BI 221 Ann Kennedy: Theory Beneath the Cortical Surface Brain Inspired 2026-09-21 | BI 221 Ann Kennedy: Theory Beneath the Cortical Surface
BI 217 Jennifer Prendki: Consciousness, Life, AI, and Quantum Physics Brain Inspired 2025-07-30 | Show notes: braininspired.co/podcast/217Patreon (full episodes, archive, Discord, more): patreon.com/braininspiredApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeMusic by @dunovankThe 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: thetransmitter.org/partnersSign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: thetransmitter.org/newslettersTo explore more neuroscience news and perspectives, visit thetransmitter.org.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 race0:00 - Intro3:25 - Jennifer's background13:10 - Consciousness16:38 - Life and consciousness23:16 - Superalignment40:11 - Quantum1:04:45 - Wetware and biological mimicry1:15:03 - Neural interfaces1:16:48 - AI ethics1:2:35 - AI models are not models1:27:13 - What scaling will get us1:39:53 - Current roadblocks1:43:19 - Philosophy
Brains Control Nodes The New Grand Plan Brain Inspired 2025-07-28 | 🎧 Full episode here: youtube.com/watch?v=UjGRlhMy7wY💡 More at braininspired.co#shorts
Brains Criticality A Future Mental Hygiene? Brain Inspired 2025-07-25 | Woodrow Shew on how criticality metrics may help mental disorders🎧 Full episode here: youtube.com/watch?v=4ud1ebmXIe8💡 More at braininspired.co#shorts #criticality #health #mentalhealth #cognitivefunction critical brain hypothesis, mental health, mental disorders, cognitive function
Breaking Symmetry The Key to Activity Brain Inspired 2025-07-23 | John Beggs on the critical brain hypothesis🎧 Full episode here: youtube.com/watch?v=ek4l_JnbCVg💡 More at braininspired.co#shorts #neuroscience #cognitivescience
Perspective Neuroscience Big Picture Conversation Brain Inspired 2025-07-21 | 🎧 Full episode here: youtube.com/watch?v=UjGRlhMy7wY💡 More at braininspired.co#shorts
Brain vs Computer: A New Framework Brain Inspired 2025-07-18 | Xiao-Jing Wang on the brain computer metaphor and dynamical systems.🎧 Full episode here: youtube.com/watch?v=qO8Mvqj1TnI 💡 More at braininspired.co#shorts #cognitivescience #neuroscience dynamical systems, brain computer metaphor
Sleep Supercharges Learning Brain Inspired 2025-07-16 | Keith Hengen on how sleep restores critical brain dynamics for learning🎧 Full episode here: youtube.com/watch?v=4ud1ebmXIe8💡 More at braininspired.co#shorts #criticality #sleep #health #learningcritical brain hypothesis, sleep science, brain states
BI 216 Woodrow Shew and Keith Hengen: The Nature of Brain Criticality Brain Inspired 2025-07-16 | Woodrow Shew and Keith Hengen on critical brain dynamics and cognition, learning, and development.Show notes: braininspired.co/podcast/216Patreon (full episodes, archive, Discord, more): patreon.com/braininspiredApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeMusic by @dunovankThe 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: thetransmitter.org/partnersSign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: thetransmitter.org/newslettersTo explore more neuroscience news and perspectives, visit thetransmitter.org.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. See that here: youtube.com/watch?v=ek4l_JnbCVgToday 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 assistant 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.There's quite a bit more in the full patreon version of this episode. Consider supporting brain inspired if you want all the full episodes, the full archive as well, or just to express how you value this podcast bringing you these kinds of thoughtful conversations. 0:00 - Intro3:41 - Collaborating6:22 - Criticality community14:47 - Tasks vs. Naturalistic20:50 - Nature of criticality25:47 - Deviating from criticality33:45 - Sleep for criticality38:41 - Neuromodulation for criticality40:45 - Criticality Definition part 1: scale invariance43:14 - Criticality Definition part 2: At a boundary51:56 - New method to assess criticality56:12 - Types of criticality1:02:23 - Value of criticality versus other metrics1:15:21 - Manifolds and criticality1:26:06 - Current challenges
Evolution & Criticality, A New Perspective Brain Inspired 2025-07-14 | 🎧 Full episode here: youtube.com/watch?v=ek4l_JnbCVg💡 More at braininspired.co#shorts
Weavers 1948 Complexity Theory Brain Inspired 2025-07-13 | David Krakauer on the origins of the term complexity science🎧 Full episode here: youtube.com/watch?v=__V89ZR3vUE💡 More at braininspired.co#shorts #complexity #philosophy complexity science, philosophy of science
Brains Algorithmic Principles Brain Inspired 2025-07-12 | Dmitri Chklovskii on brains and control theory🎧 Full episode here: youtube.com/watch?v=0-CBfn7dMcA💡 More at braininspired.co#shorts #neurons #control #algorithms
Thinking Like a Complexity Scientist Brain Inspired 2025-07-10 | 🎧 Full episode here: youtube.com/watch?v=__V89ZR3vUE💡 More at braininspired.co#shorts
Neural Complexity vs Criticality Brain Inspired 2025-07-10 | 🎧 Full episode here: youtube.com/watch?v=ek4l_JnbCVg💡 More at braininspired.co#shorts
Bifurcation in neuroscience Brain Inspired 2025-07-09 | 🎧 Full episode here: youtube.com/watch?v=qO8Mvqj1TnI 💡 More at braininspired.co#shorts
