machine metaphor so ubiquitous today in biology. One of the reasons I'm interested in this kind of work is because the cognitive sciences, including neuroscience and artificial intelligence, inherited this mechanistic perspective, and swallowed it so hard that if you don't include the word "mechanism" in your research paper, you're vastly decreasing your chances of getting your work published, when in fact the mechanistic perspective is one super useful perspective among many.

• Dan’s website. Google Scholar.
• Social: @NicholsonHPBio; @djnicholson.bsky.social
• What Is Life? Revisited
• Previous episode:
  • BI 150 Dan Nicholson: Machines, Organisms, Processes

Read the transcript.

0:00 - Intro 7:27 - Why Schrodinger wrote What is Life 15:13 - Aperiodic crystal and the meaning of code 21:39 - Order-from-order, order-from-disorder 28:32 - Appeal to authority 37:48 - Cell as machine 39:33 - Relation between DNA and organism (development) 44:44 - Negentropy 53:54 - Original contributions 58:54 - Mechanistic metaphor in neuroscience 1:16:05 - What's the lesson? 1:28:06 - Historical sleuthing 1:39:49 - Modern philosophy of biology

Vicente Raja is a research fellow at University of Murcia in Spain, where he is also part of the Minimal Intelligence Lab run by Paco Cavo, where they study plant behavior, and he is external affiliate faculty of the Rotman Institute of Philosophy at Western University. He is a philosopher, and he is a cognitive scientist, and he specializes in applying concepts from ecological psychology to understand how brains, and organisms, including plants, get about in the world.

We talk about many facets of his research, both philosophical and scientific, and maybe the best way to describe the conversation is a tour among many of the concepts in ecological psychology - like affordances, ecological information, direct perception, and resonance, and how those concepts do and don't, and should or shouldn’t, contribute to our understanding of brains and minds.

We also discuss Vicente's use of the term motif to describe scientific concepts that allow different researches to study roughly the same things even though they have different definitions for those things, and toward the end we touch on his work studying plant behavior.

• MINT Lab.
• Book: Ecological psychology
• Social: @diovicen.bsky.social
• Related papers
  • In search for an alternative to the computer metaphor of the mind and brain
  • Embodiment and cognitive neuroscience: the forgotten tales.
  • The motifs of radical embodied neuroscience
  • The Dynamics of Plant Nutation
  • Ecological Resonance Is Reflected in Human Brain Activity
  • Affordances are for life (and not just for maximizing reproductive fitness)
  • Two species of realism
• Lots of previous guests and topics mentioned:
  • BI 152 Michael L. Anderson: After Phrenology: Neural Reuse
  • BI 190 Luis Favela: The Ecological Brain
  • BI 191 Damian Kelty-Stephen: Fractal Turbulent Cascading Intelligence

Read the transcript.

0:00 - Intro 4:55 - Affordances and neuroscience 13:46 - Motifs 39:41- Reconciling neuroscience and ecological psychology 1:07:55 - Predictive processing 1:15:32 - Resonance 1:23:00 - Biggest holes in ecological psychology 1:29:50 - Plant cognition

The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.

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Nikolay Kukushkin is an associate professor at New York University, and a senior scientist at Thomas Carew’s laboratory at the Center for Neural Science. He describes himself as a "molecular philosopher", owing to his day job as a molecular biologist and his broad perspective on how it "hangs together", in the words of Wilfrid Sellers, who in 1962 wrote, “The aim of philosophy, abstractly formulated, is to understand how things in the broadest possible sense of the term hang together in the broadest possible sense of the term”.

That is what Niko does in his book One Hand Clapping: Unraveling the Mystery of the Human Mind.

This book is about essences across spatial scales in nature. More precisely, it's about giving names to what is fundamental, or essential, to how things and processes function in nature. Niko argues those essences are where meaning resides. That's very abstract, and we'll spell it out more during the discussion. But as an example at the small scale, the essences of carbon and oxygen, respectively, are creation and destruction, which allows metabolism to occur in biological organisms. Moving way up the scale, following this essence perspective leads Niko to the conclusion that there is no separation between our minds and the world, and that instead we should embrace the relational aspect of mind and world as a unifying principle. On the way, via evolution, we discuss many more examples, plus some of his own work studying how memory works in individual cells, not just neurons or populations of neurons in brains.

• Niko's website.
• Twitter: @niko_kukushkin.
• Book:
  • One Hand Clapping: Unraveling the Mystery of the Human Mind

Read the transcript.

0:00 - Intro 9:28 - Studying memory in cells 10:14 - Who the book is for 17:57 - Studying memory in cells 21:53 - What is memory? 29:49 - Book 29:52 - How the book came about 37:56 - Central message of the book 44:07 - Meaning in nature 49:09 - Meaning and essence 51:55 - Multicellularity and ant colonies 57:43 - Eukaryotes and complexification 1:03:38 - Why do we have brains? 1:06:17 - Emergence 1:10:58 - Language 1:12:41 - Human evolution 1:14:41 - Artificial intelligence, meaning and essences 1:25:49 - Consciousness

Ann Kennedy is Associate Professor at Scripps Research Institute and runs the Laboratory for Theoretical Neuroscience and Behavior.

Among other things, Ann has been studying how processes important in life, like survival, threat response, motivation, and pain, are mediated through subcortical brain areas like the hypothalamus. She also pays attention to the time course those life processes require, which has led her to consider how the expression of things like proteins help shape neural processes throughout the brain, so we can behave appropriately in those different contexts.

You'll hear us talk about how this is still a pretty open field in theoretical neuroscience, unlike the historically heavy use of theory in popular brain areas throughout the cortex, and the historically narrow focus on spikes or action potentials as the only game in town when it comes to neural computation. We discuss that and I link in the show notes to a commentary piece Ann wrote, in which she argues for both top-down and bottom-up theoretical approaches.

I also link to her papers about the early evolution of nervous systems, how heterogeneity or diversity of neurons is an advantage for neural computations, and we discuss a kaggle competition she developed to benchmark automated behavioral labels of behaving organisms, so that despite different researchers using different recording systems and setups, analyzing those data will produce consistent labels to better compare across labs and aggregated bigger and better data sets.

• Laboratory for Theoretical Neuroscience and Behavior.
• Social:
  • @antihebbiann.bsky.social
  • @Antihebbiann 
• The Kaggle competition Ann developed to generalize behavior categorization.
• Related papers
  • Dynamics of neural activity in early nervous system evolution.
  • Theoretical neuroscience has room to grow.
  • Neural heterogeneity controls computations in spiking neural networks.
  • A parabrachial hub for the prioritization of survival behavior.
  • An approximate line attractor in the hypothalamus encodes an aggressive state.

Read the transcript.

0:00 - Intro 3:36 - Why study subcortical areas? 13:30 - Evolution 15:06 - Dynamical systems and time scales 21:32 - NeuroAI 28:37 - Before there were brains 33:11 - Endogenous spontaneous activity 40:09 - Natural vs artificial 43:09 - Different is more - heterogeneity 45:32 - Neuromodulators and neuropeptide functions 55:47 - Heterogeneity: manifolds, subspaces, and gain 1:02:43 - Control knobs 1:09:45 - Theoretical neuroscience has room to grow 1:19:59 - Hypothalamus 1:20:57 - Subcortical vs "higher" cognition 1:24:53 - 4E cognition 1:26:56 - Behavior benchmarking 1:37:26 - Current challenges 1:39:46 - Advice to young researchers

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What changes and what stays the same as you scale from single neurons up to local populations of neurons up to whole brains? How tuning parameters like the gain in some neural populations affects the dynamical and computational properties of the rest of the system.

Those are the main questions my guests today discuss. Michael Breakspear is a professor of Systems Neuroscience and runs the Systems Neuroscience Group at the University of Newcastle in Australia. Mac Shine is back, he was here a few years ago. Mac runs the Shine Lab at the University of Sidney in Australia.

Michael and Mac have been collaborating on the questions I mentioned above, using a systems approach to studying brains and cognition. The short summary of what they discovered in their first collaboration is that turning up or down the gain across broad networks of neurons in the brain affects integration - working together - and segregation - working apart. They map this gain modulation on to the ascending arousal pathway, in which the locus coeruleus projects widely throughout the brain distributing noradrenaline. At a certain sweet spot of gain, integration and segregation are balanced near a bifurcation point, near criticality, which maximizes properties that are good for cognition.

In their recent collaboration, they used a coarse graining procedure inspired by physics to study the collective dynamics of various sizes of neural populations, going from single neurons to large populations of neurons. Here they found that despite different coding properties at different scales, there are also scale-free properties that suggest neural populations of all sizes, from single neurons to brains, can do cognitive stuff useful for the organism. And they found this is a conserved property across many different species, suggesting it's a universal principle of brain dynamics in general.

So we discuss all that, but to get there we talk about what a systems approach to neuroscience is, how systems neuroscience has changed over the years, and how it has inspired the questions Michael and Mac ask.

• Breakspear: Systems Neuroscience Group.
  • @DrBreaky.
• Shine: Shine Lab.
  • @jmacshine.
• Related papers
  • Dynamic models of large-scale brain activity
  • Metastable brain waves
  • The modulation of neural gain facilitates a transition between functional segregation and integration in the brain
  • Multiscale Organization of Neuronal Activity Unifies Scale-Dependent Theories of Brain Function.
  • The brain that controls itself.
  • Metastability demystified — the foundational past, the pragmatic present and the promising future.
  • Generation of surrogate brain maps preserving spatial autocorrelation through random rotation of geometric eigenmodes.
• Related episodes
  • BI 212 John Beggs: Why Brains Seek the Edge of Chaos
  • BI 216 Woodrow Shew and Keith Hengen: The Nature of Brain Criticality
  • BI 121 Mac Shine: Systems Neurobiology

Read the transcript.

0:00 - Intro 4:28 - Neuroscience vs neurobiology 8:01 - Systems approach 26:52 - Physics for neuroscience 33:15 - Gain and bifurcation: earliest collaboration 55:32 - Multiscale organization 1:17:54 - Roadblocks

The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.

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Xaq Pitkow runs the Lab for the Algorithmic Brain at Carnegie Mellon University. The main theme of our discussion is how Xaq approaches his research into cognition by way of principles, from which his questions and models and methods spring forth. We discuss those principles, and In that light, we discuss some of his specific lines of work and ideas on the theoretical side of trying understand and explain a slew of cognitive processes. A few of the specifics we discuss are:

• How when we present tasks for organisms to solve, they use strategies that are suboptimal relative to the task, but nearly optimal relative to their beliefs about what they need to do - something Xaq calls inverse rational control.
• Probabilistic graph networks.
• How brains use probabilities to compute.
• A new ecological neuroscience project Xaq has started with multiple collaborators.

• LAB: Lab for the Algorithmic Brain.
• Related papers
  • How does the brain compute with probabilities?
  • Rational thoughts in neural codes.
  • Control when confidence is costly
  • Generalization of graph network inferences in higher-order graphical models.
  • Attention when you need.

Read the transcript.

0:00 - Intro 3:57 - Xaq's approach 8:28 - Inverse rational control 19:19 - Space of input-output functions 24:48 - Cognition for cognition 27:35 - Theory vs. experiment 40:32 - How does the brain compute with probabilities? 1:03:57 - Normative vs kludge 1:07:44 - Ecological neuroscience 1:20:47 - Representations 1:29:34 - Current projects 1:36:04 - Need a synaptome 1:42:20 - Across scales

We are in an exciting time in the cross-fertilization of the neurotech industry and the cognitive sciences. My guest today is Chris Rozell, who sits in that space that connects neurotech and brain research. Chris runs the Structured Information for Precision Neuroengineering Lab at Georgia Tech University, and he was just named the inaugural director of Georgia Tech’s Institute for Neuroscience, Neurotechnology, and Society. I think this is the first time on brain inspired we've discussed stimulating brains to treat mental disorders. I think. Today we talk about Chris's work establishing a biomarker from brain recordings of patients with treatment resistant depression, a specific form of depression. These are patients who have deep brain stimulation electrodes implanted in an effort to treat their depression. Chris and his team used that stimulation in conjunction with brain recordings and machine learning tools to predict how effective the treatment will be under what circumstances, and so on, to help psychiatrists better treat their patients. We'll get into the details and surrounding issues. Toward the end we also talk about Chris's unique background and path and approach, and why he thinks interdisciplinary research is so important. He's one of the most genuinely well intentioned people I've met, and I hope you're inspired by his research and his story.

• Structured Information for Precision Neuroengineering Lab.
• Twitter: @crozSciTech.
• Related papers
  • Cingulate dynamics track depression recovery with deep brain stimulation.
• Story Collider: Wired Lives

0:00 - Intro 3:20 - Overview of the study 17:11 - Closed and open loop stimulation 19:34 - Predicting recovery 28:45 - Control knob for treatment 39:04 - Historical and modern brain stimulation 49:07 - Treatment resistant depression 53:44 - Control nodes complex systems 1:01:06 - Explainable generative AI for a biomarker 1:16:40 - Where are we and what are the obstacles? 1:21:32 - Interface Neuro 1:24:55 - Why Chris cares

Read the transcript.

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Do AI engineers need to emulate some processes and features found only in living organisms at the moment, like how brains are inextricably integrated with bodies? Is consciousness necessary for AI entities if we want them to play nice with us? Is quantum physics part of that story, or a key part, or the key part? Jennifer Prendki believes if we continue to scale AI, it will get us more of the same of what we have today, and that we should look to biology, life, and possibly consciousness to enhance AI. Jennifer is a former particle physicist turned entrepreneur and AI expert, focusing on curating the right kinds and forms of data to train AI, and in that vein she led those efforts at Deepmind on the foundation models ubiquitous in our lives now.

I was curious why someone with that background would come to the conclusion that AI needs inspiration from life, biology, and consciousness to move forward gracefully, and that it would be useful to better understand those processes in ourselves before trying to build what some people call AGI, whatever that is. Her perspective is a rarity among her cohorts, which we also discuss. And get this: she's interested in these topics because she cares about what happens to the planet and to us as a species. Perhaps also a rarity among those charging ahead to dominate profits and win the race

• Jennifer's website: Quantum of Data.
• The blog posts we discuss:
  • The Myth of Emergence
  • Embodiment & Sentience: Why the Body still Matters
  • The Architecture of Synthetic Consciousness
  • On Time and Consciousness
  • Superalignment and the Question of AI Personhood.

Read the transcript.

0:00 - Intro 3:25 - Jennifer's background 13:10 - Consciousness 16:38 - Life and consciousness 23:16 - Superalignment 40:11 - Quantum 1:04:45 - Wetware and biological mimicry 1:15:03 - Neural interfaces 1:16:48 - AI ethics 1:2:35 - AI models are not models 1:27:13 - What scaling will get us 1:39:53 - Current roadblocks 1:43:19 - Philosophy

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Sign up for Brain Inspired email alerts to be notified every time a new Brain Inspired episode is released.

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A few episodes ago, episode 212, I conversed with John Beggs about how criticality might be an important dynamic regime of brain function to optimize our cognition and behavior. Today we continue and extend that exploration with a few other folks in the criticality world.

Woodrow Shew is a professor and runs the Shew Lab at the University of Arkansas. Keith Hengen is an associate professor and runs the Hengen Lab at Washington University in St. Louis Missouri. Together, they are Hengen and Shew on a recent review paper in Neuron, titled Is criticality a unified setpoint of brain function? In the review they argue that criticality is a kind of homeostatic goal of neural activity, describing multiple properties and signatures of criticality, they discuss multiple testable predictions of their thesis, and they address the historical and current controversies surrounding criticality in the brain, surveying what Woody thinks is all the past studies on criticality, which is over 300. And they offer a account of why many of these past studies did not find criticality, but looking through a modern lens they most likely would. We discuss some of the topics in their paper, but we also dance around their current thoughts about things like the nature and implications of being nearer and farther from critical dynamics, the relation between criticality and neural manifolds, and a lot more. You get to experience Woody and Keith thinking in real time about these things, which I hope you appreciate.

• Shew Lab. @ShewLab
• Hengen Lab.
• Is criticality a unified setpoint of brain function?

Read the transcript.

0:00 - Intro 3:41 - Collaborating 6:22 - Criticality community 14:47 - Tasks vs. Naturalistic 20:50 - Nature of criticality 25:47 - Deviating from criticality 33:45 - Sleep for criticality 38:41 - Neuromodulation for criticality 40:45 - Criticality Definition part 1: scale invariance 43:14 - Criticality Definition part 2: At a boundary 51:56 - New method to assess criticality 56:12 - Types of criticality 1:02:23 - Value of criticality versus other metrics 1:15:21 - Manifolds and criticality 1:26:06 - Current challenges

The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.

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Xiao-Jing Wang is a Distinguished Global Professor of Neuroscience at NYU

Xiao-Jing was born and grew up in China, spent 8 years in Belgium studying theoretical physics like nonlinear dynamical systems and deterministic chaos. And as he says it, he arrived from Brussels to California as a postdoc, and in one day switched from French to English, from European to American culture, and physics to neuroscience. I know Xiao-Jing as a legend in non-human primate neurophysiology and modeling, paving the way for the rest of us to study brain activity related cognitive functions like working memory and decision-making.

He has just released his new textbook, Theoretical Neuroscience: Understanding Cognition, which covers the history and current research on modeling cognitive functions from the very simple to the very cognitive. The book is also somewhat philosophical, arguing that we need to update our approach to explaining how brains function, to go beyond Marr's levels and enter a cross-level mechanistic explanatory pursuit, which we discuss. I just learned he even cites my own PhD research, studying metacognition in nonhuman primates - so you know it's a great book. Learn more about Xiao-Jing and the book in the show notes. It was fun having one of my heroes on the podcast, and I hope you enjoy our discussion.

• Computational Laboratory of Cortical Dynamics
• Book: Theoretical Neuroscience: Understanding Cognition.
• Related papers
  • Division of labor among distinct subtypes of inhibitory neurons in a cortical microcircuit of working memory.
  • Macroscopic gradients of synaptic excitation and inhibition across the neocortex.
  • Theory of the multiregional neocortex: large-scale neural dynamics and distributed cognition.

0:00 - Intro 3:08 - Why the book now? 11:00 - Modularity in neuro vs AI 14:01 - Working memory and modularity 22:37 - Canonical cortical microcircuits 25:53 - Gradient of inhibitory neurons 27:47 - Comp neuro then and now 45:35 - Cross-level mechanistic understanding 1:13:38 - Bifurcation 1:24:51 - Bifurcation and degeneracy 1:34:02 - Control theory 1:35:41 - Psychiatric disorders 1:39:14 - Beyond dynamical systems 1:43:447 - Mouse as a model 1:48:11 - AI needs a PFC

The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.

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Check out this story:

What, if anything, makes mood fundamentally different from memory?

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Elusive Cures: Why Neuroscience Hasn’t Solved Brain Disorders―and How We Can Change That. Nicole Rust runs the Visual Memory laboratory at the University of Pennsylvania. Her interests have expanded now to include mood and feelings, as you'll hear. And she wrote this book, which contains a plethora of ideas about how we can pave a way forward in neuroscience to help treat mental and brain disorders. We talk about a small plethora of those ideas from her book. which also contains the story partially which will hear of her own journey in thinking about these things from working early on in visual neuroscience to where she is now.

• Nicole's website.
• Elusive Cures: Why Neuroscience Hasn’t Solved Brain Disorders―and How We Can Change That.

0:00 - Intro 6:12 - Nicole's path 19:25 - The grand plan 25:18 - Robustness and fragility 39:15 - Mood 49:25 - Model everything! 56:26 - Epistemic iteration 1:06:50 - Can we standardize mood? 1:10:36 - Perspective neuroscience 1:20:12 - William Wimsatt 1:25:40 - Consciousness

The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.

Read more about our partnership.

