Explore every episode of the podcast Brain Inspired
| Title | Pub. Date | Duration | |
|---|---|---|---|
| BI 220 Michael Breakspear and Mac Shine: Dynamic Systems from Neurons to Brains | 10 Sep 2025 | 01:25:05 | |
Support the show to get full episodes, full archive, and join the Discord community. https://www.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: https://www.thetransmitter.org/partners/ Sign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: https://www.thetransmitter.org/newsletters/ To explore more neuroscience news and perspectives, visit thetransmitter.org. What changes and what stays the same as you scale from single neurons up to local populations of neurons up to whole brains? How tuning parameters like the gain in some neural populations affects the dynamical and computational properties of the rest of the system. Those are the main questions my guests today discuss. Michael Breakspear is a professor of Systems Neuroscience and runs the Systems Neuroscience Group at the University of Newcastle in Australia. Mac Shine is back, he was here a few years ago. Mac runs the Shine Lab at the University of Sidney in Australia. Michael and Mac have been collaborating on the questions I mentioned above, using a systems approach to studying brains and cognition. The short summary of what they discovered in their first collaboration is that turning up or down the gain across broad networks of neurons in the brain affects integration - working together - and segregation - working apart. They map this gain modulation on to the ascending arousal pathway, in which the locus coeruleus projects widely throughout the brain distributing noradrenaline. At a certain sweet spot of gain, integration and segregation are balanced near a bifurcation point, near criticality, which maximizes properties that are good for cognition. In their recent collaboration, they used a coarse graining procedure inspired by physics to study the collective dynamics of various sizes of neural populations, going from single neurons to large populations of neurons. Here they found that despite different coding properties at different scales, there are also scale-free properties that suggest neural populations of all sizes, from single neurons to brains, can do cognitive stuff useful for the organism. And they found this is a conserved property across many different species, suggesting it's a universal principle of brain dynamics in general. So we discuss all that, but to get there we talk about what a systems approach to neuroscience is, how systems neuroscience has changed over the years, and how it has inspired the questions Michael and Mac ask.
0:00 - Intro 4:28 - Neuroscience vs neurobiology 8:01 - Systems approach 26:52 - Physics for neuroscience 33:15 - Gain and bifurcation: earliest collaboration 55:32 - Multiscale organization 1:17:54 - Roadblocks | |||
| BI 219 Xaq Pitkow: Principles and Constraints of Cognition | 27 Aug 2025 | 01:47:11 | |
Support the show to get full episodes, full archive, and join the Discord community. https://www.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. Sign up for Brain Inspired email alerts to be notified every time a new Brain Inspired episode is released. To explore more neuroscience news and perspectives, visit thetransmitter.org. Xaq Pitkow runs the Lab for the Algorithmic Brain at Carnegie Mellon University. The main theme of our discussion is how Xaq approaches his research into cognition by way of principles, from which his questions and models and methods spring forth. We discuss those principles, and In that light, we discuss some of his specific lines of work and ideas on the theoretical side of trying understand and explain a slew of cognitive processes. A few of the specifics we discuss are:
Read the transcript. 0:00 - Intro 3:57 - Xaq's approach 8:28 - Inverse rational control 19:19 - Space of input-output functions 24:48 - Cognition for cognition 27:35 - Theory vs. experiment 40:32 - How does the brain compute with probabilities? 1:03:57 - Normative vs kludge 1:07:44 - Ecological neuroscience 1:20:47 - Representations 1:29:34 - Current projects 1:36:04 - Need a synaptome 1:42:20 - Across scales | |||
| BI 210 Dean Buonomano: Consciousness, Time, and Organotypic Dynamics | 22 Apr 2025 | 01:50:33 | |
Support the show to get full episodes, full archive, and join the Discord community. https://www.patreon.com/braininspiredDean 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.