BI 215 Xiao-Jing Wang: Theoretical Neuroscience Comes of Age Brain Inspired 2025-07-02 | Xiao-Jing Wang on theoretical neuroscienceShow notes: braininspired.co/podcast/215Patreon (full episodes, archive, Discord, more): patreon.com/braininspiredApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeMusic by @dunovankThe 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: thetransmitter.org/partnersSign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: thetransmitter.org/newslettersTo explore more neuroscience news and perspectives, visit thetransmitter.org.0:00 - Intro3:08 - Why the book now?11:00 - Modularity in neuro vs AI14:01 - Working memory and modularity22:37 - Canonical cortical microcircuits25:53 - Gradient of inhibitory neurons27:47 - Comp neuro then and now45:35 - Cross-level mechanistic understanding1:13:38 - Bifurcation1:24:51 - Bifurcation and degeneracy1:34:02 - Control theory1:35:41 - Psychiatric disorders1:39:14 - Beyond dynamical systems1:43:447 - Mouse as a model1:48:11 - AI needs a PFC
BI 214 Nicole Rust: How To Actually Fix Brains and Minds Brain Inspired 2025-06-18 | Nicole Rust on how understanding brains as complex dynamical systems will help us accelerate treatments for brain disorders.Show notes: braininspired.co/podcast/214Patreon (full episodes and Discord community): patreon.com/braininspiredApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeThe 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: thetransmitter.org/partnersCheck out this story: What, if anything, makes mood fundamentally different from memory? thetransmitter.org/the-big-picture/what-if-anything-makes-mood-fundamentally-different-from-memorySign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: thetransmitter.org/newslettersTo explore more neuroscience news and perspectives, visit thetransmitter.org.Elusive Cures: Why Neuroscience Hasn’t Solved Brain Disorders―and How We Can Change That. Nicole Rust runs the Visual Memory laboratory at UPenn, 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.0:00 - Intro6:12 - Nicole's path19:25 - The grand plan25:18 - Robustness and fragility39:15 - Mood49:25 - Model everything!56:26 - Epistemic iteration1:06:50 - Can we standardize mood?1:10:36 - Perspective neuroscience1:20:12 - William Wimsatt1:25:40 - Consciousness
BI 213 Representations in Minds and Brains Brain Inspired 2025-06-04 | Five philosophers and neuroscientists discuss what representation means for brains and minds.Show notes: braininspired.co/podcast/213Patreon (full episodes and Discord community): patreon.com/braininspiredApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeMusic by @dunovankThe 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: thetransmitter.org/partnersCheck out this series of essays about representations:What are we talking about? Clarifying the fuzzy concept of representation in neuroscience and beyond: thetransmitter.org/defining-representations/what-are-we-talking-about-clarifying-the-fuzzy-concept-of-representation-in-neuroscience-and-beyondSign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: thetransmitter.org/newslettersTo explore more neuroscience news and perspectives, visit thetransmitter.org.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.0:00 - Intro3:55 - What is a representation to a neuroscientist?14:44 - How to deal with the dilemma21:20 - Opposing views31:00 - What's at stake?51:10 - Neural-only representation1:01:11 - When "representation" is playing a useful role1:12:56 - The role of a neuroscientist1:39:35 - The purpose of "representational talk"1:53:03 - Non-representational mental phenomenon1:55:53 - Final thoughts
BI 212 John Beggs: Why Brains Seek the Edge of Chaos Brain Inspired 2025-05-21 | John Beggs on criticality in brains.Show notes: braininspired.co/podcast/212Patreon (full episodes and Discord community: patreon.com/braininspiredApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeThe 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: thetransmitter.org/partnersSign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: thetransmitter.org/newslettersTo explore more neuroscience news and perspectives, visit thetransmitter.org.ou 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. You'll hear John is super scholarly about the subject, even when asked about topics outside his main wheelhouse, so there are lots of links to papers he mentions in the show notes. 0:00 - Intro4:28 - What is criticality?10:19 - Why is criticality special in brains?15:34 - Measuring criticality24:28 - Dynamic range and criticality28:28 - Criticisms of criticality31:43 - Current state of critical brain hypothesis33:34 - Causality and criticality36:39 - Criticality as a homeostatic set point38:49 - Is criticality necessary for life?50:15 - Shooting for criticality far from thermodynamic equilibrium52:45 - Quasi- and near-criticality55:03 - Cortex vs. whole brain58:50 - Structural criticality through development1:01:09 - Criticality in AI1:03:56 - Most pressing criticisms of criticality1:10:08 - Gradients of criticality1:22:30 - Homeostasis vs. criticality1:29:57 - Minds and criticality
