Brain Inspired

Paul Middlebrooks
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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. 5 NGÀY TRƯỚC

    BI 236 Liset de la Prida: Neurons, Ripples, and Manifolds

    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. Check out this story: From genes to dynamics: Examining brain cell types in action may reveal the logic of brain function 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. 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

    1 giờ 44 phút

  2. 8 THG 4

    BI 235 Romain Brette: The Brain, in Theory

    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. 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

    2 giờ 11 phút

  3. 25 THG 3

    BI 234 Juan Gallego: The Neural Manifold Manifesto

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

    2 giờ 2 phút

  4. 11 THG 3

    BI 233 Tom Griffiths: The Laws of Thought

    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. 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

    1 giờ 40 phút

  5. 25 THG 2

    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. 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 NeuroscienceBI 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?

    1 giờ 53 phút

  6. 11 THG 2

    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

    1 giờ 48 phút

  7. 28 THG 1

    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

    1 giờ 49 phút

  8. 14 THG 1

    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

    1 giờ 41 phút
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Giới Thiệu

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.

Thông Tin

  • Nhà sáng tạo
    Paul Middlebrooks
  • Năm hoạt động
    2018 - 2026
  • Tập
    151
  • Xếp hạng
    Sạch
  • Bản quyền
    © 2019 Brain-Inspired
  • Trang web chương trình
    Brain Inspired

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