Read the transcript. 0:00 - Intro 8:49 - AI doesn't need biology 17:52 - Time in physics and in neuroscience 34:04 - Integrated information theory 1:01:34 - Global neuronal workspace theory 1:07:46 - Organotypic slices and predictive processing 1:26:07 - Do brains actually measure time? David Robbe | |||
| BI 121 Mac Shine: Systems Neurobiology | 02 Dec 2021 | 01:43:12 | |
Support the show to get full episodes, full archive, and join the Discord community. https://www.patreon.com/braininspiredMac and I discuss his systems level approach to understanding brains, and his theoretical work suggesting important roles for the thalamus, basal ganglia, and cerebellum, shifting the dynamical landscape of brain function within varying behavioral contexts. We also discuss his recent interest in the ascending arousal system and neuromodulators. Mac thinks the neocortex has been the sole focus of too much neuroscience research, and that the subcortical brain regions and circuits have a much larger role underlying our intelligence.
0:00 - Intro 6:32 - Background 10:41 - Holistic approach 18:19 - Importance of thalamus 35:19 - Thalamus circuitry 40:30 - Cerebellum 46:15 - Predictive processing 49:32 - Brain as dynamical attractor landscape 56:48 - System 1 and system 2 1:02:38 - How to think about the thalamus 1:06:45 - Causality in complex systems 1:11:09 - Clinical applications 1:15:02 - Ascending arousal system and neuromodulators 1:27:48 - Implications for AI 1:33:40 - Career serendipity 1:35:12 - Advice | |||
| BI 120 James Fitzgerald, Andrew Saxe, Weinan Sun: Optimizing Memories | 21 Nov 2021 | 01:40:02 | |
Support the show to get full episodes, full archive, and join the Discord community. https://www.patreon.com/braininspiredJames, Andrew, and Weinan discuss their recent theory about how the brain might use complementary learning systems to optimize our memories. The idea is that our hippocampus creates our episodic memories for individual events, full of particular details. And through a complementary process, slowly consolidates those memories within our neocortex through mechanisms like hippocampal replay. The new idea in their work suggests a way for the consolidated cortical memory to become optimized for generalization, something humans are known to be capable of but deep learning has yet to build. We discuss what their theory predicts about how the "correct" process depends on how much noise and variability there is in the learning environment, how their model solves this, and how it relates to our brain and behavior.
0:00 - Intro 3:57 - Guest Intros 15:04 - Organizing memories for generalization 26:48 - Teacher, student, and notebook models 30:51 - Shallow linear networks 33:17 - How to optimize generalization 47:05 - Replay as a generalization regulator 54:57 - Whole greater than sum of its parts 1:05:37 - Unpredictability 1:10:41 - Heuristics 1:13:52 - Theoretical neuroscience for AI 1:29:42 - Current personal thinking | |||
| BI 119 Henry Yin: The Crisis in Neuroscience | 11 Nov 2021 | 01:06:36 | |
Support the show to get full episodes, full archive, and join the Discord community. https://www.patreon.com/braininspiredHenry and I discuss why he thinks neuroscience is in a crisis (in the Thomas Kuhn sense of scientific paradigms, crises, and revolutions). Henry thinks our current concept of the brain as an input-output device, with cognition in the middle, is mistaken. He points to the failure of neuroscience to successfully explain behavior despite decades of research. Instead, Henry proposes the brain is one big hierarchical set of control loops, trying to control their output with respect to internally generated reference signals. He was inspired by control theory, but points out that most control theory for biology is flawed by not recognizing that the reference signals are internally generated. Instead, most control theory approaches, and neuroscience research in general, assume the reference signals are what gets externally supplied... by the experimenter.