BI 211 COGITATE: Testing Theories of Consciousness Brain Inspired 2025-05-07 | Rony Hirschhorn, Alex Lepauvre, and Oscar Ferrante on testing integrated information and global neuronal workspace theories of consciousness.Show notes: braininspired.co/podcast/211Patreon (full episodes and Discord community): patreon.com/braininspiredApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeThe 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: thetransmitter.org/partnersSign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: thetransmitter.org/newslettersTo 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.0:00 - Intro4:00 - COGITATE17:42 - How the experiments were developed32:37 - How data was collected and analyzed41:24 - Prediction 1: Where is consciousness?47:51 - The experimental task1:00:14 - Prediction 2: Duration of consciousness-related activity1:18:37 - Prediction 3: Inter-areal communication1:28:28 - Big picture of the results1:44:25 - Moving forward
BI 210 Dean Buonomano: Consciousness, Time, and Organotypic Dynamics Brain Inspired 2025-04-23 | Dean Buonomano on time in neuroscience vs. physics, integrated information theory, testing timing dynamics in organotypic brain slices, and how AI doesn't need neuroscience to continue to progress.Show notes: braininspired.co/podcast/210Patreon (full episodes and Discord community): patreon.com/braininspiredApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLemusic by Kyle Dunovan: @dunovank 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: thetransmitter.org/partnersCheck out this story: The brain holds no exclusive rights on how to create intelligence.thetransmitter.org/neuroai/the-brain-holds-no-exclusive-rights-on-how-to-create-intelligenceSign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: thetransmitter.org/newslettersTo explore more neuroscience news and perspectives, visit thetransmitter.org.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.0:00 - Intro8:49 - AI doesn't need biology17:52 - Time in physics and in neuroscience34:04 - Integrated information theory1:01:34 - Global neuronal workspace theory1:07:46 - Organotypic slices and predictive processing1:26:07 - Do brains actually measure time? David Robbe
BI 209 Aran Nayebi: The NeuroAI Turing Test Brain Inspired 2025-04-09 | Aran Nayebi on reverse-engineering brains to build autonomous agents, and an update to the Turing test for NeuroAI.Show notes: braininspired.co/podcast/209Patreon (full episodes and Discord community): patreon.com/braininspiredApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeThe 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: thetransmitter.org/partnersSign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: thetransmitter.org/newslettersTo 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.0:00 - Intro5:24 - Background20:46 - Building embodied agents33:00 - Adaptability49:25 - Marr's levels54:12 - Sensorimotor loop and intrinsic goals1:00:05 - NeuroAI Turing Test1:18:18 - Representations1:28:18 - How to know what to measure1:32:56 - AI safety
BI 208 Gabriele Scheler: From Verbal Thought to Neuron Computation Brain Inspired 2025-03-26 | Gabriele Scheler discusses her theoretical neuroscience work, understanding the link between cognition and verbal thought, and making a new type of neuron model to improve artificial intelligence.Show notes: braininspired.co/podcast/208Patreon (full episodes and Discord community: patreon.com/braininspiredApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeThe 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: thetransmitter.org/partnersSign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: thetransmitter.org/newslettersTo explore more neuroscience news and perspectives, visit thetransmitter.org.Gabriele Scheler co-founded the Carl Correns Foundation for Mathematical Biology. In fact, 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.0:00 - Intro4:41 - Gabriele's early interests in verbal thinking14:14 - What is thinking?24:04 - Starting one's own foundation58:18 - Building a new single neuron model1:19:25 - The right level of abstraction1:25:00 - How a new neuron would change AI
BI 207 Alison Preston: Schemas in Our Brains and Minds Brain Inspired 2025-03-12 | Ali Preston on how the neuroscience of schemas, which help us form memories, integrate and differentiate information, and make predictions.Show notes: braininspired.co/podcast/207Patreon (full episodes and Discord community: patreon.com/braininspiredApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeThe 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: thetransmitter.org/partnersSign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: thetransmitter.org/newslettersTo explore more neuroscience news and perspectives, visit thetransmitter.org.The concept of a schema goes back at least to the philosopher Emmanuel 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 with Ciara Greene we talked about a little about schemas and how they underlie our memories, and learning, and predictions, and how they can lead to inaccurate memories and predictions, but today we really talk more about how circuits in the brain might adaptively underlie this process as we develop, and how to go about measuring it in the first place.0:00 - Intro6:51 - Schemas20:37 - Schemas and the developing brain35:03 - Information theory, dimensionality, and detail41:17 - Geometry of schemas47:26 - Schemas and creativity50:29 - Brain connection pruning with development1:02:46 - Information in brains1:09:20 - Schemas and development in AI