Check out this series of essays about representations:

What are we talking about? Clarifying the fuzzy concept of representation in neuroscience and beyond

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What do neuroscientists mean when they use the term representation? That's part of what Luis Favela and Edouard Machery set out to answer a couple years ago by surveying lots of folks in the cognitive sciences, and they concluded that as a field the term is used in a confused and unclear way. Confused and unclear are technical terms here, and Luis and Edouard explain what they mean in the episode. More recently Luis and Edouard wrote a follow-up piece arguing that maybe it's okay for everyone to use the term in slightly different ways, maybe it helps communication across disciplines, perhaps. My three other guests today, Frances Egan, Rosa Cao, and John Krakauer wrote responses to that argument, and on today's episode all those folks are here to further discuss that issue and why it matters. Luis is a part philosopher, part cognitive scientists at Indiana University Bloomington, Edouard is a philosopher and Director of the Center for Philosophy of Science at the University of Pittsburgh, Frances is a philosopher from Rutgers University, Rosa is a neuroscientist-turned philosopher at Stanford University, and John is a neuroscientist among other things, and co-runs the Brain, Learning, Animation, and Movement Lab at Johns Hopkins.

• Luis Favela.
  • Favela's book: The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment
• Edouard Machery.
  • Machery's book: Doing without Concepts
• Frances Egan.
  • Egan's book: Deflating Mental Representation.
• John Krakauer.
• Rosa Cao.
  • Paper mentioned: Putting representations to use.
• The exchange, in order, discussed on this episode:
  • Investigating the concept of representation in the neural and psychological sciences.
  • The concept of representation in the brain sciences: The current status and ways forward.
  • Commentaries:
    • Assessing the landscape of representational concepts: Commentary on Favela and Machery.
    • Comments on Favela and Machery's The concept of representation in the brain sciences: The current status and ways forward.
    • Where did real representations go? Commentary on: The concept of representation in the brain sciences: The current status and ways forward by Favela and Machery.
    • Reply to commentaries:
      • Contextualizing, eliminating, or glossing: What to do with unclear scientific concepts like representation.

0:00 - Intro 3:55 - What is a representation to a neuroscientist? 14:44 - How to deal with the dilemma 21:20 - Opposing views 31:00 - What's at stake? 51:10 - Neural-only representation 1:01:11 - When "representation" is playing a useful role 1:12:56 - The role of a neuroscientist 1:39:35 - The purpose of "representational talk" 1:53:03 - Non-representational mental phenomenon 1:55:53 - Final thoughts

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You may have heard of the critical brain hypothesis. It goes something like this: brain activity operates near a dynamical regime called criticality, poised at the sweet spot between too much order and too much chaos, and this is a good thing because systems at criticality are optimized for computing, they maximize information transfer, they maximize the time range over which they operate, and a handful of other good properties. John Beggs has been studying criticality in brains for over 20 years now. His 2003 paper with Deitmar Plenz is one of of the first if not the first to show networks of neurons operating near criticality, and it gets cited in almost every criticality paper I read. John runs the Beggs Lab at Indiana University Bloomington, and a few years ago he literally wrote the book on criticality, called The Cortex and the Critical Point: Understanding the Power of Emergence, which I highly recommend as an excellent introduction to the topic, and he continues to work on criticality these days.

On this episode we discuss what criticality is, why and how brains might strive for it, the past and present of how to measure it and why there isn't a consensus on how to measure it, what it means that criticality appears in so many natural systems outside of brains yet we want to say it's a special property of brains. These days John spends plenty of effort defending the criticality hypothesis from critics, so we discuss that, and much more.

• Beggs Lab.
• Book:
  • The Cortex and the Critical Point: Understanding the Power of Emergence
• Related papers
  • Addressing skepticism of the critical brain hypothesis
• Papers John mentioned:
  • Tetzlaff et al 2010: Self-organized criticality in developing neuronal networks.
  • Haldeman and Beggs 2005: Critical Branching Captures Activity in Living Neural Networks and Maximizes the Number of Metastable States.
  • Bertschinger et al 2004: At the edge of chaos: Real-time computations and self-organized criticality in recurrent neural networks.
  • Legenstein and Maass 2007: Edge of chaos and prediction of computational performance for neural circuit models.
  • Kinouchi and Copelli 2006: Optimal dynamical range of excitable networks at criticality.
  • Chialvo 2010: Emergent complex neural dynamics..
  • Mora and Bialek 2011: Are Biological Systems Poised at Criticality?

Read the transcript.

0:00 - Intro 4:28 - What is criticality? 10:19 - Why is criticality special in brains? 15:34 - Measuring criticality 24:28 - Dynamic range and criticality 28:28 - Criticisms of criticality 31:43 - Current state of critical brain hypothesis 33:34 - Causality and criticality 36:39 - Criticality as a homeostatic set point 38:49 - Is criticality necessary for life? 50:15 - Shooting for criticality far from thermodynamic equilibrium 52:45 - Quasi- and near-criticality 55:03 - Cortex vs. whole brain 58:50 - Structural criticality through development 1:01:09 - Criticality in AI 1:03:56 - Most pressing criticisms of criticality 1:10:08 - Gradients of criticality 1:22:30 - Homeostasis vs. criticality 1:29:57 - Minds and criticality

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To explore more neuroscience news and perspectives, visit thetransmitter.org.

Rony Hirschhorn, Alex Lepauvre, and Oscar Ferrante are three of many many scientists that comprise the COGITATE group. COGITATE is an adversarial collaboration project to test theories of consciousness in humans, in this case testing the integrated information theory of consciousness and the global neuronal workspace theory of consciousness. I said it's an adversarial collaboration, so what does that mean. It's adversarial in that two theories of consciousness are being pitted against each other. It's a collaboration in that the proponents of the two theories had to agree on what experiments could be performed that could possibly falsify the claims of either theory. The group has just published the results of the first round of experiments in a paper titled Adversarial testing of global neuronal workspace and integrated information theories of consciousness, and this is what Rony, Alex, and Oscar discuss with me today.

The short summary is that they used a simple task and measured brain activity with three different methods: EEG, MEG, and fMRI, and made predictions about where in the brain correlates of consciousness should be, how that activity should be maintained over time, and what kind of functional connectivity patterns should be present between brain regions. The take home is a mixed bag, with neither theory being fully falsified, but with a ton of data and results for the world to ponder and build on, to hopefully continue to refine and develop theoretical accounts of how brains and consciousness are related.

So we discuss the project itself, many of the challenges they faced, their experiences and reflections working on it and on coming together as a team, the nature of working on an adversarial collaboration, when so much is at stake for the proponents of each theory, and, as you heard last episode with Dean Buonomano, when one of the theories, IIT, is surrounded by a bit of controversy itself regarding whether it should even be considered a scientific theory.

• COGITATE.
• Oscar Ferrante. @ferrante_oscar
• Rony Hirschhorn. @RonyHirsch
• Alex Lepauvre. @LepauvreAlex
• Paper: Adversarial testing of global neuronal workspace and integrated information theories of consciousness.
• BI 210 Dean Buonomano: Consciousness, Time, and Organotypic Dynamics

Read the transcript.

0:00 - Intro 4:00 - COGITATE 17:42 - How the experiments were developed 32:37 - How data was collected and analyzed 41:24 - Prediction 1: Where is consciousness? 47:51 - The experimental task 1:00:14 - Prediction 2: Duration of consciousness-related activity 1:18:37 - Prediction 3: Inter-areal communication 1:28:28 - Big picture of the results 1:44:25 - Moving forward

Dean Buonomano runs the Buonomano lab at UCLA. Dean was a guest on Brain Inspired way back on episode 18, where we talked about his book Your Brain is a Time Machine: The Neuroscience and Physics of Time, which details much of his thought and research about how centrally important time is for virtually everything we do, different conceptions of time in philosophy, and how how brains might tell time. That was almost 7 years ago, and his work on time and dynamics in computational neuroscience continues.

One thing we discuss today, later in the episode, is his recent work using organotypic brain slices to test the idea that cortical circuits implement timing as a computational primitive it's something they do by they're very nature. Organotypic brain slices are between what I think of as traditional brain slices and full on organoids. Brain slices are extracted from an organism, and maintained in a brain-like fluid while you perform experiments on them. Organoids start with a small amount of cells that you the culture, and let them divide and grow and specialize, until you have a mass of cells that have grown into an organ of some sort, to then perform experiments on. Organotypic brain slices are extracted from an organism, like brain slices, but then also cultured for some time to let them settle back into some sort of near-homeostatic point - to them as close as you can to what they're like in the intact brain... then perform experiments on them. Dean and his colleagues use optigenetics to train their brain slices to predict the timing of the stimuli, and they find the populations of neurons do indeed learn to predict the timing of the stimuli, and that they exhibit replaying of those sequences similar to the replay seen in brain areas like the hippocampus.

But, we begin our conversation talking about Dean's recent piece in The Transmitter, that I'll point to in the show notes, called The brain holds no exclusive rights on how to create intelligence. There he argues that modern AI is likely to continue its recent successes despite the ongoing divergence between AI and neuroscience. This is in contrast to what folks in NeuroAI believe.

We then talk about his recent chapter with physicist Carlo Rovelli, titled Bridging the neuroscience and physics of time, in which Dean and Carlo examine where neuroscience and physics disagree and where they agree about the nature of time.

Finally, we discuss Dean's thoughts on the integrated information theory of consciousness, or IIT. IIT has see a little controversy lately. Over 100 scientists, a large part of that group calling themselves IIT-Concerned, have expressed concern that IIT is actually unscientific. This has cause backlash and anti-backlash, and all sorts of fun expression from many interested people. Dean explains his own views about why he thinks IIT is not in the purview of science - namely that it doesn't play well with the existing ontology of what physics says about science. What I just said doesn't do justice to his arguments, which he articulates much better.

• Buonomano lab.
• Twitter: @DeanBuono.
• Related papers
  • The brain holds no exclusive rights on how to create intelligence.
  • What makes a theory of consciousness unscientific?
  • Ex vivo cortical circuits learn to predict and spontaneously replay temporal patterns.
  • Bridging the neuroscience and physics of time.
• BI 204 David Robbe: Your Brain Doesn’t Measure Time

Read the transcript.

0:00 - Intro 8:49 - AI doesn't need biology 17:52 - Time in physics and in neuroscience 34:04 - Integrated information theory 1:01:34 - Global neuronal workspace theory 1:07:46 - Organotypic slices and predictive processing 1:26:07 - Do brains actually measure time? David Robbe

The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.

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Sign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released.

To explore more neuroscience news and perspectives, visit thetransmitter.org.

Aran Nayebi is an Assistant Professor at Carnegie Mellon University in the Machine Learning Department. He was there in the early days of using convolutional neural networks to explain how our brains perform object recognition, and since then he's a had a whirlwind trajectory through different AI architectures and algorithms and how they relate to biological architectures and algorithms, so we touch on some of what he has studied in that regard. But he also recently started his own lab, at CMU, and he has plans to integrate much of what he has learned to eventually develop autonomous agents that perform the tasks we want them to perform in similar at least ways that our brains perform them. So we discuss his ongoing plans to reverse-engineer our intelligence to build useful cognitive architectures of that sort.

We also discuss Aran's suggestion that, at least in the NeuroAI world, the Turing test needs to be updated to include some measure of similarity of the internal representations used to achieve the various tasks the models perform. By internal representations, as we discuss, he means the population-level activity in the neural networks, not the mental representations philosophy of mind often refers to, or other philosophical notions of the term representation.

• Aran's Website.
• Twitter: @ayan_nayebi.
• Related papers
  • Brain-model evaluations need the NeuroAI Turing Test.
  • Barriers and pathways to human-AI alignment: a game-theoretic approach.

0:00 - Intro 5:24 - Background 20:46 - Building embodied agents 33:00 - Adaptability 49:25 - Marr's levels 54:12 - Sensorimotor loop and intrinsic goals 1:00:05 - NeuroAI Turing Test 1:18:18 - Representations 1:28:18 - How to know what to measure 1:32:56 - AI safety

Gabriele Scheler co-founded the Carl Correns Foundation for Mathematical Biology. Carl Correns was her great grandfather, one of the early pioneers in genetics. Gabriele is a computational neuroscientist, whose goal is to build models of cellular computation, and much of her focus is on neurons.

We discuss her theoretical work building a new kind of single neuron model. She, like Dmitri Chklovskii a few episodes ago, believes we've been stuck with essentially the same family of models for a neuron for a long time, despite minor variations on those models. The model Gabriele is working on, for example, respects the computations going on not only externally, via spiking, which has been the only game in town forever, but also the computations going on within the cell itself. Gabriele is in line with previous guests like Randy Gallistel, David Glanzman, and Hessam Akhlaghpour, who argue that we need to pay attention to how neurons are computing various things internally and how that affects our cognition. Gabriele also believes the new neuron model she's developing will improve AI, drastically simplifying the models by providing them with smarter neurons, essentially.

We also discuss the importance of neuromodulation, her interest in wanting to understand how we think via our internal verbal monologue, her lifelong interest in language in general, what she thinks about LLMs, why she decided to start her own foundation to fund her science, what that experience has been like so far. Gabriele has been working on these topics for many years, and as you'll hear in a moment, she was there when computational neuroscience was just starting to pop up in a few places, when it was a nascent field, unlike its current ubiquity in neuroscience.

• Gabriele's website.
• Carl Correns Foundation for Mathematical Biology.
  • Neuro-AI spinoff
• Related papers
  • Sketch of a novel approach to a neural model.
  • Localist neural plasticity identified by mutual information.
• Related episodes
  • BI 199 Hessam Akhlaghpour: Natural Universal Computation
  • BI 172 David Glanzman: Memory All The Way Down
  • BI 126 Randy Gallistel: Where Is the Engram?

0:00 - Intro 4:41 - Gabriele's early interests in verbal thinking 14:14 - What is thinking? 24:04 - Starting one's own foundation 58:18 - Building a new single neuron model 1:19:25 - The right level of abstraction 1:25:00 - How a new neuron would change AI

The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.

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Sign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released.

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The concept of a schema goes back at least to the philosopher Immanuel Kant in the 1700s, who use the term to refer to a kind of built-in mental framework to organize sensory experience. But it was the psychologist Frederic Bartlett in the 1930s who used the term schema in a psychological sense, to explain how our memories are organized and how new information gets integrated into our memory. Fast forward another 100 years to today, and we have a podcast episode with my guest today, Alison Preston, who runs the Preston Lab at the University of Texas at Austin. On this episode, we discuss her neuroscience research explaining how our brains might carry out the processing that fits with our modern conception of schemas, and how our brains do that in different ways as we develop from childhood to adulthood.

I just said, "our modern conception of schemas," but like everything else, there isn't complete consensus among scientists exactly how to define schema. Ali has her own definition. She shares that, and how it differs from other conceptions commonly used. I like Ali's version and think it should be adopted, in part because it helps distinguish schemas from a related term, cognitive maps, which we've discussed aplenty on brain inspired, and can sometimes be used interchangeably with schemas. So we discuss how to think about schemas versus cognitive maps, versus concepts, versus semantic information, and so on.

Last episode Ciara Greene discussed schemas and how they underlie our memories, and learning, and predictions, and how they can lead to inaccurate memories and predictions. Today Ali explains how circuits in the brain might adaptively underlie this process as we develop, and how to go about measuring it in the first place.

• Preston Lab
• Twitter: @preston_lab
• Related papers:
  • Concept formation as a computational cognitive process.
  • Schema, Inference, and Memory.
  • Developmental differences in memory reactivation relate to encoding and inference in the human brain.

Read the transcript.

0:00 - Intro 6:51 - Schemas 20:37 - Schemas and the developing brain 35:03 - Information theory, dimensionality, and detail 41:17 - Geometry of schemas 47:26 - Schemas and creativity 50:29 - Brain connection pruning with development 1:02:46 - Information in brains 1:09:20 - Schemas and development in AI

Complexity Group Email List.

Ciara Greene is Associate Professor in the University College Dublin School of Psychology. In this episode we discuss Ciara's book Memory Lane: The Perfectly Imperfect Ways We Remember, co-authored by her colleague Gillian Murphy. The book is all about how human episodic memory works and why it works the way it does. Contrary to our common assumption, a "good memory" isn't necessarily highly accurate - we don't store memories like files in a filing cabinet. Instead our memories evolved to help us function in the world. That means our memories are flexible, constantly changing, and that forgetting can be beneficial, for example.

Regarding how our memories work, we discuss how memories are reconstructed each time we access them, and the role of schemas in organizing our episodic memories within the context of our previous experiences. Because our memories evolved for function and not accuracy, there's a wide range of flexibility in how we process and store memories. We're all susceptible to misinformation, all our memories are affected by our emotional states, and so on. Ciara's research explores many of the ways our memories are shaped by these various conditions, and how we should better understand our own and other's memories.

• Attention and Memory Lab
• Twitter: @ciaragreene01.
• Book: Memory Lane: The Perfectly Imperfect Ways We Remember

Read the transcript.

0:00 - Intro 5:35 - The function of memory 6:41 - Reconstructive nature of memory 13:50 - Memory schemas, highly superior autobiographical memory 20:49 - Misremembering and flashbulb memories 27:52 - Forgetting and schemas 36:06 - What is a "good" memory? 39:35 - Memories and intention 43:47 - Memory and context 49:55 - Implanting false memories 1:04:10 - Memory suggestion during interrogations 1:06:30 - Memory, imagination, and creativity 1:13:45 - Artificial intelligence and memory 1:21:21 - Driven by questions

The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.

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Since the 1940s and 50s, back at the origins of what we now think of as artificial intelligence, there have been lots of ways of conceiving what it is that brains do, or what the function of the brain is. One of those conceptions, going to back to cybernetics, is that the brain is a controller that operates under the principles of feedback control. This view has been carried down in various forms to us in present day. Also since that same time period, when McCulloch and Pitts suggested that single neurons are logical devices, there have been lots of ways of conceiving what it is that single neurons do. Are they logical operators, do they each represent something special, are they trying to maximize efficiency, for example?

Dmitri Chklovskii, who goes by Mitya, runs the Neural Circuits and Algorithms lab at the Flatiron Institute. Mitya believes that single neurons themselves are each individual controllers. They're smart agents, each trying to predict their inputs, like in predictive processing, but also functioning as an optimal feedback controller. We talk about historical conceptions of the function of single neurons and how this differs, we talk about how to think of single neurons versus populations of neurons, some of the neuroscience findings that seem to support Mitya's account, the control algorithm that simplifies the neuron's otherwise impossible control task, and other various topics.

We also discuss Mitya's early interests, coming from a physics and engineering background, in how to wire up our brains efficiently, given the limited amount of space in our craniums. Obviously evolution produced its own solutions for this problem. This pursuit led Mitya to study the C. elegans worm, because its connectome was nearly complete- actually, Mitya and his team helped complete the connectome so he'd have the whole wiring diagram to study it. So we talk about that work, and what knowing the whole connectome of C. elegans has and has not taught us about how brains work.

• Chklovskii Lab.
• Twitter: @chklovskii.
• Related papers
  • The Neuron as a Direct Data-Driven Controller.
  • Normative and mechanistic model of an adaptive circuit for efficient encoding and feature extraction.
• Related episodes
  • BI 143 Rodolphe Sepulchre: Mixed Feedback Control
  • BI 119 Henry Yin: The Crisis in Neuroscience

Read the transcript.

0:00 - Intro 7:34 - Physicists approach for neuroscience 12:39 - What's missing in AI and neuroscience? 16:36 - Connectomes 31:51 - Understanding complex systems 33:17 - Earliest models of neurons 39:08 - Smart neurons 42:56 - Neuron theories that influenced Mitya 46:50 - Neuron as a controller 55:03 - How to test the neuron as controller hypothesis 1:00:29 - Direct data-driven control 1:11:09 - Experimental evidence 1:22:25 - Single neuron doctrine and population doctrine 1:25:30 - Neurons as agents 1:28:52 - Implications for AI 1:30:02 - Limits to control perspective

The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.

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Sign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released:

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When you play hide and seek, as you do on a regular basis I'm sure, and you count to ten before shouting, "Ready or not, here I come," how do you keep track of time? Is it a clock in your brain, as many neuroscientists assume and therefore search for in their research? Or is it something else? Maybe the rhythm of your vocalization as you say, "one-one thousand, two-one thousand"? Even if you’re counting silently, could it be that you’re imagining the movements of speaking aloud and tracking those virtual actions? My guest today, neuroscientist David Robbe, believes we don't rely on clocks in our brains, or measure time internally, or really that we measure time at all. Rather, our estimation of time emerges through our interactions with the world around us and/or the world within us as we behave.