0:00 - Intro 5:40 - Kuhnian crises 9:32 - Control theory and cybernetics 17:23 - How much of brain is control system? 20:33 - Higher order control representation 23:18 - Prediction and control theory 27:36 - The way forward 31:52 - Compatibility with mental representation 38:29 - Teleology 45:53 - The right number of subjects 51:30 - Continuous measurement 57:06 - Artificial intelligence and control theory | |||
| BI 118 Johannes Jäger: Beyond Networks | 01 Nov 2021 | 01:36:08 | |
Support the show to get full episodes, full archive, and join the Discord community. https://www.patreon.com/braininspiredJohannes (Yogi) is a freelance philosopher, researcher & educator. We discuss many of the topics in his online course, Beyond Networks: The Evolution of Living Systems. The course is focused on the role of agency in evolution, but it covers a vast range of topics: process vs. substance metaphysics, causality, mechanistic dynamic explanation, teleology, the important role of development mediating genotypes, phenotypes, and evolution, what makes biological organisms unique, the history of evolutionary theory, scientific perspectivism, and a view toward the necessity of including agency in evolutionary theory. I highly recommend taking his course. We also discuss the role of agency in artificial intelligence, how neuroscience and evolutionary theory are undergoing parallel re-evaluations, and Yogi answers a guest question from Kevin Mitchell.
0:00 - Intro 4:10 - Yogi's background 11:00 - Beyond Networks - limits of dynamical systems models 16:53 - Kevin Mitchell question 20:12 - Process metaphysics 26:13 - Agency in evolution 40:37 - Agent-environment interaction, open-endedness 45:30 - AI and agency 55:40 - Life and intelligence 59:08 - Deep learning and neuroscience 1:03:21 - Mental autonomy 1:06:10 - William Wimsatt's biopsychological thicket 1:11:23 - Limtiations of mechanistic dynamic explanation 1:18:53 - Synthesis versus multi-perspectivism 1:30:31 - Specialization versus generalization | |||
| BI 117 Anil Seth: Being You | 19 Oct 2021 | 01:32:09 | |
Support the show to get full episodes, full archive, and join the Discord community. https://www.patreon.com/braininspiredAnil and I discuss a range of topics from his book, BEING YOU A New Science of Consciousness. Anil lays out his framework for explaining consciousness, which is embedded in what he calls the "real problem" of consciousness. You know the "hard problem", which was David Chalmers term for our eternal difficulties to explain why we have subjective awareness at all instead of being unfeeling, unexperiencing machine-like organisms. Anil's "real problem" aims to explain, predict, and control the phenomenal properties of consciousness, and his hope is that, by doing so, the hard problem of consciousness will dissolve much like the mystery of explaining life dissolved with lots of good science. Anil's account of perceptual consciousness, like seeing red, is that it's rooted in predicting our incoming sensory data. His account of our sense of self, is that it's rooted in predicting our bodily states to control them. We talk about that and a lot of other topics from the book, like consciousness as "controlled hallucinations", free will, psychedelics, complexity and emergence, and the relation between life, intelligence, and consciousness. Plus, Anil answers a handful of questions from Megan Peters and Steve Fleming, both previous brain inspired guests.
0:00 - Intro 6:32 - Megan Peters Q: Communicating Consciousness 15:58 - Human vs. animal consciousness 19:12 - BEING YOU A New Science of Consciousness 20:55 - Megan Peters Q: Will the hard problem go away? 30:55 - Steve Fleming Q: Contents of consciousness 41:01 - Megan Peters Q: Phenomenal character vs. content 43:46 - Megan Peters Q: Lempels of complexity 52:00 - Complex systems and emergence 55:53 - Psychedelics 1:06:04 - Free will 1:19:10 - Consciousness vs. life vs. intelligence | |||
| BI 116 Michael W. Cole: Empirical Neural Networks | 12 Oct 2021 | 01:31:20 | |
Support the show to get full episodes, full archive, and join the Discord community. Mike and I discuss his modeling approach to study cognition. Many people I have on the podcast use deep neural networks to study brains, where the idea is to train or optimize the model to perform a task, then compare the model properties with brain properties. Mike's approach is different in at least two ways. One, he builds the architecture of his models using connectivity data from fMRI recordings. Two, he doesn't train his models; instead, he uses functional connectivity data from the fMRI recordings to assign weights between nodes of the network (in deep learning, the weights are learned through lots of training). Mike calls his networks empirically-estimated neural networks (ENNs), and/or network coding models. We walk through his approach, what we can learn from models like ENNs, discuss some of his earlier work on cognitive control and our ability to flexibly adapt to new task rules through instruction, and he fields questions from Kanaka Rajan, Kendrick Kay, and Patryk Laurent.