BI 206 Ciara Greene: Memories Are Useful, Not Accurate Brain Inspired 2025-02-26 | Ciara Greene discusses how human episodic memory works, and why our common assumptions about the role of memoryShow notes: braininspired.co/podcast/206Patreon (full episodes and Discord community: patreon.com/braininspiredApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeThe 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: thetransmitter.org/partnersFor a deeper dive into the neuroscience of memory, check out this story:What makes memories last—dynamic ensembles or static synapses?thetransmitter.org/the-big-picture/what-makes-memories-last-dynamic-ensembles-or-static-synapsesSign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: thetransmitter.org/newslettersTo explore more neuroscience news and perspectives, visit thetransmitter.org.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. 0:00 - Intro5:35 - The function of memory6:41 - Reconstructive nature of memory13:50 - Memory schemas, highly superior autobiographical memory20:49 - Misremembering and flashbulb memories27:52 - Forgetting and schemas36:06 - What is a "good" memory?39:35 - Memories and intention43:47 - Memory and context49:55 - Implanting false memories1:04:10 - Memory suggestion during interrogations1:06:30 - Memory, imagination, and creativity1:13:45 - Artificial intelligence and memory1:21:21 - Driven by questions
BI 205 Dmitri Chklovskii: Neurons Are Smarter Than You Think Brain Inspired 2025-02-12 | Dmitri Chklovskii believes all your neurons are smart controller agents. Understanding this will transform neuroscience and how we build artificial intelligence.Show notes: braininspired.co/podcast/205Patreon (full episodes and Discord community): patreon.com/braininspiredApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeThe 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: thetransmitter.org/partnersSign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: thetransmitter.org/newslettersTo explore more neuroscience news and perspectives, visit thetransmitter.org.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 - 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.0:00 - Intro7:34 - Physicists approach for neuroscience12:39 - What's missing in AI and neuroscience?16:36 - Connectomes31:51 - Understanding complex systems33:17 - Earliest models of neurons39:08 - Smart neurons42:56 - Neuron theories that influenced Mitya46:50 - Neuron as a controller55:03 - How to test the neuron as controller hypothesis1:00:29 - Direct data-driven control1:11:09 - Experimental evidence1:22:25 - Single neuron doctrine and population doctrine1:25:30 - Neurons as agents1:28:52 - Implications for AI1:30:02 - Limits to control perspective
BI 204 David Robbe: Your Brain Doesnt Measure Time Brain Inspired 2025-01-29 | David Robbe challenges conventional notions of how our brains keep track of time, inspired by his own behavioral neuroscience research and Henri Bergson's concept of durée. Show notes: braininspired.co/podcast/204Patreon (full episodes and Discord community: patreon.com/braininspiredApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeThe 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: thetransmitter.org/partnersSign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: thetransmitter.org/newslettersTo explore more neuroscience news and perspectives, visit thetransmitter.org.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.0:00 - Intro3:59 - Why behavior is so important in itself10:27 - Henri Bergson21:17 - Bergson's view of life26:25 - A task to test how animals time things34:08 - Back to Bergson and duree39:44 - Externalizing time44:11 - Internal representation of time1:03:38 - Cognition as internal movement1:09:14 - Free will1:15:27 - Implications for AI
BI 203 David Krakauer: How To Think Like a Complexity Scientist Brain Inspired 2025-01-14 | David Krakauer on how complexity scientists approach understanding the systems they study, following the past 100 years of foundational complexity science research.Show notes: braininspired.co/podcast/203Patreon (full episodes and Discord community: patreon.com/braininspiredApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeThe 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: thetransmitter.org/partnersSign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: thetransmitter.org/newslettersTo explore more neuroscience news and perspectives, visit thetransmitter.org.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. 0:00 - Intro3:45 - Origins of The Complex World20:10 - 4 pillars of complexity36:27 - 40s to 70s in complexity42:33 - How to proceed as a complexity scientist54:32 - Broken symmetries1:02:40 - Emergence1:13:25 - Time scales and complexity1:18:48 - Consensus and how ideas migrate1:29:25 - Disciplinary matrix (Kuhn)1:32:45 - Intelligence vs. life
BI 202 Eli Sennesh: Divide-and-Conquer to Predict Brain Inspired 2025-01-03 | Eli Sennesh on a biologically plausible predictive coding mechanism.Show notes: braininspired.co/podcast/202Patreon (full episodes and Discord community: patreon.com/braininspiredApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeThe 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: thetransmitter.org/partnersSign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: thetransmitter.org/newslettersTo explore more neuroscience news and perspectives, visit thetransmitter.org.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.0:00 - Intro3:59 - Eli's worldview17:56 - NeuroAI is hard24:38 - Prediction errors vs surprise55:16 - Divide and conquer1:13:24 - Challenges1:18:44 - How to build AI1:25:56 - Affect1:31:55 - Abolish the value function
