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. 2 days ago

    BI 247 Maxim Raginsky: A Control Theory View on Brains and AI

    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. Maxim Raginsky is a professor at the University of Illinois at Urbana-Champaign. Max describes himself as interested in probability and stochastic processes, deterministic and stochastic control, machine learning, optimization, and information theory. Today we mostly lean on his control theory expertise, although you'll here his knowledge is vast in many other domains, even some neuroscience. I wanted his control theory perspective on neuroscience, AI, and biological autonomy, so we dance around a lot of topics related to those. Max also writes a substack called The Art of the Realizable, from which I drew during parts of our conversation. Maxim Raginsky Substack: The Art of the Realizable.  Related papers Biological Autonomy Control-related episodes BI 143 Rodolphe Sepulchre: Mixed Feedback Control BI 205 Dmitri Chklovskii: Neurons Are Smarter Than You Think Read the transcript. 0:00 - Intro 3:07 - Low energy lifestyle 4:27 - Engineering and philosophy? 13:52 - Brains vs AI 19:45 - Inferring the inside from behavior 30:57 - Analog vs digital 41:32 - Is the brain a control system? 46:50 - Willems control 1:02:12 - Control vs cybernetics 1:13:26 - A control perspective on AI vs brains 1:17:46 - AGI 1:21:48 - Turing 1950 1:29:07 - Perceptual control theory and active inference 1:40:09 - Passive control in the brain? 1:41:48 - Computation 1:43:36 - Counting spikes

  2. 16 Sept

    BI 246 Andrea Gambarotto: Cognition Requires Agency

    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. Andrea Gambarotto is a postdoctoral philosopher and researcher at the University of Luxembourg. He is an expert in the philosophy of Georg Wilhelm Friedrich Hegel, most commonly known as Hegel. More recently he has been studying the relation between some of Hegel's ideas and those of modern theoretical biology regarding questions of autonomy and agency. Andrea argues that, where Immanuel Kant  believed we should explain biological stuff and inanimate stuff the same way- via mechanistic explanations, Hegel believed to explain the biological stuff, we should leverage the fact the biological organisms have intrinsic purpose… agency. And, Hegel's approach is in line with what's called the enactive approach in cognitive science, which has a long history and continues to thrive. Andrea explains all of that during our discussion. One reason I invited Andrea on is because these issues get the heart of what some of us care about, which is, what are the differences and similarities between our natural intelligence and engineered artificial intelligence? Why should we care about those differences? A large language model isn't alive, but does it have a mind? Should we call what it does cognition? What are the relations between life, mind, cognition, intelligence, consciousness? Those kinds of questions. We even discuss why the famed octopus might be really intelligent but not conscious. Andrea Gambarotto Gambarotto papers Enactivism and the Hegelian stance on intrinsic purposiveness Body plan organization and the evolution of conscious agency Blog: Dialectical Systems Papers also mentioned Weber & Varela 2002: Life after Kant: Natural purposes and the autopoietic foundations of biological individuality. Mossio & Bich 2014: What makes biological organisation teleological? Bechtel & Bich & 2021: Grounding cognition: heterarchical control mechanisms in biology. Pessoa 2026: Beyond networks: Toward adaptive models of biological complexity. Levins 1998: Dialectics and Systems Theory. Barandiaran & Moreno 2006: On What Makes Certain Dynamical Systems Cognitive: A Minimally Cognitive Organization Program. Books mentioned Linguistic Bodies: The Continuity between Life and Language Radical Embodied Cognitive Science An Evolutionary Story of Agency 0:00 - Intro 7:51 - Intrinsic and extrinsic purposiveness 14:39 - Hegel, Kant, and autonomy 28:07 - Constraint closure and enactivism 35:13 - How Hegel and Enactivitsm agree 43:58 - Dialectics 57:44 - Brain activity and enactivism 1:20:02 - Heidegger and cognitive science 1:26:59 - Artificial intelligence and Hegel 1:29:15 - Mind agency decoupling 1:43:38 - Evolution of conscious agency 1:52:42 - Heterarchy via McCulloch