0:00 - Intro 4:58 - Cognitive control 7:44 - Rapid Instructed Task Learning and Flexible Hub Theory 15:53 - Patryk Laurent question: free will 26:21 - Kendrick Kay question: fMRI limitations 31:55 - Empirically-estimated neural networks (ENNs) 40:51 - ENNs vs. deep learning 45:30 - Clinical relevance of ENNs 47:32 - Kanaka Rajan question: a proposed collaboration 56:38 - Advantage of modeling multiple regions 1:05:30 - How ENNs work 1:12:48 - How ENNs might benefit artificial intelligence 1:19:04 - The need for causality 1:24:38 - Importance of luck and serendipity | |||
| BI 115 Steve Grossberg: Conscious Mind, Resonant Brain | 02 Oct 2021 | 01:23:41 | |
Support the show to get full episodes, full archive, and join the Discord community. https://www.patreon.com/braininspiredSteve and I discuss his book Conscious Mind, Resonant Brain: How Each Brain Makes a Mind. The book is a huge collection of his models and their predictions and explanations for a wide array of cognitive brain functions. Many of the models spring from his Adaptive Resonance Theory (ART) framework, which explains how networks of neurons deal with changing environments while maintaining self-organization and retaining learned knowledge. ART led Steve to the hypothesis that all conscious states are resonant states, which we discuss. There are also guest questions from György Buzsáki, Jay McClelland, and John Krakauer.
0:00 - Intro 2:38 - Conscious Mind, Resonant Brain 11:49 - Theoretical method 15:54 - ART, learning, and consciousness 22:58 - Conscious vs. unconscious resonance 26:56 - Györy Buzsáki question 30:04 - Remaining mysteries in visual system 35:16 - John Krakauer question 39:12 - Jay McClelland question 51:34 - Any missing principles to explain human cognition? 1:00:16 - Importance of an early good career start 1:06:50 - Has modeling training caught up to experiment training? 1:17:12 - Universal development code | |||
| BI 114 Mark Sprevak and Mazviita Chirimuuta: Computation and the Mind | 22 Sep 2021 | 01:38:07 | |
Support the show to get full episodes, full archive, and join the Discord community. Mark and Mazviita discuss the philosophy and science of mind, and how to think about computations with respect to understanding minds. Current approaches to explaining brain function are dominated by computational models and the computer metaphor for brain and mind. But there are alternative ways to think about the relation between computations and brain function, which we explore in the discussion. We also talk about the role of philosophy broadly and with respect to mind sciences, pluralism and perspectival approaches to truth and understanding, the prospects and desirability of naturalizing representations (accounting for how brain representations relate to the natural world), and much more.
0:00 - Intro 5:26 - Philosophy contributing to mind science 15:45 - Trend toward hyperspecialization 21:38 - Practice-focused philosophy of science 30:42 - Computationalism 33:05 - Philosophy of mind: identity theory, functionalism 38:18 - Computations as descriptions 41:27 - Pluralism and perspectivalism 54:18 - How much of brain function is computation? 1:02:11 - AI as computationalism 1:13:28 - Naturalizing representations 1:30:08 - Are you doing it right? | |||
| BI 113 David Barack and John Krakauer: Two Views On Cognition | 12 Sep 2021 | 01:30:38 | |
Support the show to get full episodes, full archive, and join the Discord community. David and John discuss some of the concepts from their recent paper Two Views on the Cognitive Brain, in which they argue the recent population-based dynamical systems approach is a promising route to understanding brain activity underpinning higher cognition. We discuss mental representations, the kinds of dynamical objects being used for explanation, and much more, including David's perspectives as a practicing neuroscientist and philosopher.
