Brain Inspired

Paul Middlebrooks
  • 4.8 (134)
  • NATURAL SCIENCES
  • UPDATED SEMIMONTHLY

Neuroscience and artificial intelligence work better together. Brain inspired is a celebration and exploration of the ideas driving our progress to understand intelligence. I interview experts about their work at the interface of neuroscience, artificial intelligence, cognitive science, philosophy, psychology, and more: the symbiosis of these overlapping fields, how they inform each other, where they differ, what the past brought us, and what the future brings. Topics include computational neuroscience, supervised machine learning, unsupervised learning, reinforcement learning, deep learning, convolutional and recurrent neural networks, decision-making science, AI agents, backpropagation, credit assignment, neuroengineering, neuromorphics, emergence, philosophy of mind, consciousness, general AI, spiking neural networks, data science, and a lot more. The podcast is not produced for a general audience. Instead, it aims to educate, challenge, inspire, and hopefully entertain those interested in learning more about neuroscience and AI.

  1. 58M AGO

    BI 232 How Should Neuroscience Integrate with Ecological Psychology?

    Support the show to get full episodes, full archive, and join the Discord community. 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 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. 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. de Wit lab. Luis Favela. The Ecological Brain: Unifying the Sciences of Brain, Body, and Environment MINT Lab. Ecological psychology  Previous episodes:BI 223 Vicente Raja: Ecological Psychology Motifs in NeuroscienceBI 190 Luis Favela: The Ecological Brain BI 213 Representations in Minds and Brains 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?

    1h 53m

  2. FEB 11

    BI 231 Jaan Aru: Conscious AI? Not Even Close!

    Support the show to get full episodes, full archive, and join the Discord community. 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 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. 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

    1h 48m

  3. JAN 28

    BI 230 Michael Shadlen: How Thoughts Become Conscious

    Support the show to get full episodes, full archive, and join the Discord community. 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 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. 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

    1h 49m

  4. JAN 14

    BI 229 Tomaso Poggio: Principles of Intelligence and Learning

    Support the show to get full episodes, full archive, and join the Discord community. 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 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. 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

    1h 41m

  5. 12/31/2025

    BI 228 Alex Maier: Laws of Consciousness

    Support the show to get full episodes, full archive, and join the Discord community. 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 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. 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

    1h 58m

  6. 12/17/2025

    BI 227 Decoding Memories: Aspirational Neuroscience 2025

    Support the show to get full episodes, full archive, and join the Discord community. 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 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. 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

    1h 15m

  7. 12/03/2025

    BI 226 Tatiana Engel: The High and Low Dimensional Brain

    Support the show to get full episodes, full archive, and join the Discord community. 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 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. 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

    1h 36m

  8. 11/19/2025

    BI 225 Henk De Regt: Understanding in Machines and Humans

    Support the show to get full episodes, full archive, and join the Discord community. 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 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. 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

    1h 44m
See All (145)

4.8
out of 5
134 Ratings

About

Neuroscience and artificial intelligence work better together. Brain inspired is a celebration and exploration of the ideas driving our progress to understand intelligence. I interview experts about their work at the interface of neuroscience, artificial intelligence, cognitive science, philosophy, psychology, and more: the symbiosis of these overlapping fields, how they inform each other, where they differ, what the past brought us, and what the future brings. Topics include computational neuroscience, supervised machine learning, unsupervised learning, reinforcement learning, deep learning, convolutional and recurrent neural networks, decision-making science, AI agents, backpropagation, credit assignment, neuroengineering, neuromorphics, emergence, philosophy of mind, consciousness, general AI, spiking neural networks, data science, and a lot more. The podcast is not produced for a general audience. Instead, it aims to educate, challenge, inspire, and hopefully entertain those interested in learning more about neuroscience and AI.

Information

  • Creator
    Paul Middlebrooks
  • Years Active
    2018 - 2026
  • Episodes
    145
  • Rating
    Clean
  • Copyright
    © 2019 Brain-Inspired
  • Show Website
    Brain Inspired

You Might Also Like