David is group leader of the Cortical-Basal Ganglia Circuits and Behavior Lab at the Institute of Mediterranean Neurobiology. His perspective on how organisms measure time is the result of his own behavioral experiments with rodents, and by revisiting one of his favorite philosophers, Henri Bergson. So in this episode, we discuss how all of this came about - how neuroscientists have long searched for brain activity that measures or keeps track of time in areas like the basal ganglia, which is the brain region David focuses on, how the rodents he studies behave in surprising ways when he asks them to estimate time intervals, and how Bergson introduce the world to the notion of durée, our lived experience and feeling of time.

• Cortical-Basal Ganglia Circuits and Behavior Lab.
• Twitter: @dav_robbe
• Related papers
  • Lost in time: Relocating the perception of duration outside the brain.
  • Running, Fast and Slow: The Dorsal Striatum Sets the Cost ofMovement During Foraging.

0:00 - Intro 3:59 - Why behavior is so important in itself 10:27 - Henri Bergson 21:17 - Bergson's view of life 26:25 - A task to test how animals time things 34:08 - Back to Bergson and duree 39:44 - Externalizing time 44:11 - Internal representation of time 1:03:38 - Cognition as internal movement 1:09:14 - Free will 1:15:27 - Implications for AI

The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.

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David Krakauer is the president of the Santa Fe Institute, where their mission is officially "Searching for Order in the Complexity of Evolving Worlds." When I think of the Santa Fe institute, I think of complexity science, because that is the common thread across the many subjects people study at SFI, like societies, economies, brains, machines, and evolution. David has been on before, and I invited him back to discuss some of the topics in his new book The Complex World: An Introduction to the Fundamentals of Complexity Science.

The book on the one hand serves as an introduction and a guide to a 4 volume collection of foundational papers in complexity science, which you'll David discuss in a moment. On the other hand, The Complex World became much more, discussing and connecting ideas across the history of complexity science. Where did complexity science come from? How does it fit among other scientific paradigms? How did the breakthroughs come about? Along the way, we discuss the four pillars of complexity science - entropy, evolution, dynamics, and computation, and how complexity scientists draw from these four areas to study what David calls "problem-solving matter." We discuss emergence, the role of time scales, and plenty more all with my own self-serving goal to learn and practice how to think like a complexity scientist to improve my own work on how brains do things. Hopefully our conversation, and David's book, help you do the same.

• David's website.
• David's SFI homepage.
• The book: The Complex World: An Introduction to the Fundamentals of Complexity Science.
• The 4-Volume Series: Foundational Papers in Complexity Science.
• Mentioned:
  • Aeon article: Problem-solving matter.
  • The information theory of individuality.

Read the transcript.

0:00 - Intro 3:45 - Origins of The Complex World 20:10 - 4 pillars of complexity 36:27 - 40s to 70s in complexity 42:33 - How to proceed as a complexity scientist 54:32 - Broken symmetries 1:02:40 - Emergence 1:13:25 - Time scales and complexity 1:18:48 - Consensus and how ideas migrate 1:29:25 - Disciplinary matrix (Kuhn) 1:32:45 - Intelligence vs. life

The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.

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Sign up for the “Brain Inspired” email alerts to be notified every time a new Brain Inspired episode is released.

Eli Sennesh is a postdoc at Vanderbilt University, one of my old stomping grounds, currently in the lab of Andre Bastos. Andre’s lab focuses on understanding brain dynamics within cortical circuits, particularly how communication between brain areas is coordinated in perception, cognition, and behavior. So Eli is busy doing work along those lines, as you'll hear more about. But the original impetus for having him on his recently published proposal for how predictive coding might be implemented in brains. So in that sense, this episode builds on the last episode with Rajesh Rao, where we discussed Raj's "active predictive coding" account of predictive coding.  As a super brief refresher, predictive coding is the proposal that the brain is constantly predicting what's about the happen, then stuff happens, and the brain uses the mismatch between its predictions and the actual stuff that's happening, to learn how to make better predictions moving forward. I refer you to the previous episode for more details. So Eli's account, along with his co-authors of course, which he calls "divide-and-conquer" predictive coding, uses a probabilistic approach in an attempt to account for how brains might implement predictive coding, and you'll learn more about that in our discussion. But we also talk quite a bit about the difference between practicing theoretical and experimental neuroscience, and Eli's experience moving into the experimental side from the theoretical side.

• Eli's website.
• Bastos lab.
• Twitter: @EliSennesh
• Related papers
  • Divide-and-Conquer Predictive Coding: a Structured Bayesian Inference Algorithm.
• Related episode:
  • BI 201 Rajesh Rao: Active Predictive Coding.

Read the transcript.

0:00 - Intro 3:59 - Eli's worldview 17:56 - NeuroAI is hard 24:38 - Prediction errors vs surprise 55:16 - Divide and conquer 1:13:24 - Challenges 1:18:44 - How to build AI 1:25:56 - Affect 1:31:55 - Abolish the value function

Today I'm in conversation with Rajesh Rao, a distinguished professor of computer science and engineering at the University of Washington, where he also co-directs the Center for Neurotechnology. Back in 1999, Raj and Dana Ballard published what became quite a famous paper, which proposed how predictive coding might be implemented in brains. What is predictive coding, you may be wondering? It's roughly the idea that your brain is constantly predicting incoming sensory signals, and it generates that prediction as a top-down signal that meets the bottom-up sensory signals. Then the brain computes a difference between the prediction and the actual sensory input, and that difference is sent back up to the "top" where the brain then updates its internal model to make better future predictions.

So that was 25 years ago, and it was focused on how the brain handles sensory information. But Raj just recently published an update to the predictive coding framework, one that incorporates actions and perception, suggests how it might be implemented in the cortex - specifically which cortical layers do what - something he calls "Active predictive coding." So we discuss that new proposal, we also talk about his engineering work on brain-computer interface technologies, like BrainNet, which basically connects two brains together, and like neural co-processors, which use an artificial neural network as a prosthetic that can do things like enhance memories, optimize learning, and help restore brain function after strokes, for example. Finally, we discuss Raj's interest and work on deciphering an ancient Indian text, the mysterious Indus script.

• Raj's website.
• Twitter: @RajeshPNRao.
• Related papers
  • A sensory–motor theory of the neocortex.
  • Brain co-processors: using AI to restore and augment brain function.
  • Towards neural co-processors for the brain: combining decoding and encoding in brain–computer interfaces.
  • BrainNet: A Multi-Person Brain-to-Brain Interface for Direct Collaboration Between Brains.

Read the transcript.

0:00 - Intro 7:40 - Predictive coding origins 16:14 - Early appreciation of recurrence 17:08 - Prediction as a general theory of the brain 18:38 - Rao and Ballard 1999 26:32 - Prediction as a general theory of the brain 33:24 - Perception vs action 33:28 - Active predictive coding 45:04 - Evolving to augment our brains 53:03 - BrainNet 57:12 - Neural co-processors 1:11:19 - Decoding the Indus Script 1:20:18 - Transformer models relation to active predictive coding

Joe Monaco and Grace Hwang  co-organized a recent workshop I participated in, the 2024 BRAIN NeuroAI Workshop. You may have heard of the BRAIN Initiative, but in case not, BRAIN is is huge funding effort across many agencies, one of which is the National Institutes of Health, where this recent workshop was held. The BRAIN Initiative began in 2013 under the Obama administration, with the goal to support developing technologies to help understand the human brain, so we can cure brain based diseases.

BRAIN Initiative just became a decade old, with many successes like recent whole brain connectomes, and discovering the vast array of cell types. Now the question is how to move forward, and one area they are curious about, that perhaps has a lot of potential to support their mission, is the recent convergence of neuroscience and AI... or NeuroAI. The workshop was designed to explore how NeuroAI might contribute moving forward, and to hear from NeuroAI folks how they envision the field moving forward. You'll hear more about that in a moment.

That's one reason I invited Grace and Joe on. Another reason is because they co-wrote a position paper a while back that is impressive as a synthesis of lots of cognitive sciences concepts, but also proposes a specific level of abstraction and scale in brain processes that may serve as a base layer for computation. The paper is called Neurodynamical Computing at the Information Boundaries, of Intelligent Systems, and you'll learn more about that in this episode.

• Joe's NIH page.
• Grace's NIH page.
• Twitter: 
  • Joe: @j_d_monaco
• Related papers
  • Neurodynamical Computing at the Information Boundaries of Intelligent Systems.
  • Cognitive swarming in complex environments with attractor dynamics and oscillatory computing.
  • Spatial synchronization codes from coupled rate-phase neurons.
  • Oscillators that sync and swarm.
• Mentioned
  • A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications.
  • Recalling Lashley and reconsolidating Hebb.
  • BRAIN NeuroAI Workshop (Nov 12–13)
    • NIH BRAIN NeuroAI Workshop Program Book
    • NIH VideoCast – Day 1 Recording – BRAIN NeuroAI Workshop
    • NIH VideoCast – Day 2 Recording – BRAIN NeuroAI Workshop
  • Neuromorphic Principles in Biomedicine and Healthcare Workshop (Oct 21–22)
    • NPBH 2024
  • BRAIN Investigators Meeting 2020 Symposium & Perspective Paper
    • BRAIN 2020 Symposium on Dynamical Systems Neuroscience and Machine Learning (YouTube)
    • Neurodynamical Computing at the Information Boundaries of Intelligent Systems | Cognitive Computation
  • NSF/CIRC
    • Community Infrastructure for Research in Computer and Information Science and Engineering (CIRC) | NSF - National Science Foundation
    • THOR Neuromorphic Commons - Matrix: The UTSA AI Consortium for Human Well-Being

Read the transcript.

0:00 - Intro 25:45 - NeuroAI Workshop - neuromorphics 33:31 - Neuromorphics and theory 49:19 - Reflections on the workshop 54:22 - Neurodynamical computing and information boundaries 1:01:04 - Perceptual control theory 1:08:56 - Digital twins and neural foundation models 1:14:02 - Base layer of computation

The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.

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To explore more neuroscience news and perspectives, visit thetransmitter.org.

Hessam Akhlaghpour is a postdoctoral researcher at Rockefeller University in the Maimon lab. His experimental work is in fly neuroscience mostly studying spatial memories in fruit flies. However, we are going to be talking about a different (although somewhat related) side of his postdoctoral research. This aspect of his work involves theoretical explorations of molecular computation, which are deeply inspired by Randy Gallistel and Adam King's book Memory and the Computational Brain. Randy has been on the podcast before to discuss his ideas that memory needs to be stored in something more stable than the synapses between neurons, and how that something could be genetic material like RNA. When Hessam read this book, he was re-inspired to think of the brain the way he used to think of it before experimental neuroscience challenged his views. It re-inspired him to think of the brain as a computational system. But it also led to what we discuss today, the idea that RNA has the capacity for universal computation, and Hessam's development of how that might happen. So we discuss that background and story, why universal computation has been discovered in organisms yet since surely evolution has stumbled upon it, and how RNA might and combinatory logic could implement universal computation in nature.

• Hessam's website.
• Maimon Lab.
• Twitter: @theHessam.
• Related papers
  • An RNA-based theory of natural universal computation.
  • The molecular memory code and synaptic plasticity: a synthesis.
  • Lifelong persistence of nuclear RNAs in the mouse brain.
  • Cris Moore's conjecture #5 in this 1998 paper.
  • (The Gallistel book): Memory and the Computational Brain: Why Cognitive Science Will Transform Neuroscience.
• Related episodes
  • BI 126 Randy Gallistel: Where Is the Engram?
  • BI 172 David Glanzman: Memory All The Way Down

Read the transcript.

0:00 - Intro 4:44 - Hessam's background 11:50 - Randy Gallistel's book 14:43 - Information in the brain 17:51 - Hessam's turn to universal computation 35:30 - AI and universal computation 40:09 - Universal computation to solve intelligence 44:22 - Connecting sub and super molecular 50:10 - Junk DNA 56:42 - Genetic material for coding 1:06:37 - RNA and combinatory logic 1:35:14 - Outlook 1:42:11 - Reflecting on the molecular world

The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.

Read more about our partnership.

Sign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: https://www.thetransmitter.org/newsletters/

To explore more neuroscience news and perspectives, visit thetransmitter.org.

Tony Zador runs the Zador lab at Cold Spring Harbor Laboratory. You've heard him on Brain Inspired a few times in the past, most recently in a panel discussion I moderated at this past COSYNE conference - a conference Tony co-founded 20 years ago. As you'll hear, Tony's current and past interests and research endeavors are of a wide variety, but today we focus mostly on his thoughts on NeuroAI.

We're in a huge AI hype cycle right now, for good reason, and there's a lot of talk in the neuroscience world about whether neuroscience has anything of value to provide AI engineers - and how much value, if any, neuroscience has provided in the past.

Tony is team neuroscience. You'll hear him discuss why in this episode, especially when it comes to ways in which development and evolution might inspire better data efficiency, looking to animals in general to understand how they coordinate numerous objective functions to achieve their intelligent behaviors - something Tony calls alignment - and using spikes in AI models to increase energy efficiency.

• Zador Lab
• Twitter: @TonyZador
• Previous episodes:
  • BI 187: COSYNE 2024 Neuro-AI Panel.
  • BI 125 Doris Tsao, Tony Zador, Blake Richards: NAISys
  • BI 034 Tony Zador: How DNA and Evolution Can Inform AI
• Related papers
  • Catalyzing next-generation Artificial Intelligence through NeuroAI.
  • Encoding innate ability through a genomic bottleneck.
• Essays
  • NeuroAI: A field born from the symbiosis between neuroscience, AI.
  • What the brain can teach artificial neural networks.

Read the transcript.

0:00 - Intro 3:28 - "Neuro-AI" 12:48 - Visual cognition history 18:24 - Information theory in neuroscience 20:47 - Necessary steps for progress 24:34 - Neuro-AI models and cognition 35:47 - Animals for inspiring AI 41:48 - What we want AI to do 46:01 - Development and AI 59:03 - Robots 1:25:10 - Catalyzing the next generation of AI

The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.

Read more about our partnership.

Sign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released.

To explore more neuroscience news and perspectives, visit thetransmitter.org.

Karen Adolph runs the Infant Action Lab at NYU, where she studies how our motor behaviors develop from infancy onward. We discuss how observing babies at different stages of development illuminates how movement and cognition develop in humans, how variability and embodiment are key to that development, and the importance of studying behavior in real-world settings as opposed to restricted laboratory settings. We also explore how these principles and simulations can inspire advances in intelligent robots. Karen has a long-standing interest in ecological psychology, and she shares some stories of her time studying under Eleanor Gibson and other mentors.

Finally, we get a surprise visit from her partner Mark Blumberg, with whom she co-authored an opinion piece arguing that "motor cortex" doesn't start off with a motor function, oddly enough, but instead processes sensory information during the first period of animals' lives.

• Infant Action Lab (Karen Adolph's lab)
• Sleep and Behavioral Development Lab (Mark Blumberg's lab)
• Related papers
  • Motor Development: Embodied, Embedded, Enculturated, and Enabling
  • An Ecological Approach to Learning in (Not and) Development
  • An update of the development of motor behavior
  • Protracted development of motor cortex constrains rich interpretations of infant cognition

Read the transcript.

The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists. 

This is the second conversation I had while teamed up with Gaute Einevoll at a workshop on NeuroAI in Norway. In this episode, Gaute and I are joined by Cristina Savin and Tim Vogels. Cristina shares how her lab uses recurrent neural networks to study learning, while Tim talks about his long-standing research on synaptic plasticity and how AI tools are now helping to explore the vast space of possible plasticity rules.

We touch on how deep learning has changed the landscape, enhancing our research but also creating challenges with the "fashion-driven" nature of science today. We also reflect on how these new tools have changed the way we think about brain function without fundamentally altering the structure of our questions.

Be sure to check out Gaute's Theoretical Neuroscience podcast as well!

• Mikkel Lepperød
• Cristina Savin
• Tim Vogels
  • Twitter: @TPVogels
• Gaute Einevoll
  • Twitter: @GauteEinevoll
  • Gaute's Theoretical Neuroscience podcast.
• Validating models: How would success in NeuroAI look like?

Read the transcript, provided by The Transmitter.

The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.

This is the first of two less usual episodes. I was recently in Norway at a NeuroAI workshop called Validating models: How would success in NeuroAI look like? What follows are a few recordings I made with my friend Gaute Einevoll. Gaute has been on this podcast before, but more importantly he started his own podcast a while back called Theoretical Neuroscience, which you should check out.

Gaute and I introduce the episode, then briefly speak with Mikkel Lepperød, one of the organizers of the workshop. In this first episode, we're then joined by Ken Harris and Andreas Tolias to discuss how AI has influenced their research, thoughts about brains and minds, and progress and productivity.

• Validating models: How would success in NeuroAI look like?
• Mikkel Lepperød
• Andreas Tolias
  • Twitter: @AToliasLab
• Ken Harris
  • Twitter: @kennethd_harris
• Gaute Einevoll
  • Twitter: @GauteEinevoll
  • Gaute's Theoretical Neuroscience podcast.

Read the transcript, provided by The Transmitter.

The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists. 

The Transmitter has provided a transcript for this episode.

Vijay Namoodiri runs the Nam Lab at the University of California San Francisco, and Ali Mojebi is an assistant professor at the University of Wisconsin-Madison. Ali as been on the podcast before a few times, and he's interested in how neuromodulators like dopamine affect our cognition. And it was Ali who pointed me to Vijay, because of some recent work Vijay has done reassessing how dopamine might function differently than what has become the classic story of dopamine's function as it pertains to learning. The classic story is that dopamine is related to reward prediction errors. That is, dopamine is modulated when you expect reward and don't get it, and/or when you don't expect reward but do get it. Vijay calls this a "prospective" account of dopamine function, since it requires an animal to look into the future to expect a reward. Vijay has shown, however, that a retrospective account of dopamine might better explain lots of know behavioral data. This retrospective account links dopamine to how we understand causes and effects in our ongoing behavior. So in this episode, Vijay gives us a history lesson about dopamine, his newer story and why it has caused a bit of controversy, and how all of this came to be.

I happened to be looking at the Transmitter the other day, after I recorded this episode, and low and behold, there was an article titles Reconstructing dopamine’s link to reward. Vijay is featured in the article among a handful of other thoughtful researchers who share their work and ideas about this very topic. Vijay wrote his own piece as well: Dopamine and the need for alternative theories. So check out those articles for more views on how the field is reconsidering how dopamine works.

• Nam Lab.
• Mohebi & Associates (Ali's Lab).
• Twitter:
  • @vijay_mkn
  • @mohebial
• Transmitter
  • Dopamine and the need for alternative theories.
  • Reconstructing dopamine’s link to reward.
• Related papers
  • Mesolimbic dopamine release conveys causal associations.
  • Mesostriatal dopamine is sensitive to changes in specific cue-reward contingencies.
  • What is the state space of the world for real animals?
  • The learning of prospective and retrospective cognitive maps within neural circuits
• Further reading
  • (Ali's paper): Dopamine transients follow a striatal gradient of reward time horizons.
  • Ali listed a bunch of work on local modulation of DA release:
    • Local control of striatal dopamine release.
    • Synaptic-like axo-axonal transmission from striatal cholinergic interneurons onto dopaminergic fibers.
    • Spatial and temporal scales of dopamine transmission.
    • Striatal dopamine neurotransmission: Regulation of release and uptake.
    • Striatal Dopamine Release Is Triggered by Synchronized Activity in Cholinergic Interneurons.
    • An action potential initiation mechanism in distal axons for the control of dopamine release.

Read the transcript, produced by The Transmitter.

0:00 - Intro 3:42 - Dopamine: the history of theories 32:54 - Importance of learning and behavior studies 39:12 - Dopamine and causality 1:06:45 - Controversy over Vijay's findings

The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists. 