  3. 2 Sept

    BI 245 Dan Levenstein: Neuro-AI, Dynamics, and Model Systems

    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. Daniel Levenstein started his NeuroAI and Dynamics Lab at Yale University about a year ago. We briefly discuss what it's like to transition from a postdoc to a principle investigator, i.e. head of the lab. But most mostly we discuss his work and ideas. Dan studies spontaneous neural activity during sleep, specially in brain areas like hippocampus and cortex, and how this internally generated spontaneous activity is related to learning and memory and navigation. Really, he used to study those processes directly through experimental brain recording datasets. These days he builds and studies models of those processes, using AI models and seeing how their dynamics and functions match what we see in brains. Levenstein Lab Social: @dlevenstein.bsky.social Related papers On the Role of Theory and Modeling in Neuroscience The problem-ladenness of theory Sequential predictive learning is a unifying theory for hippocampal representation and replay 0:00 - Intro 9:12 - Neuro-AI 18:23 - Experiment vs theory 20:36 - Ground vs active state neuron activity 25:38 - Beginning a lab 31:34 - Sleep and Internally generated activity 40:02 - Spiking neural networks 52:13 - Naturalistic neuro-AI 59:52 - Cognitive maps, world models 1:04:16 - Weasel words and motifs 1:08:17 - Transformers and brains 1:18:53 - AI vs biology 1:24:32 - Neuroscience theory

  4. 19 Aug

    BI 244 Marco Facchin: Philosophy and Science of Biological Brains

    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. Marco Facchin is a postdoctoral philosopher of neuroscience and cognitive sciences more broadly at the University of Antwerp. He and his colleague Farid Zahnoun recently hosted a workshop called Beyond Neuro-computationalism with themselves and a handful of speakers, almost all of whom have been on Brain Inspired. In that workshop, they discussed many topics around this sort of forever ongoing reassessment in neuroscience and philosophy about how best to think about cognition, the role of brains, embodied, enactive, embedded, extended - known together as 4E cognition - how much biological detail matters for a good explanation, and so on. The talks from that workshop are online, and I'll link to them in the show notes. So today Marco and I discuss how that all went, and many of the topics and themes I just mentioned, plus his own work and ideas along those lines. Marco Facchin Social: @marcofacchin.bsky.social Beyond neuro-computationalism talks. Why can’t we say what cognition is (at least for the time being) Predictive processing and anti-representationalism Defusing the Representation-Hungry Challenge Structural representations do not meet the job description challenge Structure and function in the predictive brain Read the transcript. 0:00 - Intro 3:30 - Beyond neuro-computationalism 14:02 - Vicente Raja motifs 17:58 - 4E cognition 32:36 - Philosophy and neuroscience 42:18 - The problem with predictive processing 48:56 - Role of AI in understanding brains and minds 54:05 - Metabolic constraints 1:07:58 - A philosopher's view of neuroscience 1:13:27 - A-lieving and AI 1:25:47 - AI consciousness

  5. 5 Aug

    BI 243 Alison Barth: Learning as a Window to Cortex

    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. Alison Barth runs the Barth Lab at Carnegie Mellon University, where they use learning experiments in mice to try to figure out how the cortex works. As you may know, the brain in general but also the cortex itself is made up of a large variety neuron cell types, with different activity properties. Alison has the gritty job of identifying those different cell types in sensory cortex, and seeing how they change when animals learn to associate rewards with sensory stimulation. So unlike many of the guests, who take a much more zoomed out view and look at how populations of neurons carry out some function, Alison is happiest down at the cellular level. So we talk about her work, why she prefers to work at that scale, and a variety of related topics. Barth Lab. Related papers Barth lab publications. Learning, prediction accuracy, and neural plasticity in sensory cortex. Read the transcript. 0:00 - Intro 4:24 - Alison's trajectory to learning and memory 21:02 - Automated mouse learning experiments 25:34 - What is success in this line of work? 32:34 - How many cell types do we need to explain? 34:33 - Current experiments 38:11 - How does cortex work? 45:19 - Predictive processing 1:02:01 - Obstacles 1:10:41 - Role of AI 1:33:38 - Moving forward