Timestamps 0:00 - Intro 3:13 - David's philosophy and neuroscience experience 20:01 - Renaissance person 24:36 - John's medical training 31:58 - Two Views on the Cognitive Brain 44:18 - Representation 49:37 - Studying populations of neurons 1:05:17 - What counts as representation 1:18:49 - Does this approach matter for AI? | |||
| BI ViDA Panel Discussion: Deep RL and Dopamine | 02 Sep 2021 | 00:57:25 | |
| BI 209 Aran Nayebi: The NeuroAI Turing Test | 09 Apr 2025 | 01:43:59 | |
Support the show to get full episodes, full archive, and join the Discord community. https://www.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. Sign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released. To explore more neuroscience news and perspectives, visit thetransmitter.org. Aran Nayebi is an Assistant Professor at Carnegie Mellon University in the Machine Learning Department. He was there in the early days of using convolutional neural networks to explain how our brains perform object recognition, and since then he's a had a whirlwind trajectory through different AI architectures and algorithms and how they relate to biological architectures and algorithms, so we touch on some of what he has studied in that regard. But he also recently started his own lab, at CMU, and he has plans to integrate much of what he has learned to eventually develop autonomous agents that perform the tasks we want them to perform in similar at least ways that our brains perform them. So we discuss his ongoing plans to reverse-engineer our intelligence to build useful cognitive architectures of that sort. We also discuss Aran's suggestion that, at least in the NeuroAI world, the Turing test needs to be updated to include some measure of similarity of the internal representations used to achieve the various tasks the models perform. By internal representations, as we discuss, he means the population-level activity in the neural networks, not the mental representations philosophy of mind often refers to, or other philosophical notions of the term representation.
0:00 - Intro 5:24 - Background 20:46 - Building embodied agents 33:00 - Adaptability 49:25 - Marr's levels 54:12 - Sensorimotor loop and intrinsic goals 1:00:05 - NeuroAI Turing Test 1:18:18 - Representations 1:28:18 - How to know what to measure 1:32:56 - AI safety | |||
| BI 112 Ali Mohebi and Ben Engelhard: The Many Faces of Dopamine | 26 Aug 2021 | 01:13:56 | |
BI 112:
Ali Mohebi and Ben Engelhard
The Many Faces of Dopamine
Announcement:
Ben has started his new lab and is recruiting grad students. Check out his lab here and apply! Engelhard Labhttps://www.patreon.com/braininspired Ali and Ben discuss the ever-expanding discoveries about the roles dopamine plays for our cognition. Dopamine is known to play a role in learning – dopamine (DA) neurons fire when our reward expectations aren’t met, and that signal helps adjust our expectation. Roughly, DA corresponds to a reward prediction error. The reward prediction error has helped reinforcement learning in AI develop into a raging success, specially with deep reinforcement learning models trained to out-perform humans in games like chess and Go. But DA likely contributes a lot more to brain function. We discuss many of those possible roles, how to think about computation with respect to neuromodulators like DA, how different time and spatial scales interact, and more. Dopamine: A Simple AND Complex Story GuestsTimestamps: 0:00 – Intro 5:02 – Virtual Dopamine Conference 9:56 – History of dopamine’s roles 16:47 – Dopamine circuits 21:13 – Multiple roles for dopamine 31:43 – Deep learning panel discussion 50:14 – Computation and neuromodulation | |||
| BI NMA 06: Advancing Neuro Deep Learning Panel | 19 Aug 2021 | 01:20:32 | |
| BI NMA 05: NLP and Generative Models Panel | 13 Aug 2021 | 01:23:50 | |
BI NMA 05:
NLP and Generative Models Panel
https://www.patreon.com/braininspired
This is the 5th in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. This is the 2nd of 3 in the deep learning series. In this episode, the panelists discuss their experiences “doing more with fewer parameters: Convnets, RNNs, attention & transformers, generative models (VAEs & GANs). Panelists
The other panels:
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| BI NMA 04: Deep Learning Basics Panel | 06 Aug 2021 | 00:59:21 | |
BI NMA 04:
Deep Learning Basics Panel
https://www.patreon.com/braininspired
This is the 4th in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. This is the first of 3 in the deep learning series. In this episode, the panelists discuss their experiences with some basics in deep learning, including Linear deep learning, Pytorch, multi-layer-perceptrons, optimization, & regularization. Guests The other panels:
Timestamps:
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| BI 111 Kevin Mitchell and Erik Hoel: Agency, Emergence, Consciousness | 28 Jul 2021 | 01:38:04 | |
Erik, Kevin, and I discuss... well a lot of things. Erik's recent novel The Revelations is a story about a group of neuroscientists trying to develop a good theory of consciousness (with a murder mystery plot). Kevin's book Innate - How the Wiring of Our Brains Shapes Who We Are describes the messy process of getting from DNA, traversing epigenetics and development, to our personalities. We talk about both books, then dive deeper into topics like whether brains evolved for moving our bodies vs. consciousness, how information theory is lending insights to emergent phenomena, and the role of agency with respect to what counts as intelligence.