Read more about our partnership.

Check out this story:  Monkeys build mental maps to navigate new tasks

Sign up for “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released.

To explore more neuroscience news and perspectives, visit thetransmitter.org.

Kim Stachenfeld embodies the original core focus of this podcast, the exploration of the intersection between neuroscience and AI, now commonly known as Neuro-AI. That's because she walks both lines. Kim is a Senior Research Scientist at Google DeepMind, the AI company that sprang from neuroscience principles, and also does research at the Center for Theoretical Neuroscience at Columbia University. She's been using her expertise in modeling, and reinforcement learning, and cognitive maps, for example, to help understand brains and to help improve AI. I've been wanting to have her on for a long time to get her broad perspective on AI and neuroscience.

We discuss the relative roles of industry and academia in pursuing various objectives related to understanding and building cognitive entities

She's studied the hippocampus in her research on reinforcement learning and cognitive maps, so we discuss what the heck the hippocampus does since it seems to implicated in so many functions, and how she thinks of reinforcement learning these days.

Most recently Kim at Deepmind has focused on more practical engineering questions, using deep learning models to predict things like chaotic turbulent flows, and even to help design things like bridges and airplanes. And we don't get into the specifics of that work, but, given that I just spoke with Damian Kelty-Stephen, who thinks of brains partially as turbulent cascades, Kim and I discuss how her work on modeling turbulence has shaped her thoughts about brains.

• Kim's website.
• Twitter: @neuro_kim.
• Related papers
  • Scaling Laws for Neural Language Models.
  • Emergent Abilities of Large Language Models.
  • Learned simulators:
    • Learned coarse models for efficient turbulence simulation.
    • Physical design using differentiable learned simulators.

Check out the transcript, provided by The Transmitter.

0:00 - Intro 4:31 - Deepmind's original and current vision 9:53 - AI as tools and models 12:53 - Has AI hindered neuroscience? 17:05 - Deepmind vs academic work balance 20:47 - Is industry better suited to understand brains? 24?42 - Trajectory of Deepmind 27:41 - Kim's trajectory 33:35 - Is the brain a ML entity? 36:12 - Hippocampus 44:12 - Reinforcement learning 51:32 - What does neuroscience need more and less of? 1:02:53 - Neuroscience in a weird place? 1:06:41 - How Kim's questions have changed 1:16:31 - Intelligence and LLMs 1:25:34 - Challenges

Àlex Gómez-Marín heads The Behavior of Organisms Laboratory at the Institute of Neuroscience in Alicante, Spain. He's one of those theoretical physicist turned neuroscientist, and he has studied a wide range of topics over his career. Most recently, he has become interested in what he calls the "edges of consciousness", which encompasses the many trying to explain what may be happening when we have experiences outside our normal everyday experiences. For example, when we are under the influence of hallucinogens, when have near-death experiences (as Alex has), paranormal experiences, and so on.

So we discuss what led up to his interests in these edges of consciousness, how he now thinks about consciousness and doing science in general, how important it is to make room for all possible explanations of phenomena, and to leave our metaphysics open all the while.

• Alex's website: The Behavior of Organisms Laboratory.
• Twitter: @behaviOrganisms.
• Previous episodes:
  • BI 168 Frauke Sandig and Eric Black w Alex Gomez-Marin: AWARE: Glimpses of Consciousness.
  • BI 136 Michel Bitbol and Alex Gomez-Marin: Phenomenology.
• Related:
  • The Consciousness of Neuroscience.
  • Seeing the consciousness forest for the trees.
  • The stairway to transhumanist heaven.

0:00 - Intro 4:13 - Evolving viewpoints 10:05 - Near-death experience 18:30 - Mechanistic neuroscience vs. the rest 22:46 - Are you doing science? 33:46 - Where is my. mind? 44:55 - Productive vs. permissive brain 59:30 - Panpsychism 1:07:58 - Materialism 1:10:38 - How to choose what to do 1:16:54 - Fruit flies 1:19:52 - AI and the Singularity

Damian Kelty-Stephen is an experimental psychologist at State University of New York at New Paltz. Last episode with Luis Favela, we discussed many of the ideas from ecological psychology, and how Louie is trying to reconcile those principles with those of neuroscience. In this episode, Damian and I in some ways continue that discussion, because Damian is also interested in unifying principles of ecological psychology and neuroscience. However, he is approaching it from a different perspective that Louie. What drew me originally to Damian was a paper he put together with a bunch of authors offering their own alternatives to the computer metaphor of the brain, which has come to dominate neuroscience. And we discuss that some, and I'll link to the paper in the show notes. But mostly we discuss Damian's work studying the fractal structure of our behaviors, connecting that structure across scales, and linking it to how our brains and bodies interact to produce our behaviors. Along the way, we talk about his interests in cascades dynamics and turbulence to also explain our intelligence and behaviors. So, I hope you enjoy this alternative slice into thinking about how we think and move in our bodies and in the world.

• Damian's website.
• Related papers
  • In search for an alternative to the computer metaphor of the mind and brain.
  • Multifractal emergent processes: Multiplicative interactions override nonlinear component properties.

0:00 - Intro 2:34 - Damian's background 9:02 - Brains 12:56 - Do neuroscientists have it all wrong? 16:56 - Fractals everywhere 28:01 - Fractality, causality, and cascades 32:01 - Cascade instability as a metaphor for the brain 40:43 - Damian's worldview 46:09 - What is AI missing? 54:26 - Turbulence 1:01:02 - Intelligence without fractals? Multifractality 1:10:28 - Ergodicity 1:19:16 - Fractality, intelligence, life 1:23:24 - What's exciting, changing viewpoints

Luis Favela is an Associate Professor at Indiana University Bloomington. He is part philosopher, part cognitive scientist, part many things, and on this episode we discuss his new book, The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment.

In the book, Louie presents his NeuroEcological Nexus Theory, or NExT, which, as the subtitle says, proposes a way forward to tie together our brains, our bodies, and the environment; namely it has a lot to do with the complexity sciences and manifolds, which we discuss. But the book doesn't just present his theory. Among other things, it presents a rich historical look into why ecological psychology and neuroscience haven't been exactly friendly over the years, in terms of how to explain our behaviors, the role of brains in those explanations, how to think about what minds are, and so on. And it suggests how the two fields can get over their differences and be friends moving forward. And I'll just say, it's written in a very accessible manner, gently guiding the reader through many of the core concepts and science that have shaped ecological psychology and neuroscience, and for that reason alone I highly it.

Ok, so we discuss a bunch of topics in the book, how Louie thinks, and Louie gives us some great background and historical lessons along the way.

• Luis' website.
• Book:
  • The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment

0:00 - Intro 7:05 - Louie's target with NEXT 20:37 - Ecological psychology and grid cells 22:06 - Why irreconcilable? 28:59 - Why hasn't ecological psychology evolved more? 47:13 - NExT 49:10 - Hypothesis 1 55:45 - Hypothesis 2 1:02:55 - Artificial intelligence and ecological psychology 1:16:33 - Manifolds 1:31:20 - Hypothesis 4: Body, low-D, Synergies 1:35:53 - Hypothesis 5: Mind emerges 1:36:23 - Hypothesis 6:

Jovo, as you'll learn, is theoretically oriented, and enjoys the formalism of mathematics to approach questions that begin with a sense of wonder. So after I learn more about his overall approach, the first topic we discuss is the world's currently largest map of an entire brain... the connectome of an insect, the fruit fly. We talk about his role in this collaborative effort, what the heck a connectome is, why it's useful and what to do with it, and so on.

The second main topic we discuss is his theoretical work on what his team has called prospective learning. Prospective learning differs in a fundamental way from the vast majority of AI these days, which they call retrospective learning. So we discuss what prospective learning is, and how it may improve AI moving forward.

At some point there's a little audio/video sync issues crop up, so we switched to another recording method and fixed it... so just hang tight if you're viewing the podcast... it'll get better soon.

0:00 - Intro 05:25 - Jovo's approach 13:10 - Connectome of a fruit fly 26:39 - What to do with a connectome 37:04 - How important is a connectome? 51:48 - Prospective learning 1:15:20 - Efficiency 1:17:38 - AI doomerism

Jolande Fooken is a post-postdoctoral researcher interested in how we move our eyes and move our hands together to accomplish naturalistic tasks. Hand-eye coordination is one of those things that sounds simple and we do it all the time to make meals for our children day in, and day out, and day in, and day out. But it becomes way less seemingly simple as soon as you learn how we make various kinds of eye movements, and how we make various kinds of hand movements, and use various strategies to do repeated tasks. And like everything in the brain sciences, it's something we don't have a perfect story for yet. So, Jolande and I discuss her work, and thoughts, and ideas around those and related topics.

• Jolande's website.
• Twitter: @ookenfooken.
• Related papers
  • I am a parent. I am a scientist.
  • Eye movement accuracy determines natural interception strategies.
  • Perceptual-cognitive integration for goal-directed action in naturalistic environments.

0:00 - Intro 3:27 - Eye movements 8:53 - Hand-eye coordination 9:30 - Hand-eye coordination and naturalistic tasks 26:45 - Levels of expertise 34:02 - Yarbus and eye movements 42:13 - Varieties of experimental paradigms, varieties of viewing the brain 52:46 - Career vision 1:04:07 - Evolving view about the brain 1:10:49 - Coordination, robots, and AI

Recently I was invited to moderate a panel at the annual Computational and Systems Neuroscience, or COSYNE, conference. This year was the 20th anniversary of COSYNE, and we were in Lisbon Porturgal. The panel goal was to discuss the relationship between neuroscience and AI. The panelists were Tony Zador, Alex Pouget, Blaise Aguera y Arcas, Kim Stachenfeld, Jonathan Pillow, and Eva Dyer. And I'll let them introduce themselves soon. Two of the panelists, Tony and Alex, co-founded COSYNE those 20 years ago, and they continue to have different views about the neuro-AI relationship. Tony has been on the podcast before and will return soon, and I'll also have Kim Stachenfeld on in a couple episodes. I think this was a fun discussion, and I hope you enjoy it. There's plenty of back and forth, a wide range of opinions, and some criticism from one of the audience questioners. This is an edited audio version, to remove long dead space and such. There's about 30 minutes of just panel, then the panel starts fielding questions from the audience.

• COSYNE.

Mazviita Chirimuuta is a philosopher at the University of Edinburgh. Today we discuss topics from her new book, The Brain Abstracted: Simplification in the History and Philosophy of Neuroscience.

She largely argues that when we try to understand something complex, like the brain, using models, and math, and analogies, for example - we should keep in mind these are all ways of simplifying and abstracting away details to give us something we actually can understand. And, when we do science, every tool we use and perspective we bring, every way we try to attack a problem, these are all both necessary to do the science and limit the interpretation we can claim from our results. She does all this and more by exploring many topics in neuroscience and philosophy throughout the book, many of which we discuss today.

• Mazviita's University of Edinburgh page.
• The Brain Abstracted: Simplification in the History and Philosophy of Neuroscience.
• Previous Brain Inspired episodes:
  • BI 072 Mazviita Chirimuuta: Understanding, Prediction, and Reality
  • BI 114 Mark Sprevak and Mazviita Chirimuuta: Computation and the Mind

0:00 - Intro 5:28 - Neuroscience to philosophy 13:39 - Big themes of the book 27:44 - Simplifying by mathematics 32:19 - Simplifying by reduction 42:55 - Simplification by analogy 46:33 - Technology precedes science 55:04 - Theory, technology, and understanding 58:04 - Cross-disciplinary progress 58:45 - Complex vs. simple(r) systems 1:08:07 - Is science bound to study stability? 1:13:20 - 4E for philosophy but not neuroscience? 1:28:50 - ANNs as models 1:38:38 - Study of mind

As some of you know, I recently got back into the research world, and in particular I work in Eric Yttris' lab at Carnegie Mellon University.

Eric's lab studies the relationship between various kinds of behaviors and the neural activity in a few areas known to be involved in enacting and shaping those behaviors, namely the motor cortex and basal ganglia.  And study that, he uses tools like optogentics, neuronal recordings, and stimulations, while mice perform certain tasks, or, in my case, while they freely behave wandering around an enclosed space.

We talk about how Eric got here, how and why the motor cortex and basal ganglia are still mysteries despite lots of theories and experimental work, Eric's work on trying to solve those mysteries using both trained tasks and more naturalistic behavior. We talk about the valid question, "What is a behavior?", and lots more.

Yttri Lab

• Twitter: @YttriLab
• Related papers
  • Opponent and bidirectional control of movement velocity in the basal ganglia.
  • B-SOiD, an open-source unsupervised algorithm for identification and fast prediction of behaviors.

0:00 - Intro 2:36 - Eric's background 14:47 - Different animal models 17:59 - ANNs as models for animal brains 24:34 - Main question 25:43 - How circuits produce appropriate behaviors 26:10 - Cerebellum 27:49 - What do motor cortex and basal ganglia do? 49:12 - Neuroethology 1:06:09 - What is a behavior? 1:11:18 - Categorize behavior (B-SOiD) 1:22:01 - Real behavior vs. ANNs 1:33:09 - Best era in neuroscience

Peter Stratton is a research scientist at Queensland University of Technology.

I was pointed toward Pete by a patreon supporter, who sent me a sort of perspective piece Pete wrote that is the main focus of our conversation, although we also talk about some of his work in particular - for example, he works with spiking neural networks, like my last guest, Dan Goodman.

What Pete argues for is what he calls a sideways-in approach. So a bottom-up approach is to build things like we find them in the brain, put them together, and voila, we'll get cognition. A top-down approach, the current approach in AI, is to train a system to perform a task, give it some algorithms to run, and fiddle with the architecture and lower level details until you pass your favorite benchmark test. Pete is focused more on the principles of computation brains employ that current AI doesn't. If you're familiar with David Marr, this is akin to his so-called "algorithmic level", but it's between that and the "implementation level", I'd say. Because Pete is focused on the synthesis of different kinds of brain operations - how they intermingle to perform computations and produce emergent properties. So he thinks more like a systems neuroscientist in that respect. Figuring that out is figuring out how to make better AI, Pete says. So we discuss a handful of those principles, all through the lens of how challenging a task it is to synthesize multiple principles into a coherent functioning whole (as opposed to a collection of parts). Buy, hey, evolution did it, so I'm sure we can, too, right?

• Peter's website.
• Related papers
  • Convolutionary, Evolutionary, and Revolutionary: What’s Next for Brains, Bodies, and AI?
  • Making a Spiking Net Work: Robust brain-like unsupervised machine learning.
  • Global segregation of cortical activity and metastable dynamics.
  • Unlocking neural complexity with a robotic key

0:00 - Intro 3:50 - AI background, neuroscience principles 8:00 - Overall view of modern AI 14:14 - Moravec's paradox and robotics 20:50 -Understanding movement to understand cognition 30:01 - How close are we to understanding brains/minds? 32:17 - Pete's goal 34:43 - Principles from neuroscience to build AI 42:39 - Levels of abstraction and implementation 49:57 - Mental disorders and robustness 55:58 - Function vs. implementation 1:04:04 - Spiking networks 1:07:57 - The roadmap 1:19:10 - AGI 1:23:48 - The terms AGI and AI 1:26:12 - Consciousness

You may know my guest as the co-founder of Neuromatch, the excellent online computational neuroscience academy, or as the creator of the Brian spiking neural network simulator, which is freely available. I know him as a spiking neural network practitioner extraordinaire. Dan Goodman runs the Neural Reckoning Group at Imperial College London, where they use spiking neural networks to figure out how biological and artificial brains reckon, or compute.

All of the current AI we use to do all the impressive things we do, essentially all of it, is built on artificial neural networks. Notice the word "neural" there. That word is meant to communicate that these artificial networks do stuff the way our brains do stuff. And indeed, if you take a few steps back, spin around 10 times, take a few shots of whiskey, and squint hard enough, there is a passing resemblance. One thing you'll probably still notice, in your drunken stupor, is that, among the thousand ways ANNs differ from brains, is that they don't use action potentials, or spikes. From the perspective of neuroscience, that can seem mighty curious. Because, for decades now, neuroscience has focused on spikes as the things that make our cognition tick.

We count them and compare them in different conditions, and generally put a lot of stock in their usefulness in brains.

So what does it mean that modern neural networks disregard spiking altogether?

Maybe spiking really isn't important to process and transmit information as well as our brains do. Or maybe spiking is one among many ways for intelligent systems to function well. Dan shares some of what he's learned and how he thinks about spiking and SNNs and a host of other topics.

• Neural Reckoning Group.
• Twitter: @neuralreckoning.
• Related papers
  • Neural heterogeneity promotes robust learning.
  • Dynamics of specialization in neural modules under resource constraints.
  • Multimodal units fuse-then-accumulate evidence across channels.
  • Visualizing a joint future of neuroscience and neuromorphic engineering.

0:00 - Intro 3:47 - Why spiking neural networks, and a mathematical background 13:16 - Efficiency 17:36 - Machine learning for neuroscience 19:38 - Why not jump ship from SNNs? 23:35 - Hard and easy tasks 29:20 - How brains and nets learn 32:50 - Exploratory vs. theory-driven science 37:32 - Static vs. dynamic 39:06 - Heterogeneity 46:01 - Unifying principles vs. a hodgepodge 50:37 - Sparsity 58:05 - Specialization and modularity 1:00:51 - Naturalistic experiments 1:03:41 - Projects for SNN research 1:05:09 - The right level of abstraction 1:07:58 - Obstacles to progress 1:12:30 - Levels of explanation 1:14:51 - What has AI taught neuroscience? 1:22:06 - How has neuroscience helped AI?

Check out my free video series about what's missing in AI and Neuroscience

John Krakauer has been on the podcast multiple times (see links below). Today we discuss some topics framed around what he's been working on and thinking about lately. Things like

• Whether brains actually reorganize after damage
• The role of brain plasticity in general
• The path toward and the path not toward understanding higher cognition
• How to fix motor problems after strokes
• AGI
• Functionalism, consciousness, and much more.

Relevant links:

• John's Lab.
• Twitter: @blamlab
• Related papers
  • What are we talking about? Clarifying the fuzzy concept of representation in neuroscience and beyond.
  • Against cortical reorganisation.
• Other episodes with John:
  • BI 025 John Krakauer: Understanding Cognition
  • BI 077 David and John Krakauer: Part 1
  • BI 078 David and John Krakauer: Part 2
  • BI 113 David Barack and John Krakauer: Two Views On Cognition

Time stamps 0:00 - Intro 2:07 - It's a podcast episode! 6:47 - Stroke and Sherrington neuroscience 19:26 - Thinking vs. moving, representations 34:15 - What's special about humans? 56:35 - Does cortical reorganization happen? 1:14:08 - Current era in neuroscience

Check out my free video series about what's missing in AI and Neuroscience

By day, Max Bennett is an entrepreneur. He has cofounded and CEO'd multiple AI and technology companies. By many other countless hours, he has studied brain related sciences. Those long hours of research have payed off in the form of this book, A Brief History of Intelligence: Evolution, AI, and the Five Breakthroughs That Made Our Brains.

Three lines of research formed the basis for how Max synthesized knowledge into the ideas in his current book: findings from comparative psychology (comparing brains and minds of different species), evolutionary neuroscience (how brains have evolved), and artificial intelligence, especially the algorithms developed to carry out functions. We go through I think all five of the breakthroughs in some capacity. A recurring theme is that each breakthrough may explain multiple new abilities. For example, the evolution of the neocortex may have endowed early mammals with the ability to simulate or imagine what isn't immediately present, and this ability might further explain mammals' capacity to engage in vicarious trial and error (imagining possible actions before trying them out), the capacity to engage in counterfactual learning (what would have happened if things went differently than they did), and the capacity for episodic memory and imagination.

The book is filled with unifying accounts like that, and it makes for a great read. Strap in, because Max gives a sort of masterclass about many of the ideas in his book.

• Twitter:
  • @maxsbennett
• Book:
  • A Brief History of Intelligence: Evolution, AI, and the Five Breakthroughs That Made Our Brains.