BI 201 Rajesh Rao: From Predictive Coding to Brain Co-Processors Brain Inspired 2024-12-18 | Rajesh Rao shares his updated theory on how the cortex could implement active predictive codingShow notes: braininspired.co/podcast/201Patreon (full episodes and Discord community: patreon.com/braininspiredApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeThe 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: thetransmitter.org/partnersSign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: thetransmitter.org/newslettersTo explore more neuroscience news and perspectives, visit thetransmitter.org.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. 0:00 - Intro7:40 - Predictive coding origins16:14 - Early appreciation of recurrence17:08 - Prediction as a general theory of the brain18:38 - Rao and Ballard 199926:32 - Prediction as a general theory of the brain33:24 - Perception vs action33:28 - Active predictive coding45:04 - Evolving to augment our brains53:03 - BrainNet57:12 - Neural co-processors1:11:19 - Decoding the Indus Script1:20:18 - Transformer models relation to active predictive coding
BI 200 Grace Hwang and Joe Monaco: The Future of Neuro-AI Brain Inspired 2024-12-04 | Grace Hwang and Joe Monaco discuss the future of the interaction between neuroscience and artificial intelligence.Show notes: braininspired.co/podcast/200Patreon (full episodes and Discord community: patreon.com/braininspiredApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeThe 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: thetransmitter.org/partnersSign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: thetransmitter.org/newslettersTo explore more neuroscience news and perspectives, visit thetransmitter.org.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. So it 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. So 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.0:00 - Intro25:45 - NeuroAI Workshop - neuromorphics33:31 - Neuromorphics and theory49:19 - Reflections on the workshop54:22 - Neurodynamical computing and information boundaries1:01:04 - Perceptual control theory1:08:56 - Digital twins and neural foundation models1:14:02 - Base layer of computation
BI 199 Hessam Akhlaghpour: Natural Universal Computation Brain Inspired 2024-11-26 | Hessam Akhlaghpour describes how organisms may have evolved universal computation using RNA sequences and secondary structures.Show notes: braininspired.co/podcast/199Patreon (full episodes and Discord community: patreon.com/braininspiredApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeThe 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: thetransmitter.org/partnersSign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: thetransmitter.org/newslettersTo explore more neuroscience news and perspectives, visit thetransmitter.org.Hessam Akhlaghpour is a postdoctoral researcher at Rockefeller University in the Maimon lab. In that capacity, he studies the neuroscience of decision making and cognition in fruit flies. However, his interests took a different turn, or an additional turn, after being 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, as you'll hear him describe, 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.0:00 - Intro4:44 - Hessam's background11:50 - Randy Gallistel's book14:43 - Information in the brain17:51 - Hessam's turn to universal computation35:30 - AI and universal computation40:09 - Universal computation to solve intelligence44:22 - Connecting sub and super molecular50:10 - Junk DNA56:42 - Genetic material for coding1:06:37 - RNA and combinatory logic1:35:14 - Outlook1:42:11 - Reflecting on the molecular world
BI 198 Tony Zador: Neuroscience Principles to Improve AI Brain Inspired 2024-11-11 | Tony Zador discusses why he's excited about how neuroscience can help "catalyze" the next generation of artificial intelligence.Show notes: braininspired.co/podcast/198Patreon (full episodes and Discord community: patreon.com/braininspiredApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeThe 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: thetransmitter.org/newslettersTo explore more neuroscience news and perspectives, visit thetransmitter.org.Check out Tony's essays in the Transmitter:NeuroAI: A field born from the symbiosis between neuroscience, AI: thetransmitter.org/neuroai/neuroai-a-field-born-from-the-symbiosis-between-neuroscience-aiWhat the brain can teach artificial neural networks: thetransmitter.org/neuroai/what-the-brain-can-teach-artificial-neural-networksTony 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. So, 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. If you like written essays, by chance Tony has written two essays, on the past and the future of NeuroAI, which are available on the Transmitter website and I think nicely complement our discussion on this episode. I'll link to those essays in the show notes, where I also link to a couple of the papers we discuss, and Tony's previous BI episodes. 0:00 - Intro3:28 - "Neuro-AI"12:48 - Visual cognition history18:24 - Information theory in neuroscience20:47 - Necessary steps for progress24:34 - Neuro-AI models and cognition35:47 - Animals for inspiring AI41:48 - What we want AI to do46:01 - Development and AI59:03 - Robots1:25:10 - Catalyzing the next generation of AI