Timestamps 0:00 - Intro 3:28 - The Revelations - Erik's novel 15:15 - Innate - Kevin's book 22:56 - Cycle of progress 29:05 - Brains for movement or consciousness? 46:46 - Freud's influence 59:18 - Theories of consciousness 1:02:02 - Meaning and emergence 1:05:50 - Reduction in neuroscience 1:23:03 - Micro and macro - emergence 1:29:35 - Agency and intelligence | |||
| BI NMA 03: Stochastic Processes Panel | 22 Jul 2021 | 01:00:48 | |
Panelists: This is the third in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. In this episode, the panelists discuss their experiences with stochastic processes, including Bayes, decision-making, optimal control, reinforcement learning, and causality. The other panels:
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| BI NMA 02: Dynamical Systems Panel | 15 Jul 2021 | 01:15:28 | |
Panelists: This is the second in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. In this episode, the panelists discuss their experiences with linear systems, real neurons, and dynamic networks. Other panels:
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| BI NMA 01: Machine Learning Panel | 12 Jul 2021 | 01:27:12 | |
Panelists: This is the first in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. In this episode, the panelists discuss their experiences with model fitting, GLMs/machine learning, dimensionality reduction, and deep learning. Other panels:
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| BI 110 Catherine Stinson and Jessica Thompson: Neuro-AI Explanation | 06 Jul 2021 | 01:25:02 | |
Catherine, Jess, and I use some of the ideas from their recent papers to discuss how different types of explanations in neuroscience and AI could be unified into explanations of intelligence, natural or artificial. Catherine has written about how models are related to the target system they are built to explain. She suggests both the model and the target system should be considered as instantiations of a specific kind of phenomenon, and explanation is a product of relating the model and the target system to that specific aspect they both share. Jess has suggested we shift our focus of explanation from objects - like a brain area or a deep learning model - to the shared class of phenomenon performed by those objects. Doing so may help bridge the gap between the different forms of explanation currently used in neuroscience and AI. We also discuss Henk de Regt's conception of scientific understanding and its relation to explanation (they're different!), and plenty more.
Timestamps: 0:00 - Intro 11:11 - Background and approaches 27:00 - Understanding distinct from explanation 36:00 - Explanations as programs (early explanation) 40:42 - Explaining classes of phenomena 52:05 - Constitutive (neuro) vs. etiological (AI) explanations 1:04:04 - Do nonphysical objects count for explanation? 1:10:51 - Advice for early philosopher/scientists | |||
| BI 109 Mark Bickhard: Interactivism | 26 Jun 2021 | 02:03:43 | |
Mark and I discuss a wide range of topics surrounding his Interactivism framework for explaining cognition. Interactivism stems from Mark's account of representations and how what we represent in our minds is related to the external world - a challenge that has plagued the mind-body problem since the beginning. Basically, representations are anticipated interactions with the world, that can be true (if enacting one helps an organism maintain its thermodynamic relation with the world) or false (if it doesn't). And representations are functional, in that they function to maintain far from equilibrium thermodynamics for the organism for self-maintenance. Over the years, Mark has filled out Interactivism, starting with a process metaphysics foundation and building from there to account for representations, how our brains might implement representations, and why AI is hindered by our modern "encoding" version of representation. We also compare interactivism to other similar frameworks, like enactivism, predictive processing, and the free energy principle. For related discussions on the foundations (and issues of) representations, check out episode 60 with Michael Rescorla, episode 61 with Jörn Diedrichsen and Niko Kriegeskorte, and especially episode 79 with Romain Brette.