0:00 - Intro 5:26 - Why evolution is important 7:22 - Maclean's triune brain 14:59 - Breakthrough 1: Steering 29:06 - Fish intelligence 40:38 - Breakthrough 3: Mentalizing 52:44 - How could we improve the human brain? 1:00:44 - What is intelligence? 1:13:50 - Breakthrough 5: Speaking

Welcome to another special panel discussion episode.

I was recently invited to moderate at discussion amongst 6 people at the annual Aspirational Neuroscience meetup. Aspirational Neuroscience is a nonprofit community run by Kenneth Hayworth. Ken has been on the podcast before on episode 103. Ken helps me introduce the meetup and panel discussion for a few minutes. The goal in general was to discuss how current and developing neuroscience technologies might be used to decode a nontrivial memory from a static connectome - what the obstacles are, how to surmount those obstacles, and so on.

There isn't video of the event, just audio, and because we were all sharing microphones and they were being passed around, you'll hear some microphone type noise along the way - but I did my best to optimize the audio quality, and it turned out mostly quite listenable I believe.

• Aspirational Neuroscience
• Panelists:
  • Anton Arkhipov, Allen Institute for Brain Science.
    • @AntonSArkhipov
  • Konrad Kording, University of Pennsylvania.
    • @KordingLab
  • Tomás Ryan, Trinity College Dublin.
    • @TJRyan_77
  • Srinivas Turaga, Janelia Research Campus.
  • Dong Song, University of Southern California.
    • @dongsong
  • Zhihao Zheng, Princeton University.
    • @zhihaozheng

0:00 - Intro 1:45 - Ken Hayworth 14:09 - Panel Discussion

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Laura Gradowski is a philosopher of science at the University of Pittsburgh. Pluralism is roughly the idea that there is no unified account of any scientific field, that we should be tolerant of and welcome a variety of theoretical and conceptual frameworks, and methods, and goals, when doing science. Pluralism is kind of a buzz word right now in my little neuroscience world, but it's an old and well-trodden notion... many philosophers have been calling for pluralism for many years. But how pluralistic should we be in our studies and explanations in science? Laura suggests we should be very, very pluralistic, and to make her case, she cites examples in the history of science of theories and theorists that were once considered "fringe" but went on to become mainstream accepted theoretical frameworks. I thought it would be fun to have her on to share her ideas about fringe theories, mainstream theories, pluralism, etc.

We discuss a wide range of topics, but also discuss some specific to the brain and mind sciences. Laura goes through an example of something and someone going from fringe to mainstream - the Garcia effect, named after John Garcia, whose findings went agains the grain of behaviorism, the dominant dogma of the day in psychology. But this overturning only happened after Garcia had to endure a long scientific hell of his results being ignored and shunned. So, there are multiple examples like that, and we discuss a handful. This has led Laura to the conclusion we should accept almost all theoretical frameworks, We discuss her ideas about how to implement this, where to draw the line, and much more.

0:00 - Intro 3:57 - What is fringe? 10:14 - What makes a theory fringe? 14:31 - Fringe to mainstream 17:23 - Garcia effect 28:17 - Fringe to mainstream: other examples 32:38 - Fringe and consciousness 33:19 - Words meanings change over time 40:24 - Pseudoscience 43:25 - How fringe becomes mainstream 47:19 - More fringe characteristics 50:06 - Pluralism as a solution 54:02 - Progress 1:01:39 - Encyclopedia of theories 1:09:20 - When to reject a theory 1:20:07 - How fringe becomes fringe 1:22:50 - Marginilization 1:27:53 - Recipe for fringe theorist

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Eric Shea-Brown is a theoretical neuroscientist and principle investigator of the working group on neural dynamics at the University of Washington. In this episode, we talk a lot about dynamics and dimensionality in neural networks... how to think about them, why they matter, how Eric's perspectives have changed through his career. We discuss a handful of his specific research findings about dynamics and dimensionality, like how dimensionality changes when one is performing a task versus when you're just sort of going about your day, what we can say about dynamics just by looking at different structural connection motifs, how different modes of learning can rely on different dimensionalities, and more.We also talk about how he goes about choosing what to work on and how to work on it. You'll hear in our discussion how much credit Eric gives to those surrounding him and those who came before him - he drops tons of references and names, so get ready if you want to follow up on some of the many lines of research he mentions.

• Eric's website.
• Related papers
  • Predictive learning as a network mechanism for extracting low-dimensional latent space representations.
  • A scale-dependent measure of system dimensionality.
  • From lazy to rich to exclusive task representations in neural networks and neural codes.
  • Feedback through graph motifs relates structure and function in complex networks.

0:00 - Intro 4:15 - Reflecting on the rise of dynamical systems in neuroscience 11:15 - DST view on macro scale 15:56 - Intuitions 22:07 - Eric's approach 31:13 - Are brains more or less impressive to you now? 38:45 - Why is dimensionality important? 50:03 - High-D in Low-D 54:14 - Dynamical motifs 1:14:56 - Theory for its own sake 1:18:43 - Rich vs. lazy learning 1:22:58 - Latent variables 1:26:58 - What assumptions give you most pause?

I was recently invited to moderate a panel at the Annual Bernstein conference - this one was in Berlin Germany. The panel I moderated was at a satellite workshop at the conference called How can machine learning be used to generate insights and theories in neuroscience? Below are the panelists. I hope you enjoy the discussion!

• Program: How can machine learning be used to generate insights and theories in neuroscience?
• Panelists:
  • Katrin Franke
    • Lab website.
    • Twitter: @kfrankelab.
  • Ralf Haefner
    • Haefner lab.
    • Twitter: @haefnerlab.
  • Martin Hebart
    • Hebart Lab.
    • Twitter: @martin_hebart.
  • Johannes Jaeger
    • Yogi's website.
    • Twitter: @yoginho.
  • Fred Wolf
    • Fred's university webpage.

Organizers:

• Alexander Ecker | University of Göttingen, Germany
• Fabian Sinz | University of Göttingen, Germany
• Mohammad Bashiri, Pavithra Elumalai, Michaela Vystrcilová | University of Göttingen, Germany

David runs his lab at NYU, where they stud`y auditory cognition, speech perception, language, and music. On the heels of the episode with David Glanzman, we discuss the ongoing mystery regarding how memory works, how to study and think about brains and minds, and the reemergence (perhaps) of the language of thought hypothesis.

David has been on the podcast a few times... once by himself, and again with Gyorgy Buzsaki.

• Poeppel lab
• Twitter: @davidpoeppel.
• Related papers
  • We don’t know how the brain stores anything, let alone words.
  • Memory in humans and deep language models: Linking hypotheses for model augmentation.
  • The neural ingredients for a language of thought are available.

0:00 - Intro 11:17 - Across levels 14:598 - Nature of memory 24:12 - Using the right tools for the right question 35:46 - LLMs, what they need, how they've shaped David's thoughts 44:55 - Across levels 54:07 - Speed of progress 1:02:21 - Neuroethology and mental illness - patreon 1:24:42 - Language of Thought

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Kevin Mitchell is professor of genetics at Trinity College Dublin. He's been on the podcast before, and we talked a little about his previous book, Innate – How the Wiring of Our Brains Shapes Who We Are. He's back today to discuss his new book Free Agents: How Evolution Gave Us Free Will. The book is written very well and guides the reader through a wide range of scientific knowledge and reasoning that undergirds Kevin's main take home: our free will comes from the fact that we are biological organisms, biological organisms have agency, and as that agency evolved to become more complex and layered, so does our ability to exert free will. We touch on a handful of topics in the book, like the idea of agency, how it came about at the origin of life, and how the complexity of kinds of agency, the richness of our agency, evolved as organisms became more complex.

We also discuss Kevin's reliance on the indeterminacy of the universe to tell his story, the underlying randomness at fundamental levels of physics. Although indeterminacy isn't necessary for ongoing free will, it is responsible for the capacity for free will to exist in the first place. We discuss the brain's ability to harness its own randomness when needed, creativity, whether and how it's possible to create something new, artificial free will, and lots more.

• Kevin's website.
• Twitter: @WiringtheBrain
• Book: Free Agents: How Evolution Gave Us Free Will

4:27 - From Innate to Free Agents 9:14 - Thinking of the whole organism 15:11 - Who the book is for 19:49 - What bothers Kevin 27:00 - Indeterminacy 30:08 - How it all began 33:08 - How indeterminacy helps 43:58 - Libet's free will experiments 50:36 - Creativity 59:16 - Selves, subjective experience, agency, and free will 1:10:04 - Levels of agency and free will 1:20:38 - How much free will can we have? 1:28:03 - Hierarchy of mind constraints 1:36:39 - Artificial agents and free will 1:42:57 - Next book?

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Alicia Juarrero is a philosopher and has been interested in complexity since before it was cool.

In this episode, we discuss many of the topics and ideas in her new book, Context Changes Everything: How Constraints Create Coherence, which makes the thorough case that constraints should be given way more attention when trying to understand complex systems like brains and minds - how they're organized, how they operate, how they're formed and maintained, and so on. Modern science, thanks in large part to the success of physics, focuses on a single kind of causation - the kind involved when one billiard ball strikes another billiard ball. But that kind of causation neglects what Alicia argues are the most important features of complex systems the constraints that shape the dynamics and possibility spaces of systems. Much of Alicia's book describes the wide range of types of constraints we should be paying attention to, and how they interact and mutually influence each other. I highly recommend the book, and you may want to read it before, during, and after our conversation. That's partly because, if you're like me, the concepts she discusses still aren't comfortable to think about the way we're used to thinking about how things interact. Thinking across levels of organization turns out to be hard. You might also want her book handy because, hang on to your hats, we jump around a lot among those concepts. Context Changes everything comes about 25 years after her previous classic, Dynamics In Action, which we also discuss and which I also recommend if you want more of a primer to her newer more expansive work. Alicia's work touches on all things complex, from self-organizing systems like whirlpools, to ecologies, businesses, societies, and of course minds and brains.

• Book:
  • Context Changes Everything: How Constraints Create Coherence

0:00 - Intro 3:37 - 25 years thinking about constraints 8:45 - Dynamics in Action and eliminativism 13:08 - Efficient and other kinds of causation 19:04 - Complexity via context independent and dependent constraints 25:53 - Enabling and limiting constraints 30:55 - Across scales 36:32 - Temporal constraints 42:58 - A constraint cookbook? 52:12 - Constraints in a mechanistic worldview 53:42 - How to explain using constraints 56:22 - Concepts and multiple realizabillity 59:00 - Kevin Mitchell question 1:08:07 - Mac Shine Question 1:19:07 - 4E 1:21:38 - Dimensionality across levels 1:27:26 - AI and constraints 1:33:08 - AI and life

In the intro, I mention the Bernstein conference workshop I'll participate in, called How can machine learning be used to generate insights and theories in neuroscience?. Follow that link to learn more, and register for the conference here. Hope to see you there in late September in Berlin!

Justin Wood runs the Wood Lab at Indiana University, and his lab's tagline is "building newborn minds in virtual worlds." In this episode, we discuss his work comparing the visual cognition of newborn chicks and AI models. He uses a controlled-rearing technique with natural chicks, whereby the chicks are raised from birth in completely controlled visual environments. That way, Justin can present designed visual stimuli to test what kinds of visual abilities chicks have or can immediately learn. Then he can building models and AI agents that are trained on the same data as the newborn chicks. The goal is to use the models to better understand natural visual intelligence, and use what we know about natural visual intelligence to help build systems that better emulate biological organisms. We discuss some of the visual abilities of the chicks and what he's found using convolutional neural networks. Beyond vision, we discuss his work studying the development of collective behavior, which compares chicks to a model that uses CNNs, reinforcement learning, and an intrinsic curiosity reward function. All of this informs the age-old nature (nativist) vs. nurture (empiricist) debates, which Justin believes should give way to embrace both nature and nurture.

• Wood lab.
• Related papers:
  • Controlled-rearing studies of newborn chicks and deep neural networks.
  • Development of collective behavior in newborn artificial agents.
  • A newborn embodied Turing test for view-invariant object recognition.
• Justin mentions these papers:
  • Untangling invariant object recognition (Dicarlo & Cox 2007)

0:00 - Intro 5:39 - Origins of Justin's current research 11:17 - Controlled rearing approach 21:52 - Comparing newborns and AI models 24:11 - Nativism vs. empiricism 28:15 - CNNs and early visual cognition 29:35 - Smoothness and slowness 50:05 - Early biological development 53:27 - Naturalistic vs. highly controlled 56:30 - Collective behavior in animals and machines 1:02:34 - Curiosity and critical periods 1:09:05 - Controlled rearing vs. other developmental studies 1:13:25 - Breaking natural rules 1:16:33 - Deep RL collective behavior 1:23:16 - Bottom-up and top-down

David runs his lab at UCLA where he's also a distinguished professor.  David used to believe what is currently the mainstream view, that our memories are stored in our synapses, those connections between our neurons.  So as we learn, the synaptic connections strengthen and weaken until their just right, and that serves to preserve the memory. That's been the dominant view in neuroscience for decades, and is the fundamental principle that underlies basically all of deep learning in AI. But because of his own and others experiments, which he describes in this episode, David has come to the conclusion that memory must be stored not at the synapse, but in the nucleus of neurons, likely by some epigenetic mechanism mediated by RNA molecules. If this sounds familiar, I had Randy Gallistel on the the podcast on episode 126 to discuss similar ideas, and David discusses where he and Randy differ in their thoughts. This episode starts out pretty technical as David describes the series of experiments that changed his mind, but after that we broaden our discussion to a lot of the surrounding issues regarding whether and if his story about memory is true. And we discuss meta-issues like how old discarded ideas in science often find their way back, what it's like studying non-mainstream topic, including challenges trying to get funded for it, and so on.

• David's Faculty Page.
• Related papers
  • The central importance of nuclear mechanisms in the storage of memory.
  • David mentions Arc and virus-like transmission:
    • The Neuronal Gene Arc Encodes a Repurposed Retrotransposon Gag Protein that Mediates Intercellular RNA Transfer.
    • Structure of an Arc-ane virus-like capsid.
• David mentions many of the ideas from the Pushing the Boundaries: Neuroscience, Cognition, and Life  Symposium.
• Related episodes:
  • BI 126 Randy Gallistel: Where Is the Engram?
  • BI 127 Tomás Ryan: Memory, Instinct, and Forgetting

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My guest is Michael C. Frank, better known as Mike Frank, who runs the Language and Cognition lab at Stanford. Mike's main interests center on how children learn language - in particular he focuses a lot on early word learning, and what that tells us about our other cognitive functions, like concept formation and social cognition.

We discuss that, his love for developing open data sets that anyone can use,

The dance he dances between bottom-up data-driven approaches in this big data era, traditional experimental approaches, and top-down theory-driven approaches

How early language learning in children differs from LLM learning

Mike's rational speech act model of language use, which considers the intentions or pragmatics of speakers and listeners in dialogue.

• Language & Cognition Lab
• Twitter: @mcxfrank.
  • I mentioned Mike's tweet thread about saying LLMs "have" cognitive functions:
• Related papers:
  • Pragmatic language interpretation as probabilistic inference.
  • Toward a “Standard Model” of Early Language Learning.
  • The pervasive role of pragmatics in early language.
  • The Structure of Developmental Variation in Early Childhood.
  • Relational reasoning and generalization using non-symbolic neural networks.
  • Unsupervised neural network models of the ventral visual stream.

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In this episode I have a casual chat with Ali Mohebi about his new faculty position and his plans for the future.

• Ali's website.
• Twitter: @mohebial

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My guest today is Andrea Martin, who is the Research Group Leader in the department of Language and Computation in Neural Systems at the Max Plank Institute and the Donders Institute. Andrea is deeply interested in understanding how our biological brains process and represent language. To this end, she is developing a theoretical model of language. The aim of the model is to account for the properties of language, like its structure, its compositionality, its infinite expressibility, while adhering to physiological data we can measure from human brains.

Her theoretical model of language, among other things, brings in the idea of low-dimensional manifolds and neural dynamics along those manifolds. We've discussed manifolds a lot on the podcast, but they are a kind of abstract structure in the space of possible neural population activity - the neural dynamics. And that manifold structure defines the range of possible trajectories, or pathways, the neural dynamics can take over  time.

One of Andrea's ideas is that manifolds might be a way for the brain to combine two properties of how we learn and use language. One of those properties is the statistical regularities found in language - a given word, for example, occurs more often near some words and less often near some other words. This statistical approach is the foundation of how large language models are trained. The other property is the more formal structure of language: how it's arranged and organized in such a way that gives it meaning to us. Perhaps these two properties of language can come together as a single trajectory along a neural manifold. But she has lots of ideas, and we discuss many of them. And of course we discuss large language models, and how Andrea thinks of them with respect to biological cognition. We talk about modeling in general and what models do and don't tell us, and much more.

• Andrea's website.
• Twitter: @andrea_e_martin.
• Related papers
  • A Compositional Neural Architecture for Language
  • An oscillating computational model can track pseudo-rhythmic speech by using linguistic predictions
  • Neural dynamics differentially encode phrases and sentences during spoken language comprehension
  • Hierarchical structure in language and action: A formal comparison
• Andrea mentions this book: The Geometry of Biological Time.

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This is one in a periodic series of episodes with Alex Gomez-Marin, exploring how the arts and humanities can impact (neuro)science. Artistic creations, like cinema, have the ability to momentarily lower our ever-critical scientific mindset and allow us to imagine alternate possibilities and experience emotions outside our normal scientific routines. Might this feature of art potentially change our scientific attitudes and perspectives?

Frauke Sandig and Eric Black recently made the documentary film AWARE: Glimpses of Consciousness, which profiles six researchers studying consciousness from different perspectives. The film is filled with rich visual imagery and conveys a sense of wonder and awe in trying to understand subjective experience, while diving deep into the reflections of the scientists and thinkers approaching the topic from their various perspectives.

This isn't a "normal" Brain Inspired episode, but I hope you enjoy the discussion!

• AWARE: Glimpses of Consciousness
• Umbrella Films

0:00 - Intro 19:42 - Mechanistic reductionism 45:33 - Changing views during lifetime 53:49 - Did making the film alter your views? 57:49 - ChatGPT 1:04:20 - Materialist assumption 1:11:00 - Science of consciousness 1:20:49 - Transhumanism 1:32:01 - Integrity 1:36:19 - Aesthetics 1:39:50 - Response to the film

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Panayiota Poirazi runs the Poirazi Lab at the FORTH Institute of Molecular Biology and Biotechnology, and Yiota loves dendrites, those branching tree-like structures sticking out of all your neurons, and she thinks you should love dendrites, too, whether you study biological or artificial intelligence. In neuroscience, the old story was that dendrites just reach out and collect incoming signals for the all-important neuron cell body to process. Yiota, and people Like Matthew Larkum, with whom I chatted in episode 138, are continuing to demonstrate that dendrites are themselves computationally complex and powerful, doing many varieties of important signal transformation before signals reach the cell body. For example, in 2003, Yiota showed that because of dendrites, a single neuron can act as a two-layer artificial neural network, and since then others have shown single neurons can act as deeper and deeper multi-layer networks.  In Yiota's opinion, an even more important function of dendrites is increased computing efficiency, something evolution favors and something artificial networks need to favor as well moving forward.