  6. 15 Jul

    BI 242 Kathryn Nave: How Life Gets its Meaning and Intelligence

    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. Kathryn Nave is a Leverhulme Trust Early Career Fellow at the University of Edinburgh, and the author of the book A Drive to Survive: The Free Energy Principle and the Meaning of Life. In the book, Kate dives deep into the free energy principle and active inference, which are popular approaches to studying brains, minds, and organisms in general, and which are being used in artificial intelligence. Ultimately, Kate finds these approaches come up short as explanatory frameworks for life, and autonomy, and intelligence. Instead, Kate and many others advocate a framework that Kate calls constraint closure or closure of constraints, but also goes by the name organizational closure. This is a concept from philosophy and theoretical biology that people like Alvaro Moreno and Matteo Mossio have put forth in their 2015 book Biological Autonomy. The core ideas are also found in various forms from people like Robert Rosen, Stuart Kauffman, Alicia Juarrero, Terrence Deacon, and others. We discuss what constraint closure is, why Kate thinks it's a solid foundation to build on, and what if anything it means for cognitive science and brain sciences to embrace this constraint closure view. I highly recommend the book even if you're looking for a primer on the free energy principle and active inference. As we discuss, Kate's journalism experience has helped her become a wonderful communicator of these notoriously difficult concepts. Kathryn Nave @kathrynnave; @kathrynnave.eurosky.social. A Drive to Survive: The Free Energy Principle and the Meaning of Life Related episode: BI 241 Johannes Jaeger: Agency and the Cyborg Myth Mentioned in the episode: We Need To Rewild The Internet Beyond Control: Finding the Purpose of Enactive Cognitive Science Read the transcript. 0:00 - Intro 5:39 - Journalism back to philosophy 15:56 - How Kate got into predictive processing etc. 21:30 - Predictive processing and phenomenology 30:45 - Organizational closure 37:37 - Constraint closure beyond the single cell 45:04 - Brain as metabolic 50:12 - Basal cognition 52:13 - Degeneracy 55:08 - Neutral networks 1:00:33 - AI and autonomy 1:08:12 - Meaning and mind 1:10:02 - Why do we need brains? 1:17:33 - Reframe neuroscience? 1:23:51 - Reifying models 1:27:43 - Free energy principle and active inference 1:37:16 - Tolerating as much variability as possible

  7. 1 Jul

    BI 241 Johannes Jaeger: Agency and the Cyborg Myth

    Support the show to get full episodes, full archive, and join the Discord community. Johannes Jaeger is Associate Faculty at the Complexity Science Hub in Vienna. He's also a freelance researcher, a philosopher, and an educator. He's here today to educate us about some of the fundamental differences between living organisms and machines, like AI, and why we should care about those differences. We discuss his paper The Cyborg Myth, an argument for why we can't seamlessly replace ourselves with machine parts over time. We talk about judgment and relevance realization as a fundamental difference between AI and living organisms -the ability to judge what is a relevant problem to solve in the first place, assuming intelligence is about problem solving. We also discuss what agency is in living systems, and why AI agents are something completely different. I think you get the recurring theme here. Yogi is writing a book called Beyond the Age of Machines, a work in progress and you can read it as he writes it on his expanding possibilities website. Untethered in the Platonic Realm (Yogi's website) Expanding Possibilities Book in progress: Beyond the Age of Machines Mastadon: @yoginho Related The Cyborg Myth.(talk version here) Naturalizing relevance realization: why agency and cognition are fundamentally not computational. Artificial intelligence is algorithmic mimicry: why artificial "agents" are not (and won't be) proper agents. Read the transcript. 0:00 - Intro 7:11 - The cyborg myth 15:16 - Judgment 24:22 - Consciousness 28:56 - Agency 36:40 - Relevance realization and energy efficiency 46:44 - Metabolism as a metaphor 1:00:39 - Robert Rosen 1:06:20 - Conceptual engineering 1:12:55 - Dynamics and computation 1:23:07 - Agency book

  8. 17 Jun

    BI 240 Cristopher Moore: Cognition and Computational Complexity

    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. Cristopher Moore is a professor at the Santa Fe Institute in New Mexico, and he is a computation and computational complexity expert. He recently joined a us in my complexity discussion group, and answered a bunch of our questions, but I wasn't done with him regarding what, if anything, computational complexity has to do understanding how brains and minds work. So that's why he's here today, and we discuss a wide variety of topics related to AI, computation, computational complexity, and cognition. Cris's Homepage Book: The Nature of Computation Related papers What Is a Macrostate? Subjective Observations and Objective Dynamics Read the transcript. 0:00 - Intro 4:24 - The Nature of Computation 9:14 - Computational complexity 28:22 - Real mathematics 35:08 - Current state of AI 39:04 - Computational complexity in the AI world 47:53 - Cognition, creation, problems 56:16 - Rugged landscapes and generalization 1:13:52 - What is computation? 1:32:31 - How would you study the brain?

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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
    162
  • Rating
    Explicit
  • Copyright
    © 2019 Brain-Inspired
  • Show Website
    Brain Inspired

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