Timestamps 0:00 - Intro 5:06 - Previous and upcoming book 9:17 - Origins of Mark's thinking 14:31 - Process vs. substance metaphysics 27:10 - Kinds of emergence 32:16 - Normative emergence to normative function and representation 36:33 - Representation in Interactivism 46:07 - Situation knowledge 54:02 - Interactivism vs. Enactivism 1:09:37 - Interactivism vs Predictive/Bayesian brain 1:17:39 - Interactivism vs. Free energy principle 1:21:56 - Microgenesis 1:33:11 - Implications for neuroscience 1:38:18 - Learning as variation and selection 1:45:07 - Implications for AI 1:55:06 - Everything is a clock 1:58:14 - Is Mark a philosopher? | |||
| BI 208 Gabriele Scheler: From Verbal Thought to Neuron Computation | 26 Mar 2025 | 01:35:08 | |
Support the show to get full episodes, full archive, and join the Discord community. https://www.patreon.com/braininspiredGabriele Scheler co-founded the Carl Correns Foundation for Mathematical Biology. Carl Correns was her great grandfather, one of the early pioneers in genetics. Gabriele is a computational neuroscientist, whose goal is to build models of cellular computation, and much of her focus is on neurons. We discuss her theoretical work building a new kind of single neuron model. She, like Dmitri Chklovskii a few episodes ago, believes we've been stuck with essentially the same family of models for a neuron for a long time, despite minor variations on those models. The model Gabriele is working on, for example, respects the computations going on not only externally, via spiking, which has been the only game in town forever, but also the computations going on within the cell itself. Gabriele is in line with previous guests like Randy Gallistel, David Glanzman, and Hessam Akhlaghpour, who argue that we need to pay attention to how neurons are computing various things internally and how that affects our cognition. Gabriele also believes the new neuron model she's developing will improve AI, drastically simplifying the models by providing them with smarter neurons, essentially. We also discuss the importance of neuromodulation, her interest in wanting to understand how we think via our internal verbal monologue, her lifelong interest in language in general, what she thinks about LLMs, why she decided to start her own foundation to fund her science, what that experience has been like so far. Gabriele has been working on these topics for many years, and as you'll hear in a moment, she was there when computational neuroscience was just starting to pop up in a few places, when it was a nascent field, unlike its current ubiquity in neuroscience.
0:00 - Intro 4:41 - Gabriele's early interests in verbal thinking 14:14 - What is thinking? 24:04 - Starting one's own foundation 58:18 - Building a new single neuron model 1:19:25 - The right level of abstraction 1:25:00 - How a new neuron would change AI | |||
| BI 108 Grace Lindsay: Models of the Mind | 16 Jun 2021 | 01:26:12 | |
Grace and I discuss her new book Models of the Mind, about the blossoming and conceptual foundations of the computational approach to study minds and brains. Each chapter of the book focuses on one major topic and provides historical context, the major concepts that connect models to brain functions, and the current landscape of related research endeavors. We cover a handful of those during the episode, including the birth of AI, the difference between math in physics and neuroscience, determining the neural code and how Shannon information theory plays a role, whether it's possible to guess a brain function based on what we know about some brain structure, "grand unified theories" of the brain. We also digress and explore topics beyond the book. Timestamps 0:00 - Intro 4:19 - Cognition beyond vision 12:38 - Models of the Mind - book overview 14:00 - The good and bad of using math 21:33 - I quiz Grace on her own book 25:03 - Birth of AI and computational approach 38:00 - Rediscovering old math for new neuroscience 41:00 - Topology as good math to know now 45:29 - Physics vs. neuroscience math 49:32 - Neural code and information theory 55:03 - Rate code vs. timing code 59:18 - Graph theory - can you deduce function from structure? 1:06:56 - Multiple realizability 1:13:01 - Grand Unified theories of the brain | |||
| BI 107 Steve Fleming: Know Thyself | 06 Jun 2021 | 01:29:24 | |
Steve and I discuss many topics from his new book Know Thyself: The Science of Self-Awareness. The book covers the full range of what we know about metacognition and self-awareness, including how brains might underlie metacognitive behavior, computational models to explain mechanisms of metacognition, how and why self-awareness evolved, which animals beyond humans harbor metacognition and how to test it, its role and potential origins in theory of mind and social interaction, how our metacognitive skills develop over our lifetimes, what our metacognitive skill tells us about our other psychological traits, and so on. We also discuss what it might look like when we are able to build metacognitive AI, and whether that's even a good idea.