• Poirazi Lab
• Twitter: @YiotaPoirazi.
• Related papers
  • Drawing Inspiration from Biological Dendrites to Empower Artificial Neural Networks.
  • Illuminating dendritic function with computational models.
  • Introducing the Dendrify framework for incorporating dendrites to spiking neural networks.
  • Pyramidal Neuron as Two-Layer Neural Network

0:00 - Intro 3:04 - Yiota's background 6:40 - Artificial networks and dendrites 9:24 - Dendrites special sauce? 14:50 - Where are we in understanding dendrite function? 20:29 - Algorithms, plasticity, and brains 29:00 - Functional unit of the brain 42:43 - Engrams 51:03 - Dendrites and nonlinearity 54:51 - Spiking neural networks 56:02 - Best level of biological detail 57:52 - Dendrify 1:05:41 - Experimental work 1:10:58 - Dendrites across species and development 1:16:50 - Career reflection 1:17:57 - Evolution of Yiota's thinking

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Nick Enfield is a professor of linguistics at the University of Sydney. In this episode we discuss topics in his most recent book, Language vs. Reality: Why Language Is Good for Lawyers and Bad for Scientists. A central question in the book is what is language for? What's the function of language. You might be familiar with the debate about whether language evolved for each of us thinking our wonderful human thoughts, or for communicating those thoughts between each other. Nick would be on the communication side of that debate, but if by communication we mean simply the transmission of thoughts or information between people - I have a thought, I send it to you in language, and that thought is now in your head - then Nick wouldn't take either side of that debate. He argues the function language goes beyond the transmission of information, and instead is primarily an evolved solution for social coordination - coordinating our behaviors and attention. When we use language, we're creating maps in our heads so we can agree on where to go.

For example, when I say, "This is brain inspired," I'm pointing you to a place to meet me on a conceptual map, saying, "Get ready, we're about to have a great time again!"  In any case, with those 4 words, "This is brain inspired," I'm not just transmitting information from my head into your head. I'm providing you with a landmark so you can focus your attention appropriately.

From that premise, that language is about social coordination, we talk about a handful of topics in his book, like the relationship between language and reality, the idea that all language is framing- that is, how we say something influences how to think about it. We discuss how our language changes in different social situations, the role of stories, and of course, how LLMs fit into Nick's story about language.

• Nick's website
• Twitter: @njenfield
• Book:
  • Language vs. Reality: Why Language Is Good for Lawyers and Bad for Scientists.
• Papers:
  • Linguistic concepts are self-generating choice architectures

0:00 - Intro 4:23 - Is learning about language important? 15:43 - Linguistic Anthropology 28:56 - Language and truth 33:57 - How special is language 46:19 - Choice architecture and framing 48:19 - Language for thinking or communication 52:30 - Agency and language 56:51 - Large language models 1:16:18 - Getting language right 1:20:48 - Social relationships and language

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Jeffrey Bowers is a psychologist and professor at the University of Bristol. As you know, many of my previous guests are in the business of comparing brain activity to the activity of units in artificial neural network models, when humans or animals and the models are performing the same tasks. And a big story that has emerged over the past decade or so is that there's a remarkable similarity between the activities and representations in brains and models. This was originally found in object categorization tasks, where the goal is to name the object shown in a given image, where researchers have compared the activity in the models good at doing that to the activity in the parts of our brains good at doing that. It's been found in various other tasks using various other models and analyses, many of which we've discussed on previous episodes, and more recently a similar story has emerged regarding a similarity between language-related activity in our brains and the activity in large language models. Namely, the ability of our brains to predict an upcoming word can been correlated with the models ability to predict an upcoming word. So the word is that these deep learning type models are the best models of how our brains and cognition work.

However, this is where Jeff Bowers comes in and raises the psychology flag, so to speak. His message is that these predictive approaches to comparing artificial and biological cognition aren't enough, and can mask important differences between them. And what we need to do is start performing more hypothesis driven tests like those performed in psychology, for example, to ask whether the models are indeed solving tasks like our brains and minds do. Jeff and his group, among others, have been doing just that are discovering differences in models and minds that may be important if we want to use models to understand minds. We discuss some of his work and thoughts in this regard, and a lot more.

• Website
• Twitter: @jeffrey_bowers
• Related papers:
  • Deep Problems with Neural Network Models of Human Vision.
  • Parallel Distributed Processing Theory in the Age of Deep Networks.
  • Successes and critical failures of neural networks in capturing human-like speech recognition.

0:00 - Intro 3:52 - Testing neural networks 5:35 - Neuro-AI needs psychology 23:36 - Experiments in AI and neuroscience 23:51 - Why build networks like our minds? 44:55 - Vision problem spaces, solution spaces, training data 55:45 - Do we implement algorithms? 1:01:33 - Relational and combinatorial cognition 1:06:17 - Comparing representations in different networks 1:12:31 - Large language models 1:21:10 - Teaching LLMs nonsense languages

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Gary Lupyan runs the Lupyan Lab at University of Wisconsin, Madison, where he studies how language and cognition are related. In some ways, this is a continuation of the conversation I had last episode with Ellie Pavlick, in that we  partly continue to discuss large language models. But Gary is more focused on how language, and naming things, categorizing things, changes our cognition related those things. How does naming something change our perception of it, and so on. He's interested in how concepts come about, how they map onto language. So we talk about some of his work and ideas related to those topics.

And we actually start the discussion with some of Gary's work related the variability of individual humans' phenomenal experience, and how that affects our individual cognition. For instance, some people are more visual thinkers, others are more verbal, and there seems to be an appreciable spectrum of differences that Gary is beginning to experimentally test.

• Lupyan Lab.
• Twitter: @glupyan.
• Related papers:
  • Hidden Differences in Phenomenal Experience.
  • Verbal interference paradigms: A systematic review investigating the role of language in cognition.
• Gary mentioned Richard Feynman's Ways of Thinking video.
• Gary and Andy Clark's Aeon article: Super-cooperators.

0:00 - Intro 2:36 - Words and communication 14:10 - Phenomenal variability 26:24 - Co-operating minds 38:11 - Large language models 40:40 - Neuro-symbolic AI, scale 44:43 - How LLMs have changed Gary's thoughts about language 49:26 - Meaning, grounding, and language 54:26 - Development of language 58:53 - Symbols and emergence 1:03:20 - Language evolution in the LLM era 1:08:05 - Concepts 1:11:17 - How special is language? 1:18:08 - AGI

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Ellie Pavlick runs her Language Understanding and Representation Lab at Brown University, where she studies lots of topics related to language. In AI, large language models, sometimes called foundation models, are all the rage these days, with their ability to generate convincing language, although they still make plenty of mistakes. One of the things Ellie is interested in is how these models work, what kinds of representations are being generated in them to produce the language they produce. So we discuss how she's going about studying these models. For example, probing them to see whether something symbolic-like might be implemented in the models, even though they are the deep learning neural network type, which aren't suppose to be able to work in a symbol-like manner. We also discuss whether grounding is required for language understanding - that is, whether a model that produces language well needs to connect with the real world to actually understand the text it generates. We talk about what language is for, the current limitations of large language models, how the models compare to humans, and a lot more.

• Language Understanding and Representation Lab
• Twitter: @Brown_NLP
• Related papers
  • Semantic Structure in Deep Learning.
  • Pretraining on Interactions for Learning Grounded Affordance Representations.
  • Mapping Language Models to Grounded Conceptual Spaces.

0:00 - Intro 2:34 - Will LLMs make us dumb? 9:01 - Evolution of language 17:10 - Changing views on language 22:39 - Semantics, grounding, meaning 37:40 - LLMs, humans, and prediction 41:19 - How to evaluate LLMs 51:08 - Structure, semantics, and symbols in models 1:00:08 - Dimensionality 1:02:08 - Limitations of LLMs 1:07:47 - What do linguists think? 1:14:23 - What is language for?

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Earl Miller runs the Miller Lab at MIT, where he studies how our brains carry out our executive functions, like working memory, attention, and decision-making. In particular he is interested in the role of the prefrontal cortex and how it coordinates with other brain areas to carry out these functions. During this episode, we talk broadly about how neuroscience has changed during Earl's career, and how his own thoughts have changed. One thing we focus on is the increasing appreciation of brain oscillations for our cognition.

Recently on BI we've discussed oscillations quite a bit. In episode 153, Carolyn Dicey-Jennings discussed her philosophical ideas relating attention to the notion of the self, and she leans a lot on Earl's research to make that argument.  In episode 160, Ole Jensen discussed his work in humans showing that  low frequency oscillations exert a top-down control on incoming sensory stimuli, and this is directly in agreement with Earl's work over many years in nonhuman primates. So we continue that discussion relating low-frequency oscillations to executive control. We also discuss a new concept Earl has developed called spatial computing, which is an account of how brain oscillations can dictate where in various brain areas neural activity be on or off, and hence contribute or not to ongoing mental function. We also discuss working memory in particular, and a host of related topics.

• Miller lab.
• Twitter: @MillerLabMIT.
• Related papers:
  • An integrative theory of prefrontal cortex function. Annual Review of Neuroscience.
  • Working Memory Is Complex and Dynamic, Like Your Thoughts.
  • Traveling waves in the prefrontal cortex during working memory.

0:00 - Intro 6:22 - Evolution of Earl's thinking 14:58 - Role of the prefrontal cortex 25:21 - Spatial computing 32:51 - Homunculus problem 35:34 - Self 37:40 - Dimensionality and thought 46:13 - Reductionism 47:38 - Working memory and capacity 1:01:45 - Capacity as a principle 1:05:44 - Silent synapses 1:10:16 - Subspaces in dynamics

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Hugo Spiers runs the Spiers Lab at University College London. In general Hugo is interested in understanding spatial cognition, like navigation, in relation to other processes like planning and goal-related behavior, and how brain areas like the hippocampus and prefrontal cortex coordinate these cognitive functions. So, in this episode, we discuss a range of his research and thoughts around those topics. You may have heard about the studies he's been involved with for years, regarding London taxi drivers and how their hippocampus changes as a result of their grueling efforts to memorize how to best navigate London. We talk about that, we discuss the concept of a schema, which is roughly an abstracted form of knowledge that helps you know how to behave in different environments. Probably the most common example is that we all have a schema for eating at a restaurant, independent of which restaurant we visit, we know about servers, and menus, and so on. Hugo is interested in spatial schemas, for things like navigating a new city you haven't visited. Hugo describes his work using reinforcement learning methods to compare how humans and animals solve navigation tasks. And finally we talk about the video game Hugo has been using to collect vast amount of data related to navigation, to answer questions like how our navigation ability changes over our lifetimes, the different factors that seem to matter more for our navigation skills, and so on.

• Spiers Lab.
• Twitter: @hugospiers.
• Related papers
  • Predictive maps in rats and humans for spatial navigation.
  • From cognitive maps to spatial schemas.
  • London taxi drivers: A review of neurocognitive studies and an exploration of how they build their cognitive map of London.
  • Explaining World-Wide Variation in Navigation Ability from Millions of People: Citizen Science Project Sea Hero Quest.

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Ole Jensen is co-director of the Centre for Human Brain Health at University of Birmingham, where he runs his Neuronal Oscillations Group lab. Ole is interested in how the oscillations in our brains affect our cognition by helping to shape the spiking patterns of neurons, and by helping to allocate resources to parts of our brains that are relevant for whatever ongoing behaviors we're performing in different contexts. People have been studying oscillations for decades, finding that different frequencies of oscillations have been linked to a bunch of different cognitive functions. Some of what we discuss today is Ole's work on alpha oscillations, which are around 10 hertz, so 10 oscillations per second. The overarching story is that alpha oscillations are thought to inhibit or disrupt processing in brain areas that aren't needed during a given behavior. And therefore by disrupting everything that's not needed, resources are allocated to the brain areas that are needed. We discuss his work in the vein on attention - you may remember the episode with Carolyn Dicey-Jennings, and her ideas about how findings like Ole's are evidence we all have selves. We also talk about the role of alpha rhythms for working memory, for moving our eyes, and for previewing what we're about to look at before we move our eyes, and more broadly we discuss the role of oscillations in cognition in general, and of course what this might mean for developing better artificial intelligence.

• The Neuronal Oscillations Group.
• Twitter: @neuosc.
• Related papers
  • Shaping functional architecture by oscillatory alpha activity: gating by inhibition
  • FEF-Controlled Alpha Delay Activity Precedes Stimulus-Induced Gamma-Band Activity in Visual Cortex
  • The theta-gamma neural code
  • A pipelining mechanism supporting previewing during visual exploration and reading.
  • Specific lexico-semantic predictions are associated with unique spatial and temporal patterns of neural activity.

0:00 - Intro 2:58 - Oscillations import over the years 5:51 - Oscillations big picture 17:62 - Oscillations vs. traveling waves 22:00 - Oscillations and algorithms 28:53 - Alpha oscillations and working memory 44:46 - Alpha as the controller 48:55 - Frequency tagging 52:49 - Timing of attention 57:41 - Pipelining neural processing 1:03:38 - Previewing during reading 1:15:50 - Previewing, prediction, and large language models 1:24:27 - Dyslexia

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Chris Summerfield runs the Human Information Processing Lab at University of Oxford, and he's a research scientist at Deepmind. You may remember him from episode 95 with Sam Gershman, when we discussed ideas around the usefulness of neuroscience and psychology for AI. Since then, Chris has released his book, Natural General Intelligence: How understanding the brain can help us build AI. In the book, Chris makes the case that inspiration and communication between the cognitive sciences and AI is hindered by the different languages each field speaks. But in reality, there has always been and still is a lot of overlap and convergence about ideas of computation and intelligence, and he illustrates this using tons of historical and modern examples.

• Human Information Processing Lab.
• Twitter: @summerfieldlab.
• Book: Natural General Intelligence: How understanding the brain can help us build AI.
• Other books mentioned:
  • Are We Smart Enough to Know How Smart Animals Are? by Frans de Waal
  • The Mind is Flat by Nick Chater.

0:00 - Intro 2:20 - Natural General Intelligence 8:05 - AI and Neuro interaction 21:42 - How to build AI 25:54 - Umwelts and affordances 32:07 - Different kind of intelligence 39:16 - Ecological validity and AI 48:30 - Is reward enough? 1:05:14 - Beyond brains 1:15:10 - Large language models and brains

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Paul Rosenbloom is Professor Emeritus of Computer Science at the University of Southern California. In the early 1980s, Paul , along with John Laird and the early AI pioneer Alan Newell, developed one the earliest and best know cognitive architectures called SOAR. A cognitive architecture, as Paul defines it, is a model of the fixed structures and processes underlying minds, and in Paul's case the human mind. And SOAR was aimed at generating general intelligence. He doesn't work on SOAR any more, although SOAR is still alive and well in the hands of his old partner John Laird. He did go on to develop another cognitive architecture, called Sigma, and in the intervening years between those projects, among other things Paul stepped back and explored how our various scientific domains are related, and how computing itself should be considered a great scientific domain. That's in his book On Computing: The Fourth Great Scientific Domain.

He also helped develop the Common Model of Cognition, which isn't a cognitive architecture itself, but instead a theoretical model meant to generate consensus regarding the minimal components for a human-like mind. The idea is roughly to create a shared language and framework among cognitive architecture researchers, so the field can , so that whatever cognitive architecture you work on, you have a basis to compare it to, and can communicate effectively among your peers.

All of what I just said, and much of what we discuss, can be found in Paul's memoir, In Search of Insight: My Life as an Architectural Explorer.

• Paul's website.
• Related papers
  • Working memoir: In Search of Insight: My Life as an Architectural Explorer.
  • Book: On Computing: The Fourth Great Scientific Domain.
  • A Standard Model of the Mind: Toward a Common Computational Framework across Artificial Intelligence, Cognitive Science, Neuroscience, and Robotics.
  • Analysis of the human connectome data supports the notion of a “Common Model of Cognition” for human and human-like intelligence across domains.
  • Common Model of Cognition Bulletin.

0:00 - Intro 3:26 - A career of exploration 7:00 - Alan Newell 14:47 - Relational model and dichotomic maps 24:22 - Cognitive architectures 28:31 - SOAR cognitive architecture 41:14 - Sigma cognitive architecture 43:58 - SOAR vs. Sigma 53:06 - Cognitive architecture community 55:31 - Common model of cognition 1:11:13 - What's missing from the common model 1:17:48 - Brains vs. cognitive architectures 1:21:22 - Mapping the common model onto the brain 1:24:50 - Deep learning 1:30:23 - AGI

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Sarah Robins is a philosopher at the University of Kansas, one a growing handful of philosophers specializing in memory. Much of her work focuses on memory traces, which is roughly the idea that somehow our memories leave a trace in our minds. We discuss memory traces themselves and how they relate to the engram (see BI 126 Randy Gallistel: Where Is the Engram?, and BI 127 Tomás Ryan: Memory, Instinct, and Forgetting).

Psychology has divided memories into many categories - the taxonomy of memory. Sarah and I discuss how memory traces may cross-cut those categories, suggesting we may need to re-think our current ontology and taxonomy of memory.

We discuss a couple challenges to the idea of a stable memory trace in the brain. Neural dynamics is the notion that all our molecules and synapses are constantly changing and being recycled. Memory consolidation refers to the process of transferring our memory traces from an early unstable version to a more stable long-term version in a different part of the brain. Sarah thinks neither challenge poses a real threat to the idea

We also discuss the impact of optogenetics on the philosophy and neuroscience and memory, the debate about whether memory and imagination are essentially the same thing, whether memory's function is future oriented, and whether we want to build AI with our often faulty human-like memory or with perfect memory.

• Sarah's website.
• Twitter: @SarahKRobins.
• Related papers:
  • Her Memory chapter, with Felipe de Brigard, in the book Mind, Cognition, and Neuroscience: A Philosophical Introduction.
  • Memory and Optogenetic Intervention: Separating the engram from the ecphory.
  • Stable Engrams and Neural Dynamics.

0:00 - Intro 4:18 - Philosophy of memory 5:10 - Making a move 6:55 - State of philosophy of memory 11:19 - Memory traces or the engram 20:44 - Taxonomy of memory 25:50 - Cognitive ontologies, neuroscience, and psychology 29:39 - Optogenetics 33:48 - Memory traces vs. neural dynamics and consolidation 40:32 - What is the boundary of a memory? 43:00 - Process philosophy and memory 45:07 - Memory vs. imagination 49:40 - Constructivist view of memory and imagination 54:05 - Is memory for the future? 58:00 - Memory errors and intelligence 1:00:42 - Memory and AI 1:06:20 - Creativity and memory errors

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Mariam Aly runs the Aly lab at Columbia University, where she studies the interaction of memory, attention, and perception in brain regions like the hippocampus. The short story is that memory affects our perceptions, attention affects our memories, memories affect our attention, and these effects have signatures in neural activity measurements in our hippocampus and other brain areas. We discuss her experiments testing the nature of those interactions. We also discuss a particularly difficult stretch in Mariam's graduate school years, and how she now prioritizes her mental health.

• Aly Lab.
• Twitter: @mariam_s_aly.
• Related papers
  • Attention promotes episodic encoding by stabilizing hippocampal representations.
  • The medial temporal lobe is critical for spatial relational perception.
  • Cholinergic modulation of hippocampally mediated attention and perception.
  • Preparation for upcoming attentional states in the hippocampus and medial prefrontal cortex.
  • How hippocampal memory shapes, and is shaped by, attention.
  • Attentional fluctuations and the temporal organization of memory.

0:00 - Intro 3:50 - Mariam's background 9:32 - Hippocampus history and current science 12:34 - hippocampus and perception 13:42 - Relational information 18:30 - How much memory is explicit? 22:32 - How attention affects hippocampus 32:40 - fMRI levels vs. stability 39:04 - How is hippocampus necessary for attention 57:00 - How much does attention affect memory? 1:02:24 - How memory affects attention 1:06:50 - Attention and memory relation big picture 1:07:42 - Current state of memory and attention 1:12:12 - Modularity 1:17:52 - Practical advice to improve attention/memory 1:21:22 - Mariam's challenges

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Luiz Pessoa runs his Laboratory of Cognition and Emotion at the University of Maryland, College Park, where he studies how emotion and cognition interact. On this episode, we discuss many of the topics from his latest book, The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together, which is aimed at a general audience. The book argues we need to re-think how to study the brain. Traditionally, cognitive functions of the brain have been studied in a modular fashion: area X does function Y. However, modern research has revealed the brain is highly complex and carries out cognitive functions in a much more interactive and integrative fashion: a given cognitive function results from many areas and circuits temporarily coalescing (for similar ideas, see also BI 152 Michael L. Anderson: After Phrenology: Neural Reuse). Luiz and I discuss the implications of studying the brain from a complex systems perspective, why we need go beyond thinking about anatomy and instead think about functional organization, some of the brain's principles of organization, and a lot more.