BI 197 Karen Adolph: How Babies Learn to Move and Think Brain Inspired 2024-10-25 | Karen Adolph describes her research and ideas studying motor development in babies, and how it could help improve robots.Apologies this is audio-only!Show notes: braininspired.co/podcast/197Patreon (full episodes and Discord community: patreon.com/braininspiredApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeThe 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: thetransmitter.org/partnersSign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: thetransmitter.org/newslettersTo 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.0:00 - Intro3:06 - Karen's background5:57 - Eleanor Gibson10:02 - Karen's early interest in ecological psychology13:12 - Ecological Psychology concepts20:23 - What is mind?23:10 - Modern AI from Karen's perspective29:18 - Development, bodies, and robots34:46 - Falling is a good thing (when you're young)38:38 - Outlook on AI49:14 - How to study babies58:11 - Naturalistic behavior1:08:32 - Motor cortex isn't motor related early in development1:20:12 - Karen's future directions
BI 196 Cristina Savin and Tim Vogels with Gaute Einevoll and Mikkel Lepperød Brain Inspired 2024-10-11 | 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 to discuss how artificial intelligence has influenced their neuroscience research, thoughts about brains and minds, and productivity.Show notes: braininspired.co/podcast/196Patreon for full episodes and Discord community: patreon.com/braininspiredThe 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: thetransmitter.org/partnersSign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: thetransmitter.org/newsletters To explore more neuroscience news and perspectives, visit thetransmitter.org.Apple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLe
BI 195 Ken Harris and Andreas Tolias with Gaute Einevoll and Mikkel Lepperød Brain Inspired 2024-10-08 | This is the first of two less usual episodes, recorded during a recent NeuroAI workshop in Norway. Gaute Einevoll and I have discussions with neuroscientists Ken Harris, Andreas Tolias, and Mikkel Lepperød about how artificial intelligence has shaped their neuroscience research, thoughts, and productivity.Show notes: braininspired.co/podcast/195Patreon for full episodes and Discord community: patreon.com/braininspiredThe 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: thetransmitter.org/partnersSign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: thetransmitter.org/newsletters To explore more neuroscience news and perspectives, visit thetransmitter.org.Apple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLe
BI 194 Vijay Namboodiri & Ali Mohebi: Dopamine Keeps Getting More Interesting Brain Inspired 2024-09-27 | Show notes: braininspired.co/podcast/194Patreon for full episodes and Discord community: patreon.com/braininspiredApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeThe 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: thetransmitter.org/partnersCheck out these stories:Dopamine and the need for alternative theories Reconstructing dopamine’s link to rewardSign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: thetransmitter.org/newsletters To explore more neuroscience news and perspectives, visit thetransmitter.org.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. 0:00 - Intro3:42 - Dopamine: the history of theories32:54 - Importance of learning and behavior studies39:12 - Dopamine and causality1:06:45 - Controversy over Vijay's findings
BI 193 Kim Stachenfeld: Enhancing Neuroscience and AI Brain Inspired 2024-09-11 | Show notes: braininspired.co/podcast/193Patreon (full episodes and Discord community): patreon.com/braininspiredApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeThe 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: thetransmitter.org/partnersCheck out this story: Monkeys build mental maps to navigate new tasks thetransmitter.org/cognitive-neuroscience/monkeys-build-mental-maps-to-navigate-new-tasksSign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: thetransmitter.org/newsletters To explore more neuroscience news and perspectives, visit thetransmitter.org.Music by: The New Year: http://www.thenewyear.netKim 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.ble learned simulators.0:00 - Intro4:31 - Deepmind's original and current vision9:53 - AI as tools and models12:53 - Has AI hindered neuroscience?17:05 - Deepmind vs academic work balance20:47 - Is industry better suited to understand brains?24?42 - Trajectory of Deepmind27:41 - Kim's trajectory33:35 - Is the brain a ML entity?36:12 - Hippocampus44:12 - Reinforcement learning51:32 - What does neuroscience need more and less of?1:02:53 - Neuroscience in a weird place?1:06:41 - How Kim's questions have changed1:16:31 - Intelligence and LLMs1:25:34 - Challenges
BI 192 Àlex Gómez-Marín: The Edges of Consciousness Brain Inspired 2024-08-28 | Show notes: braininspired.co/podcast/192Patreon for full episodes and Discord community: patreon.com/braininspiredApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeMusic by: The New Year: http://www.thenewyear.netAlex Gomez-Marin 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. 0:00 - Intro4:13 - Evolving viewpoints10:05 - Near-death experience18:30 - Mechanistic neuroscience vs. the rest22:46 - Are you doing science?33:46 - Where is my. mind?44:55 - Productive vs. permissive brain59:30 - Panpsychism1:07:58 - Materialism1:10:38 - How to choose what to do1:16:54 - Fruit flies1:19:52 - AI and the Singularity