• Laboratory of Cognition and Emotion.
• Twitter: @PessoaBrain.
• Book: The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together

0:00 - Intro 2:47 - The Entangled Brain 16:24 - How to think about complex systems 23:41 - Modularity thinking 28:16 - How to train one's mind to think complex 33:26 - Problem or principle? 44:22 - Complex behaviors 47:06 - Organization vs. structure 51:09 - Principles of organization: Massive Combinatorial Anatomical Connectivity 55:15 - Principles of organization: High Distributed Functional Connectivity 1:00:50 - Principles of organization: Networks as Functional Units 1:06:15 - Principles of Organization: Interactions via Cortical-Subcortical Loops 1:08:53 - Open and closed loops 1:16:43 - Principles of organization: Connectivity with the Body 1:21:28 - Consciousness 1:24:53 - Emotions 1:32:49 - Emottions and AI 1:39:47 - Emotion as a concept 1:43:25 - Complexity and functional organization in AI

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Anne Collins runs her  Computational Cognitive Neuroscience Lab at the University of California, Berkley One of the things she's been working on for years is how our working memory plays a role in learning as well, and specifically how working memory and reinforcement learning interact to affect how we learn, depending on the nature of what we're trying to learn. We discuss that interaction specifically. We also discuss more broadly how segregated and how overlapping and interacting our cognitive functions are, what that implies about our natural tendency to think in dichotomies - like MF vs MB-RL, system-1 vs system-2, etc., and we dive into plenty other subjects, like how to possibly incorporate these ideas into AI.

• Computational Cognitive Neuroscience Lab.
• Twitter: @ccnlab or @Anne_On_Tw.
• Related papers:
  • How Working Memory and Reinforcement Learning Are Intertwined: A Cognitive, Neural, and Computational Perspective. 
  • Beyond simple dichotomies in reinforcement learning.
  • The Role of Executive Function in Shaping Reinforcement Learning.
  • What do reinforcement learning models measure? Interpreting model parameters in cognition and neuroscience.

0:00 - Intro 5:25 - Dimensionality of learning 11:19 - Modularity of function and computations 16:51 - Is working memory a thing? 19:33 - Model-free model-based dichotomy 30:40 - Working memory and RL 44:43 - How working memory and RL interact 50:50 - Working memory and attention 59:37 - Computations vs. implementations 1:03:25 - Interpreting results 1:08:00 - Working memory and AI

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Carolyn Dicey Jennings is a philosopher and a cognitive scientist at University of California, Merced. In her book The Attending Mind, she lays out an attempt to unify the concept of attention. Carolyn defines attention roughly as the mental prioritization of some stuff over other stuff based on our collective interests. And one of her main claims is that attention is evidence of a real, emergent self or subject, that can't be reduced to microscopic brain activity. She does connect attention to more macroscopic brain activity, suggesting slow longer-range oscillations in our brains can alter or entrain the activity of more local neural activity, and this is a candidate for mental causation. We unpack that more in our discussion, and how Carolyn situates attention among other cognitive functions, like consciousness, action, and perception.

• Carolyn's website.
• Books:
  • The Attending Mind.
• Aeon article:
  • I Attend, Therefore I Am.
• Related papers
  • The Subject of Attention.
  • Consciousness and Mind.
  • Practical Realism about the Self.

0:00 - Intro 12:15 - Reconceptualizing attention 16:07 - Types of attention 19:02 - Predictive processing and attention 23:19 - Consciousness, identity, and self 30:39 - Attention and the brain 35:47 - Integrated information theory 42:05 - Neural attention 52:08 - Decoupling oscillations from spikes 57:16 - Selves in other organisms 1:00:42 - AI and the self 1:04:43 - Attention, consciousness, conscious perception 1:08:36 - Meaning and attention 1:11:12 - Conscious entrainment 1:19:57 - Is attention a switch or knob?

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Michael L. Anderson is a professor at the Rotman Institute of Philosophy, at Western University. His book, After Phrenology: Neural Reuse and the Interactive Brain, calls for a re-conceptualization of how we understand and study brains and minds. Neural reuse is the phenomenon that any given brain area is active for multiple cognitive functions, and partners with different sets of brain areas to carry out different cognitive functions. We discuss the implications for this, and other topics in Michael's research and the book, like evolution, embodied cognition, and Gibsonian perception. Michael also fields guest questions from John Krakauer and Alex Gomez-Marin, about representations and metaphysics, respectively.

• Michael's website.
• Twitter: @mljanderson.
• Book:
  • After Phrenology: Neural Reuse and the Interactive Brain.
• Related papers
  • Neural reuse: a fundamental organizational principle of the brain.
  • Some dilemmas for an account of neural representation: A reply to Poldrack.
  • Debt-free intelligence: Ecological information in minds and machines
  • Describing functional diversity of brain regions and brain networks.

0:00 - Intro 3:02 - After Phrenology 13:18 - Typical neuroscience experiment 16:29 - Neural reuse 18:37 - 4E cognition and representations 22:48 - John Krakauer question 27:38 - Gibsonian perception 36:17 - Autoencoders without representations 49:22 - Pluralism 52:42 - Alex Gomez-Marin question - metaphysics 1:01:26 - Stimulus-response historical neuroscience 1:10:59 - After Phrenology influence 1:19:24 - Origins of neural reuse 1:35:25 - The way forward

Steve Byrnes is a physicist turned AGI safety researcher. He's concerned that when we create AGI, whenever and however that might happen, we run the risk of creating it in a less than perfectly safe way. AGI safety (AGI not doing something bad) is a wide net that encompasses AGI alignment (AGI doing what we want it to do). We discuss a host of ideas Steve writes about in his Intro to Brain-Like-AGI Safety blog series, which uses what he has learned about brains to address how we might safely make AGI.

• Steve's website.
• Twitter: @steve47285
• Intro to Brain-Like-AGI Safety.

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Dan Nicholson is a philosopher at George Mason University. He incorporates the history of science and philosophy into modern analyses of our conceptions of processes related to life and organisms. He is also interested in re-orienting our conception of the universe as made fundamentally of things/substances, and replacing it with the idea the universe is made fundamentally of processes (process philosophy). In this episode, we both of those subjects, the why the "machine conception of the organism" is incorrect, how to apply these ideas to topics like neuroscience and artificial intelligence, and much more.

• Dan's website. Google Scholar.
• Twitter: @NicholsonHPBio
• Book
  • Everything Flows: Towards a Processual Philosophy of Biology.
• Related papers
  • Is the Cell Really a Machine?
  • The Machine Conception of the Organism in Development and Evolution: A Critical Analysis.
  • On Being the Right Size, Revisited: The Problem with Engineering Metaphors in Molecular Biology.
• Related episode: BI 118 Johannes Jäger: Beyond Networks.

0:00 - Intro 2:49 - Philosophy and science 16:37 - Role of history 23:28 - What Is Life? And interaction with James Watson 38:37 - Arguments against the machine conception of organisms 49:08 - Organisms as streams (processes) 57:52 - Process philosophy 1:08:59 - Alfred North Whitehead 1:12:45 - Process and consciousness 1:22:16 - Artificial intelligence and process 1:31:47 - Language and symbols and processes

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William B. Miller is an ex-physician turned evolutionary biologist. In this episode, we discuss topics related to his new book, Bioverse: How the Cellular World Contains the Secrets to Life's Biggest Questions. The premise of the book is that all individual cells are intelligent in their own right, and possess a sense of self. From this, Bill makes the case that cells cooperate with other cells to engineer whole organisms that in turn serve as wonderful hosts for the myriad cell types. Further, our bodies are collections of our own cells (with our DNA), and an enormous amount and diversity of foreign cells - our microbiome - that communicate and cooperate with each other and with our own cells. We also discuss how cell intelligence compares to human intelligence, what Bill calls the "era of the cell" in science, how the future of medicine will harness the intelligence of cells and their cooperative nature, and much more.

• William's website.
• Twitter: @BillMillerMD.
• Book: Bioverse: How the Cellular World Contains the Secrets to Life's Biggest Questions.

0:00 - Intro 3:43 - Bioverse 7:29 - Bill's cell appreciation origins 17:03 - Microbiomes 27:01 - Complexity of microbiomes and the "Era of the cell" 46:00 - Robustness 55:05 - Cell vs. human intelligence 1:10:08 - Artificial intelligence 1:21:01 - Neuro-AI 1:25:53 - Hard problem of consciousness

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Gaute Einevoll is a professor at the University of Oslo and Norwegian University of Life Sciences. Use develops detailed models of brain networks to use as simulations, so neuroscientists can test their various theories and hypotheses about how networks implement various functions. Thus, the models are tools. The goal is to create models that are multi-level, to test questions at various levels of biological detail; and multi-modal, to predict that handful of signals neuroscientists measure from real brains (something Gaute calls "measurement physics"). We also discuss Gaute's thoughts on Carina Curto's "beautiful vs ugly models", and his reaction to Noah Hutton's In Silico documentary about the Blue Brain and Human Brain projects (Gaute has been funded by the Human Brain Project since its inception).

• Gaute's website.
• Twitter: @GauteEinevoll.
• Related papers:
  • The Scientific Case for Brain Simulations.
  • Brain signal predictions from multi-scale networks using a linearized framework.
  • Uncovering circuit mechanisms of current sinks and sources with biophysical simulations of primary visual cortex
• LFPy: a Python module for calculation of extracellular potentials from multicompartment neuron models.
• Gaute's Sense and Science podcast.

0:00 - Intro 3:25 - Beautiful and messy models 6:34 - In Silico 9:47 - Goals of human brain project 15:50 - Brain simulation approach 21:35 - Degeneracy in parameters 26:24 - Abstract principles from simulations 32:58 - Models as tools 35:34 - Predicting brain signals 41:45 - LFPs closer to average 53:57 - Plasticity in simulations 56:53 - How detailed should we model neurons? 59:09 - Lessons from predicting signals 1:06:07 - Scaling up 1:10:54 - Simulation as a tool 1:12:35 - Oscillations 1:16:24 - Manifolds and simulations 1:20:22 - Modeling cortex like Hodgkin and Huxley

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Noah Hutton writes, directs, and scores documentary and narrative films. On this episode, we discuss his documentary In Silico. In 2009, Noah watched a TED talk by Henry Markram, in which Henry claimed it would take 10 years to fully simulate a human brain. This claim inspired Noah to chronicle the project, visiting Henry and his team periodically throughout. The result was In Silico, which tells the science, human, and social story of Henry's massively funded projects - the Blue Brain Project and the Human Brain Project.

• In Silico website.
  • Rent or buy In Silico.
• Noah's website.
• Twitter: @noah_hutton.

0:00 - Intro 3:36 - Release and premier 7:37 - Noah's background 9:52 - Origins of In Silico 19:39 - Recurring visits 22:13 - Including the critics 25:22 - Markram's shifting outlook and salesmanship 35:43 - Promises and delivery 41:28 - Computer and brain terms interchange 49:22 - Progress vs. illusion of progress 52:19 - Close to quitting 58:01 - Salesmanship vs bad at estimating timelines 1:02:12 - Brain simulation science 1:11:19 - AGI 1:14:48 - Brain simulation vs. neuro-AI 1:21:03 - Opinion on TED talks 1:25:16 - Hero worship 1:29:03 - Feedback on In Silico

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Lauren Ross is an Associate Professor at the University of California, Irvine. She studies and writes about causal and non-causal explanations in philosophy of science, including distinctions among causal structures. Throughout her work, Lauren employs Jame's Woodward's interventionist approach to causation, which Jim and I discussed in episode 145. In this episode, we discuss Jim's lasting impact on the philosophy of causation, the current dominance of mechanistic explanation and its relation to causation, and various causal structures of explanation, including pathways, cascades, topology, and constraints.

• Lauren's website.
• Twitter: @ProfLaurenRoss
• Related papers
  • A call for more clarity around causality in neuroscience.
  • The explanatory nature of constraints: Law-based, mathematical, and causal.
  • Causal Concepts in Biology: How Pathways Differ from Mechanisms and Why It Matters.
  • Distinguishing topological and causal explanation.
  • Multiple Realizability from a Causal Perspective.
  • Cascade versus mechanism: The diversity of causal structure in science.

0:00 - Intro 2:46 - Lauren's background 10:14 - Jim Woodward legacy 15:37 - Golden era of causality 18:56 - Mechanistic explanation 28:51 - Pathways 31:41 - Cascades 36:25 - Topology 41:17 - Constraint 50:44 - Hierarchy of explanations 53:18 - Structure and function 57:49 - Brain and mind 1:01:28 - Reductionism 1:07:58 - Constraint again 1:14:38 - Multiple realizability

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James Woodward is a recently retired Professor from the Department of History and Philosophy of Science at the University of Pittsburgh. Jim has tremendously influenced the field of causal explanation in the philosophy of science. His account of causation centers around intervention - intervening on a cause should alter its effect. From this minimal notion, Jim has described many facets and varieties of causal structures. In this episode, we discuss topics from his recent book, Causation with a Human Face: Normative Theory and Descriptive Psychology. In the book, Jim advocates that how we should think about causality - the normative - needs to be studied together with how we actually do think about causal relations in the world - the descriptive. We discuss many topics around this central notion, epistemology versus metaphysics, the the nature and varieties of causal structures.

• Jim's website.
• Making Things Happen: A Theory of Causal Explanation.
• Causation with a Human Face: Normative Theory and Descriptive Psychology.

0:00 - Intro 4:14 - Causation with a Human Face & Functionalist approach 6:16 - Interventionist causality; Epistemology and metaphysics 9:35 - Normative and descriptive 14:02 - Rationalist approach 20:24 - Normative vs. descriptive 28:00 - Varying notions of causation 33:18 - Invariance 41:05 - Causality in complex systems 47:09 - Downward causation 51:14 - Natural laws 56:38 - Proportionality 1:01:12 - Intuitions 1:10:59 - Normative and descriptive relation 1:17:33 - Causality across disciplines 1:21:26 - What would help our understanding of causation

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Large language models, often now called "foundation models", are the model de jour in AI, based on the transformer architecture. In this episode, I bring together Evelina Fedorenko and Emily M. Bender to discuss how language models stack up to our own language processing and generation (models and brains both excel at next-word prediction), whether language evolved in humans for complex thoughts or for communication (communication, says Ev), whether language models grasp the meaning of the text they produce (Emily says no), and much more.

Evelina Fedorenko is a cognitive scientist who runs the EvLab at MIT. She studies the neural basis of language. Her lab has amassed a large amount of data suggesting language did not evolve to help us think complex thoughts, as Noam Chomsky has argued, but rather for efficient communication. She has also recently been comparing the activity in language models to activity in our brain's language network, finding commonality in the ability to predict upcoming words.

Emily M. Bender is a computational linguist at University of Washington. Recently she has been considering questions about whether language models understand the meaning of the language they produce (no), whether we should be scaling language models as is the current practice (not really), how linguistics can inform language models, and more.

• EvLab.
• Emily's website.
• Twitter: @ev_fedorenko; @emilymbender.
• Related papers
  • Language and thought are not the same thing: Evidence from neuroimaging and neurological patients. (Fedorenko)
  • The neural architecture of language: Integrative modeling converges on predictive processing. (Fedorenko)
  • On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? (Bender)
  • Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data (Bender)

0:00 - Intro 4:35 - Language and cognition 15:38 - Grasping for meaning 21:32 - Are large language models producing language? 23:09 - Next-word prediction in brains and models 32:09 - Interface between language and thought 35:18 - Studying language in nonhuman animals 41:54 - Do we understand language enough? 45:51 - What do language models need? 51:45 - Are LLMs teaching us about language? 54:56 - Is meaning necessary, and does it matter how we learn language? 1:00:04 - Is our biology important for language? 1:04:59 - Future outlook

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Rodolphe Sepulchre is a control engineer and theorist at Cambridge University. He focuses on applying feedback control engineering principles to build circuits that model neurons and neuronal circuits. We discuss his work on mixed feedback control - positive and negative - as an underlying principle of the mixed digital and analog brain signals,, the role of neuromodulation as a controller, applying these principles to Eve Marder's lobster/crab neural circuits, building mixed-feedback neuromorphics, some feedback control history, and how "If you wish to contribute original work, be prepared to face loneliness," among other topics.

• Rodolphe's website.
• Related papers
  • Spiking Control Systems.
  • Control Across Scales by Positive and Negative Feedback.
  • Neuromorphic control. (arXiv version)
• Related episodes:
  • BI 130 Eve Marder: Modulation of Networks
  • BI 119 Henry Yin: The Crisis in Neuroscience

0:00 - Intro 4:38 - Control engineer 9:52 - Control vs. dynamical systems 13:34 - Building vs. understanding 17:38 - Mixed feedback signals 26:00 - Robustness 28:28 - Eve Marder 32:00 - Loneliness 37:35 - Across levels 44:04 - Neuromorphics and neuromodulation 52:15 - Barrier to adopting neuromorphics 54:40 - Deep learning influence 58:04 - Beyond energy efficiency 1:02:02 - Deep learning for neuro 1:14:15 - Role of philosophy 1:16:43 - Doing it right

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Cameron Buckner is a philosopher and cognitive scientist at The University of Houston. He is writing a book about the age-old philosophical debate on how much of our knowledge is innate (nature, rationalism) versus how much is learned (nurture, empiricism). In the book and his other works, Cameron argues that modern AI can help settle the debate. In particular, he suggests we focus on what types of psychological "domain-general faculties" underlie our own intelligence, and how different kinds of deep learning models are revealing how those faculties may be implemented in our brains. The hope is that by building systems that possess the right handful of faculties, and putting those systems together in a way they can cooperate in a general and flexible manner, it will result in cognitive architectures we would call intelligent. Thus, what Cameron calls The New DoGMA: Domain-General Modular Architecture. We also discuss his work on mental representation and how representations get their content - how our thoughts connect to the natural external world. 

• Cameron's Website.
• Twitter: @cameronjbuckner.
• Related papers
  • Empiricism without Magic: Transformational Abstraction in Deep Convolutional Neural Networks.
  • A Forward-Looking Theory of Content.
• Other sources Cameron mentions:
  • Innateness, AlphaZero, and Artificial Intelligence (Gary Marcus).
  • Radical Empiricism and Machine Learning Research (Judea Pearl).
  • Fodor’s guide to the Humean mind (Tamás Demeter).

0:00 - Intro 4:55 - Interpreting old philosophy 8:26 - AI and philosophy 17:00 - Empiricism vs. rationalism 27:09 - Domain-general faculties 33:10 - Faculty psychology 40:28 - New faculties? 46:11 - Human faculties 51:15 - Cognitive architectures 56:26 - Language 1:01:40 - Beyond dichotomous thinking 1:04:08 - Lower-level faculties 1:10:16 - Animal cognition 1:14:31 - A Forward-Looking Theory of Content

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Carina Curto is a professor in the Department of Mathematics at The Pennsylvania State University. She uses her background skills in mathematical physics/string theory to study networks of neurons. On this episode, we discuss the world of topology in neuroscience - the study of the geometrical structures mapped out by active populations of neurons. We also discuss her work on "combinatorial linear threshold networks" (CLTNs). Unlike the large deep learning models popular today as models of brain activity, the CLTNs Carina builds are relatively simple, abstracted graphical models. This property is important to Carina, whose goal is to develop mathematically tractable neural network models. Carina has worked out how the structure of many CLTNs allows prediction of the model's allowable dynamics, how motifs of model structure can be embedded in larger models while retaining their dynamical features, and more. The hope is that these elegant models can tell us more about the principles our messy brains employ to generate the robust and beautiful dynamics underlying our cognition.

• Carina's website.
• The Mathematical Neuroscience Lab.
• Related papers
  • A major obstacle impeding progress in brain science is the lack of beautiful models.
  • What can topology tells us about the neural code?
  • Predicting neural network dynamics via graphical analysis

0:00 - Intro 4:25 - Background: Physics and math to study brains 20:45 - Beautiful and ugly models 35:40 - Topology 43:14 - Topology in hippocampal navigation 56:04 - Topology vs. dynamical systems theory 59:10 - Combinatorial linear threshold networks 1:25:26 - How much more math do we need to invent?

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Jeff Schall is the director of the Center for Visual Neurophysiology at York University, where he runs the Schall Lab. His research centers around studying the mechanisms of our decisions, choices, movement control, and attention within the saccadic eye movement brain systems and in mathematical psychology models- in other words, how we decide where and when to look. Jeff was my postdoctoral advisor at Vanderbilt University, and I wanted to revisit a few guiding principles he instills in all his students. Linking Propositions by Davida Teller are a series of logical statements to ensure we rigorously connect the brain activity we record to the psychological functions we want to explain. Strong Inference by John Platt is the scientific method on steroids - a way to make our scientific practice most productive and efficient. We discuss both of these topics in the context of Jeff's eye movement and decision-making science. We also discuss how neurophysiology has changed over the past 30 years, we compare the relatively small models he employs with the huge deep learning models, many of his current projects, and plenty more. If you want to learn more about Jeff's work and approach, I recommend reading in order two of his review papers we discuss as well. One was written 20 years ago (On Building a Bridge Between Brain and Behavior), and the other 2-ish years ago (Accumulators, Neurons, and Response Time).

• Schall Lab.
• Twitter: @LabSchall.
• Related papers
  • Linking Propositions.
  • Strong Inference.
  • On Building a Bridge Between Brain and Behavior.
  • Accumulators, Neurons, and Response Time.

0:00 - Intro 6:51 - Neurophysiology old and new 14:50 - Linking propositions 24:18 - Psychology working with neurophysiology 35:40 - Neuron doctrine, population doctrine 40:28 - Strong Inference and deep learning 46:37 - Model mimicry 51:56 - Scientific fads 57:07 - Current projects 1:06:38 - On leaving academia 1:13:51 - How academia has changed for better and worse

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Marc Howard runs his Theoretical Cognitive Neuroscience Lab at Boston University, where he develops mathematical models of cognition, constrained by psychological and neural data. In this episode, we discuss the idea that a Laplace transform and its inverse may serve as a unified framework for memory. In short, our memories are compressed on a continuous log-scale: as memories get older, their representations "spread out" in time. It turns out this kind of representation seems ubiquitous in the brain and across cognitive functions, suggesting it is likely a canonical computation our brains use to represent a wide variety of cognitive functions. We also discuss some of the ways Marc is incorporating this mathematical operation in deep learning nets to improve their ability to handle information at different time scales.

• Theoretical Cognitive Neuroscience Lab. 
• Twitter: @marcwhoward777.
• Related papers:
  • Memory as perception of the past: Compressed time in mind and brain.
  • Formal models of memory based on temporally-varying representations.
  • Cognitive computation using neural representations of time and space in the Laplace domain.
  • Time as a continuous dimension in natural and artificial networks.
  • DeepSITH: Efficient learning via decomposition of what and when across time scales.

0:00 - Intro 4:57 - Main idea: Laplace transforms 12:00 - Time cells 20:08 - Laplace, compression, and time cells 25:34 - Everywhere in the brain 29:28 - Episodic memory 35:11 - Randy Gallistel's memory idea 40:37 - Adding Laplace to deep nets 48:04 - Reinforcement learning 1:00:52 - Brad Wyble Q: What gets filtered out? 1:05:38 - Replay and complementary learning systems 1:11:52 - Howard Goldowsky Q: Gyorgy Buzsaki 1:15:10 - Obstacles

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Matthew Larkum runs his lab at Humboldt University of Berlin, where his group studies how dendrites contribute to  computations within and across layers of the neocortex. Since the late 1990s, Matthew has continued to uncover key properties of the way pyramidal neurons stretch across layers of the cortex, their dendrites receiving inputs from those different layers - and thus different brain areas. For example, layer 5 pyramidal neurons have a set of basal dendrites near the cell body that receives feedforward-like input, and a set of apical dendrites all the way up in layer 1 that receives feedback--like input. Depending on which set of dendrites is receiving input, or neither or both, the neuron's output functions in different modes- silent, regular spiking, or burst spiking. Matthew realized the different sets of dendritic inputs could signal different operations, often pairing feedforward sensory--like signals and feedback context-like signals. His research has shown this kind of coincidence detection is important for cognitive functions like perception, memory, learning, and even wakefulness. We discuss many of his ideas and research findings, why dendrites have long been neglected in favor of neuron cell bodies, the possibility of learning about computations by studying implementation-level phenomena, and much more.

• Larkum Lab.
• Twitter: @mattlark.
• Related papers
  • Cellular Mechanisms of Conscious Processing.
  • Perirhinal input to neocortical layer 1 controls learning. (bioRxiv link: https://www.biorxiv.org/content/10.1101/713883v1)
  • Are dendrites conceptually useful?
  • Memories off the top of your head.
  • Do Action Potentials Cause Consciousness?
• Blake Richard's episode discussing back-propagation in the brain (based on Matthew's experiments)

0:00 - Intro 5:31 - Background: Dendrites 23:20 - Cortical neuron bodies vs. branches 25:47 - Theories of cortex 30:49 - Feedforward and feedback hierarchy 37:40 - Dendritic integration hypothesis 44:32 - DIT vs. other consciousness theories 51:30 - Mac Shine Q1 1:04:38 - Are dendrites conceptually useful? 1:09:15 - Insights from implementation level 1:24:44 - How detailed to model? 1:28:15 - Do action potentials cause consciousness? 1:40:33 - Mac Shine Q2

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Brian Butterworth is Emeritus Professor of Cognitive Neuropsychology at University College London. In his book, Can Fish Count?: What Animals Reveal About Our Uniquely Mathematical Minds, he describes the counting and numerical abilities across many different species, suggesting our ability to count is evolutionarily very old (since many diverse species can count). We discuss many of the examples in his book, the mathematical disability dyscalculia and its relation to dyslexia, how to test counting abilities in various species, how counting may happen in brains, the promise of creating artificial networks that can do math, and many more topics.

• Brian's website: The Mathematical Brain
• Twitter: @b_butterworth
• The book:
  • Can Fish Count?: What Animals Reveal About Our Uniquely Mathematical Minds

0:00 - Intro 3:19 - Why Counting? 5:31 - Dyscalculia 12:06 - Dyslexia 19:12 - Counting 26:37 - Origins of counting vs. language 34:48 - Counting vs. higher math 46:46 - Counting some things and not others 53:33 - How to test counting 1:03:30 - How does the brain count? 1:13:10 - Are numbers real?

Check out my short video series about what's missing in AI and Neuroscience.

Michel Bitbol is Director of Research at CNRS (Centre National de la Recherche Scientifique). Alex Gomez-Marin is a neuroscientist running his lab, The Behavior of Organisms Laboratory, at the Instituto de Neurociencias in Alicante. We discuss phenomenology as an alternative perspective on our scientific endeavors. Although we like to believe our science is objective and explains the reality of the world we inhabit, we can't escape the fact that all of our scientific knowledge comes through our perceptions and interpretations as conscious living beings. Michel has used phenomenology to resolve many of the paradoxes that quantum mechanics generates when it is understood as a description of reality, and more recently he has applied phenomenology to the philosophy of mind and consciousness. Alex is currently trying to apply the phenomenological approach to his research on brains and behavior. Much of our conversation revolves around how phenomenology and our "normal" scientific explorations can co-exist, including the study of minds, brains, and intelligence- our own and that of other organisms. We also discuss the "blind spot" of science, the history and practice of phenomenology, various kinds of explanation, the language we use to describe things, and more.

• Michel's website
• Alex's Lab: The Behavior of Organisms Laboratory.
• Twitter: @behaviOrganisms (Alex)
• Related papers
  • The Blind Spot of Neuroscience  
  • The Life of Behavior
  • A Clash of Umwelts 
• Related events:
  • The Future Scientist (a conversation series)

0:00 - Intro 4:32 - The Blind Spot 15:53 - Phenomenology and interpretation 22:51 - Personal stories: appreciating phenomenology 37:42 - Quantum physics example 47:16 - Scientific explanation vs. phenomenological description 59:39 - How can phenomenology and science complement each other? 1:08:22 - Neurophenomenology 1:17:34 - Use of language 1:25:46 - Mutual constraints

Check out my short video series about what's missing in AI and Neuroscience.

Brains are often conceived as consisting of neurons and "everything else." As Elena discusses, the "everything else," including glial cells and in particular astrocytes, have largely been ignored in neuroscience. That's partly because the fast action potentials of neurons have been assumed to underlie computations in the brain, and because technology only recently afforded closer scrutiny of astrocyte activity. Now that we can record calcium signaling in astrocytes, it's possible to relate how astrocyte signaling with each other and with neurons may complement the cognitive roles once thought the sole domain of neurons. Although the computational role of astrocytes remains unclear, it is clear that astrocytes interact with neurons and neural circuits in dynamic and interesting ways. We talk about the historical story of astrocytes, the emerging modern story, and Elena shares her views on the path forward to understand astrocyte function in cognition, disease, homeostasis, and - Elena's favorite current hypothesis - their integrative role in negative feedback control.

• Elena's website.
• Twitter: @elenagalea1
• Related papers
  • A roadmap to integrate astrocytes into Systems Neuroscience.
  • Elena recommended this paper: Biological feedback control—Respect the loops.

0:00 - Intro 5:23 - The changing story of astrocytes 14:58 - Astrocyte research lags neuroscience 19:45 - Types of astrocytes 23:06 - Astrocytes vs neurons 26:08 - Computational roles of astrocytes 35:45 - Feedback control 43:37 - Energy efficiency 46:25 - Current technology 52:58 - Computational astroscience 1:10:57 - Do names for things matter

Check out my short video series about what's missing in AI and Neuroscience.

Srini is Emeritus Professor at Queensland Brain Institute in Australia. In this episode, he shares his wide range of behavioral experiments elucidating the principles of flight and navigation in insects. We discuss how bees use optic flow signals to determine their speed, distance, proximity to objects, and to gracefully land. These abilities are largely governed via control systems, balancing incoming perceptual signals with internal reference signals. We also talk about a few of the aerial robotics projects his research has inspired, many of the other cognitive skills bees can learn, the possibility of their feeling pain , and the nature of their possible subjective conscious experience.

• Srini's Website.
• Related papers
  • Vision, perception, navigation and 'cognition' in honeybees and applications to aerial robotics.

0:00 - Intro 3:34 - Background 8:20 - Bee experiments 14:30 - Bee flight and navigation 28:05 - Landing 33:06 - Umwelt and perception 37:26 - Bee-inspired aerial robotics 49:10 - Motion camouflage 51:52 - Cognition in bees 1:03:10 - Small vs. big brains 1:06:42 - Pain in bees 1:12:50 - Subjective experience 1:15:25 - Deep learning 1:23:00 - Path forward

Check out my short video series about what's missing in AI and Neuroscience.

Ken discusses the recent work in his lab that allows communication with subjects while they experience lucid dreams. This new paradigm opens many avenues to study the neuroscience and psychology of consciousness, sleep, dreams, memory, and learning, and to improve and optimize sleep for cognition. Ken and his team are developing a Lucid Dreaming App which is freely available via his lab. We also discuss much of his work on memory and learning in general and specifically related to sleep, like reactivating specific memories during sleep to improve learning.

• Ken's Cognitive Neuroscience Laboratory.
• Twitter: @kap101.
• The Lucid Dreaming App.
• Related papers
  • Memory and Sleep: How Sleep Cognition Can Change the Waking Mind for the Better.
  • Does memory reactivation during sleep support generalization at the cost of memory specifics?
  • Real-time dialogue between experimenters and dreamers during REM sleep.

0:00 - Intro 2:48 - Background and types of memory 14:44 -Consciousness and memory 23:32 - Phases and sleep and wakefulness 28:19 - Sleep, memory, and learning 33:50 - Targeted memory reactivation 48:34 - Problem solving during sleep 51:50 - 2-way communication with lucid dreamers 1:01:43 - Confounds to the paradigm 1:04:50 - Limitations and future studies 1:09:35 - Lucid dreaming app 1:13:47 - How sleep can inform AI 1:20:18 - Advice for students

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Ila discusses her theoretical neuroscience work suggesting how our memories are formed within the cognitive maps we use to navigate the world and navigate our thoughts. The main idea is that grid cell networks in the entorhinal cortex internally generate a structured scaffold, which gets sent to the hippocampus. Neurons in the hippocampus, like the well-known place cells, receive that scaffolding and also receive external signals from the neocortex- signals about what's happening in the world and in our thoughts. Thus, the place cells act to "pin" what's happening in our neocortex to the scaffold, forming a memory. We also discuss her background as a physicist and her approach as a "neurophysicist", and a review she's publishing all about the many brain areas and cognitive functions being explained as attractor landscapes within a dynamical systems framework.

• The Fiete Lab.
• Related papers
  • A structured scaffold underlies activity in the hippocampus.
  • Attractor and integrator networks in the brain.

0:00 - Intro 3:36 - "Neurophysicist" 9:30 - Bottom-up vs. top-down 15:57 - Tool scavenging 18:21 - Cognitive maps and hippocampus 22:40 - Hopfield networks 27:56 - Internal scaffold 38:42 - Place cells 43:44 - Grid cells 54:22 - Grid cells encoding place cells 59:39 - Scaffold model: stacked hopfield networks 1:05:39 - Attractor landscapes 1:09:22 - Landscapes across scales 1:12:27 - Dimensionality of landscapes

Sri and Mei join me to discuss how including principles of neuromodulation in deep learning networks may improve network performance. It's an ever-present question how much detail to include in models, and we are in the early stages of learning how neuromodulators and their interactions shape biological brain function. But as we continue to learn more, Sri and Mei are interested in building "neuromodulation-aware DNNs".

• Neural Circuits Laboratory.
• Twitter: Sri: @srikipedia; Jie: @neuro_Mei.
• Related papers
  • Informing deep neural networks by multiscale principles of neuromodulatory systems.

0:00 - Intro 3:10 - Background 9:19 - Bottom-up vs. top-down 14:42 - Levels of abstraction 22:46 - Biological neuromodulation 33:18 - Inventing neuromodulators 41:10 - How far along are we? 53:31 - Multiple realizability 1:09:40 -Modeling dendrites 1:15:24 - Across-species neuromodulation

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Eve discusses many of the lessons she has learned studying a small nervous system, the crustacean stomatogastric nervous system (STG). The STG has only about 30 neurons and its connections and neurophysiology are well-understood. Yet Eve's work has shown it functions under a remarkable diversity of conditions, and does so is a remarkable variety of ways. We discuss her work on the STG specifically, and what her work implies about trying to study much larger nervous systems, like our human brains.

• The Marder Lab.
• Twitter: @MarderLab.
• Related to our conversation:
  • Understanding Brains: Details, Intuition, and Big Data.
  • Emerging principles governing the operation of neural networks (Eve mentions this regarding "building blocks" of neural networks).

0:00 - Intro 3:58 - Background 8:00 - Levels of ambiguity 9:47 - Stomatogastric nervous system 17:13 - Structure vs. function 26:08 - Role of theory 34:56 - Technology vs. understanding 38:25 - Higher cognitive function 44:35 - Adaptability, resilience, evolution 50:23 - Climate change 56:11 - Deep learning 57:12 - Dynamical systems

Patryk and I discuss his wide-ranging background working in both the neuroscience and AI worlds, and his resultant perspective on what's needed to move forward in AI, including some principles of brain processes that are more and less important. We also discuss his own work using some of those principles to help deep learning generalize to better capture how humans behave in and perceive the world.

• Patryk's homepage.
• Twitter: @paklnet.
• Related papers
  • Unsupervised Learning from Continuous Video in a Scalable Predictive Recurrent Network.

0:00 - Intro 2:22 - Patryk's background 8:37 - Importance of diverse skills 16:14 - What is intelligence? 20:34 - Important brain principles 22:36 - Learning from the real world 35:09 - Language models 42:51 - AI contribution to neuroscience 48:22 - Criteria for "real" AI 53:11 - Neuroscience for AI 1:01:20 - What can we ignore about brains? 1:11:45 - Advice to past self

Hakwan and I discuss many of the topics in his new book, In Consciousness we Trust: The Cognitive Neuroscience of Subjective Experience. Hakwan describes his perceptual reality monitoring theory of consciousness, which suggests consciousness may act as a systems check between our sensory perceptions and higher cognitive functions. We also discuss his latest thoughts on mental quality space and how it relates to perceptual reality monitoring. Among many other topics, we chat about the many confounds and challenges to empirically studying consciousness, a topic featured heavily in the first half of his book. Hakwan was on a previous episode with Steve Fleming, BI 099 Hakwan Lau and Steve Fleming: Neuro-AI Consciousness.

• Hakwan's lab: Consciousness and Metacognition Lab.
• Twitter: @hakwanlau.
• Book:
  • In Consciousness we Trust: The Cognitive Neuroscience of Subjective Experience.

0:00 - Intro 4:37 - In Consciousness We Trust 12:19 - Too many consciousness theories? 19:26 - Philosophy and neuroscience of consciousness 29:00 - Local vs. global theories 31:20 - Perceptual reality monitoring and GANs 42:43 - Functions of consciousness 47:17 - Mental quality space 56:44 - Cognitive maps 1:06:28 - Performance capacity confounds 1:12:28 - Blindsight 1:19:11 - Philosophy vs. empirical work

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Tomás and I discuss his research and ideas on how memories are encoded (the engram), the role of forgetting, and the overlapping mechanisms of memory and instinct. Tomás uses otpogenetics and other techniques to label and control neurons involved in learning and memory, and has shown that forgotten memories can be restored by stimulating “engram cells” originally associated with the forgotten memory. This line of research has led Tomás to think forgetting might be a learning mechanism itself, a adaption our brains make based on the predictability and affordances of the environment. His work on engrams has also led Tomás to think our instincts (ingrams) may share the same mechanism of our memories (engrams), and that memories may transition to instincts across generations. We begin by addressing Randy Gallistel’s engram ideas from the previous episode: BI 126 Randy Gallistel: Where Is the Engram?

• Ryan Lab.
• Twitter: @TJRyan_77.
• Related papers
  • Engram cell connectivity: an evolving substrate for information storage.
  • Forgetting as a form of adaptive engram cell plasticity.
  • Memory and Instinct as a Continuum of Information Storage in The Cognitive Neurosciences.
  • The Bandwagon by Claude Shannon.

0:00 – Intro
4:05 – Response to Randy Gallistel
10:45 – Computation in the brain
14:52 – Instinct and memory
19:37 – Dynamics of memory
21:55 – Wiring vs. connection strength plasticity
24:16 – Changing one’s mind
33:09 – Optogenetics and memory experiments
47:24 – Forgetting as learning
1:06:35 – Folk psychological terms
1:08:49 – Memory becoming instinct
1:21:49 – Instinct across the lifetime
1:25:52 – Boundaries of memories
1:28:52 – Subjective experience of memory
1:31:58 – Interdisciplinary research
1:37:32 – Communicating science

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Randy and I discuss his long-standing interest in how the brain stores information to compute. That is, where is the engram, the physical trace of memory in the brain? Modern neuroscience is dominated by the view that memories are stored among synaptic connections in populations of neurons. Randy believes a more reasonable and reliable way to store abstract symbols, like numbers, is to write them into code within individual neurons. Thus, the spiking code, whatever it is, functions to write and read memories into and out of intracellular substrates, like polynucleotides (DNA, RNA, e.g.). He lays out his case in detail in his book with Adam King, Memory and the Computational Brain: Why Cognitive Science will Transform Neuroscience. We also talk about some research and theoretical work since then that support his views.

• Randy’s Rutger’s website.
• Book:
  • Memory and the Computational Brain: Why Cognitive Science will Transform Neuroscience.
• Related papers:
  • The theoretical RNA paper Randy mentions: An RNA-based theory of natural universal computation.
  • Evidence for intracellular engram in cerebellum: Memory trace and timing mechanism localized to cerebellar Purkinje cells.
• The exchange between Randy and John Lisman.
• The blog post Randy mentions about Universal function approximation:
  • The Truth About the [Not So] Universal Approximation Theorem