BI 191 Damian Kelty-Stephen: Fractal Turbulent Cascading Intelligence Brain Inspired 2024-08-15 | Show notes:braininspired.co/podcast/191Patreon for full episodes and Discord community: patreon.com/braininspiredVideo Series: Open Questions in AI and Neuroscience:braininspired.co/openApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeMusic by: The New Year: http://www.thenewyear.netDamian 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. 0:00 - Intro2:34 - Damian's background9:02 - Brains12:56 - Do neuroscientists have it all wrong?16:56 - Fractals everywhere28:01 - Fractality, causality, and cascades32:01 - Cascade instability as a metaphor for the brain40:43 - Damian's worldview46:09 - What is AI missing?54:26 - Turbulence1:01:02 - Intelligence without fractals? Multifractality1:10:28 - Ergodicity1:19:16 - Fractality, intelligence, life1:23:24 - What's exciting, changing viewpoints
BI 190 Luis Favela: The Ecological Brain Brain Inspired 2024-07-31 | braininspired.co/podcast/190Patreon for full episodes and Discord community: patreon.com/braininspiredVideo Series: Open Questions in AI and Neuroscience:braininspired.co/openApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeMusic by: The New Year: http://www.thenewyear.netLuis 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.0:00 - Intro7:05 - Louie's target with NEXT20:37 - Ecological psychology and grid cells22:06 - Why irreconcilable?28:59 - Why hasn't ecological psychology evolved more?47:13 - NExT49:10 - Hypothesis 155:45 - Hypothesis 21:02:55 - Artificial intelligence and ecological psychology1:16:33 - Manifolds1:31:20 - Hypothesis 4: Body, low-D, Synergies1:35:53 - Hypothesis 5: Mind emerges1:36:23 - Hypothesis 6:
BI 189 Joshua Vogelstein: Connectomes and Prospective Learning Brain Inspired 2024-06-29 | Show notes: braininspired.co/podcast/189Patreon for full episodes and Discord community: patreon.com/braininspiredVideo Series: Open Questions in AI and Neuroscience:braininspired.co/openApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeMusic by: The New Year: http://www.thenewyear.netJoshua Vogelstein runs the Neurodata Lab at Johns Hopkins, which seeks to "Understand and improve animal and machine learning worldwide."Today our discussion revolves around two main themes, along with my usual random tangentsJovo, 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'l get better soon.0:00 - Intro05:25 - Jovo's approach13:10 - Connectome of a fruit fly26:39 - What to do with a connectome37:04 - How important is a connectome?51:48 - Prospective learning1:15:20 - Efficiency1:17:38 - AI doomerism
BI 188 Jolande Fooken: Coordinating Action and Perception Brain Inspired 2024-05-27 | Show notes: braininspired.co/podcast/188Patreon for full episodes and Discord community: patreon.com/braininspiredApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeMusic by: The New Year: http://www.thenewyear.netJolande 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. 0:00 - Intro3:27 - Eye movements8:53 - Hand-eye coordination9:30 - Hand-eye coordination and naturalistic tasks26:45 - Levels of expertise34:02 - Yarbus and eye movements42:13 - Varieties of experimental paradigms, varieties of viewing the brain52:46 - Career vision1:04:07 - Evolving view about the brain1:10:49 - Coordination, robots, and AI
BI 186 Mazviita Chirimuuta: The Brain Abstracted Brain Inspired 2024-03-25 | Show notes: braininspired.co/podcast/186Patreon for full episodes and Discord community: patreon.com/braininspiredVideo Series: Open Questions in AI and Neuroscience:braininspired.co/openApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeMusic by: The New Year: http://www.thenewyear.netMazviita 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.0:00 - Intro5:28 - Neuroscience to philosophy13:39 - Big themes of the book27:44 - Simplifying by mathematics32:19 - Simplifying by reduction42:55 - Simplification by analogy46:33 - Technology precedes science55:04 - Theory, technology, and understanding58:04 - Cross-disciplinary progress58:45 - Complex vs. simple(r) systems1:08:07 - Is science bound to study stability?1:13:20 - 4E for philosophy but not neuroscience?1:28:50 - ANNs as models1:38:38 - Study of mind
BI 185 Eric Yttri: Orchestrating Behavior Brain Inspired 2024-03-06 | Show notes: braininspired.co/podcast/185Patreon for full episodes and Discord community: patreon.com/braininspiredVideo Series: Open Questions in AI and Neuroscience:braininspired.co/openApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeMusic by: The New Year: http://www.thenewyear.netAs 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.0:00 - Intro2:36 - Eric's background14:47 - Different animal models17:59 - ANNs as models for animal brains24:34 - Main question25:43 - How circuits produce appropriate behaviors26:10 - Cerebellum27:49 - What do motor cortex and basal ganglia do?49:12 - Neuroethology1:06:09 - What is a behavior?1:11:18 - Categorize behavior (B-SOiD)1:22:01 - Real behavior vs. ANNs1:33:09 - Best era in neuroscience
BI 184 Peter Stratton: Synthesize Neural Principles Brain Inspired 2024-02-20 | Show notes: braininspired.co/podcast/184Patreon for full episodes and Discord community: patreon.com/braininspiredVideo Series: Open Questions in AI and Neuroscience:braininspired.co/openApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeMusic by: The New Year: http://www.thenewyear.netPeter 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?0:00 - Intro3:50 - AI background, neuroscience principles8:00 - Overall view of modern AI14:14 - Moravec's paradox and robotics20:50 -Understanding movement to understand cognition30:01 - How close are we to understanding brains/minds?32:17 - Pete's goal34:43 - Principles from neuroscience to build AI42:39 - Levels of abstraction and implementation49:57 - Mental disorders and robustness55:58 - Function vs. implementation1:04:04 - Spiking networks1:07:57 - The roadmap1:19:10 - AGI1:23:48 - The terms AGI and AI1:26:12 - Consciousness
BI 183 Dan Goodman: Neural Reckoning Brain Inspired 2024-02-06 | Show notes: braininspired.co/podcast/183Patreon for full episodes and Discord community: patreon.com/braininspiredVideo Series: Open Questions in AI and Neuroscience:braininspired.co/openApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeMusic by: The New Year: http://www.thenewyear.netYou may know my guest today 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. 0:00 - Intro3:47 - Why spiking neural networks, and a mathematical background13:16 - Efficiency17:36 - Machine learning for neuroscience19:38 - Why not jump ship from SNNs?23:35 - Hard and easy tasks29:20 - How brains and nets learn32:50 - Exploratory vs. theory-driven science37:32 - Static vs. dynamic39:06 - Heterogeneity46:01 - Unifying principles vs. a hodgepodge50:37 - Sparsity58:05 - Specialization and modularity1:00:51 - Naturalistic experiments1:03:41 - Projects for SNN research1:05:09 - The right level of abstraction1:07:58 - Obstacles to progress1:12:30 - Levels of explanation1:14:51 - What has AI taught neuroscience?1:22:06 - How has neuroscience helped AI?
BI 182: John Krakauer Returns... Again Brain Inspired 2024-01-19 | Show notes: braininspired.co/podcast/182Patreon for full episodes and Discord community: patreon.com/braininspiredVideo Series: Open Questions in AI and Neuroscience:braininspired.co/openApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeMusic by: The New Year: http://www.thenewyear.netJohn 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.0:00 - Intro2:07 - It's a podcast episode!6:47 - Stroke and Sherrington neuroscience19:26 - Thinking vs. moving, representations34:15 - What's special about humans?56:35 - Does cortical reorganization happen?1:14:08 - Current era in neuroscience
BI 181 Max Bennett: A Brief History of Intelligence Brain Inspired 2023-12-25 | Show notes: braininspired.co/podcast/181Patreon for full episodes and Discord community: patreon.com/braininspiredVideo Series: Open Questions in AI and Neuroscience:braininspired.co/openApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeMusic by: The New Year: http://www.thenewyear.netBy day, Max Bennett has cofounded and CEO'd multiple AI and technology companies making him an entrepreneur. 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. As he says toward the beginning, but it's worth repeating here, 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, and a recurring theme is that a given single breakthrough Max cites 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.So the book is filled with unifying accounts like that, and it makes for a great read, and you should strap in, but because Max gives a sort of masterclass about many of the ideas in his book. 0:00 - Intro5:26 - Why evolution is important7:22 - Maclean's triune brain14:59 - Breakthrough 1: Steering29:06 - Fish intelligence40:38 - Breakthrough 3: Mentalizing52:44 - How could we improve the human brain?1:00:44 - What is intelligence?1:13:50 - Breakthrough 5: Speaking
BI 180 Panel Discussion: Long-term Memory Encoding and Connectome Decoding Brain Inspired 2023-12-11 | Show notes: braininspired.co/podcast/180Patreon for full episodes and Discord community: patreon.com/braininspiredVideo Series: Open Questions in AI and Neuroscience:braininspired.co/openApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeMusic by: The New Year: http://www.thenewyear.netWelcome 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.0:00 - Intro1:45 - Ken Hayworth14:09 - Panel Discussion
BI 179 Laura Gradowski: Include the Fringe with Pluralism Brain Inspired 2023-11-27 | Patreon for full episodes and Discord community: patreon.com/braininspiredVideo Series: Open Questions in AI and Neuroscience:braininspired.co/openApple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLeMusic by: The New Year: http://www.thenewyear.netShow notes: braininspired.co/podcast/179Laura Gradowski is a philosopher of science at the University of Pittsburgh. Pluralism, or scientific pluralism anyway, 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... and we discuss her ideas about how to implement this, where to draw the line, and much more.0:00 - Intro3:57 - What is fringe?10:14 - What makes a theory fringe?14:31 - Fringe to mainstream17:23 - Garcia effect28:17 - Fringe to mainstream: other examples32:38 - Fringe and consciousness33:19 - Words meanings change over time40:24 - Pseudoscience43:25 - How fringe becomes mainstream47:19 - More fringe characteristics50:06 - Pluralism as a solution54:02 - Progress1:01:39 - Encyclopedia of theories1:09:20 - When to reject a theory1:20:07 - How fringe becomes fringe1:22:50 - Marginilization1:27:53 - Recipe for fringe theorist