Timestamps 0:00 - Intro 3:25 - Steve's Career 10:43 - Sub-personal vs. personal metacognition 17:55 - Meditation and metacognition 20:51 - Replay tools for mind-wandering 30:56 - Evolutionary cultural origins of self-awareness 45:02 - Animal metacognition 54:25 - Aging and self-awareness 58:32 - Is more always better? 1:00:41 - Political dogmatism and overconfidence 1:08:56 - Reliance on AI 1:15:15 - Building self-aware AI 1:23:20 - Future evolution of metacognition | |||
| BI 106 Jacqueline Gottlieb and Robert Wilson: Deep Curiosity | 27 May 2021 | 01:31:53 | |
Jackie and Bob discuss their research and thinking about curiosity. Jackie's background is studying decision making and attention, recording neurons in nonhuman primates during eye movement tasks, and she's broadly interested in how we adapt our ongoing behavior. Curiosity is crucial for this, so she recently has focused on behavioral strategies to exercise curiosity, developing tasks that test exploration, information sampling, uncertainty reduction, and intrinsic motivation. Bob's background is developing computational models of reinforcement learning (including the exploration-exploitation tradeoff) and decision making, and he behavior and neuroimaging data in humans to test the models. He's broadly interested in how and whether we can understand brains and cognition using mathematical models. Recently he's been working on a model for curiosity known as deep exploration, which suggests we make decisions by deeply simulating a handful of scenarios and choosing based on the simulation outcomes. We also discuss how one should go about their career (qua curiosity), how eye movements compare with other windows into cognition, and whether we can and should create curious AI agents (Bob is an emphatic yes, and Jackie is slightly worried that will be the time to worry about AI).
Timestamps: 0:00 - Intro 4:15 - Central scientific interests 8:32 - Advent of mathematical models 12:15 - Career exploration vs. exploitation 28:03 - Eye movements and active sensing 35:53 - Status of eye movements in neuroscience 44:16 - Why are we curious? 50:26 - Curiosity vs. Exploration vs. Intrinsic motivation 1:02:35 - Directed vs. random exploration 1:06:16 - Deep exploration 1:12:52 - How to know what to pay attention to 1:19:49 - Does AI need curiosity? 1:26:29 - What trait do you wish you had more of? | |||
| BI 105 Sanjeev Arora: Off the Convex Path | 17 May 2021 | 01:01:43 | |
Sanjeev and I discuss some of the progress toward understanding how deep learning works, specially under previous assumptions it wouldn't or shouldn't work as well as it does. Deep learning theory poses a challenge for mathematics, because its methods aren't rooted in mathematical theory and therefore are a "black box" for math to open. We discuss how Sanjeev thinks optimization, the common framework for thinking of how deep nets learn, is the wrong approach. Instead, a promising alternative focuses on the learning trajectories that occur as a result of different learning algorithms. We discuss two examples of his research to illustrate this: creating deep nets with infinitely large layers (and the networks still find solutions among the infinite possible solutions!), and massively increasing the learning rate during training (the opposite of accepted wisdom, and yet, again, the network finds solutions!). We also discuss his past focus on computational complexity and how he doesn't share the current neuroscience optimism comparing brains to deep nets.
Timestamps 0:00 - Intro 7:32 - Computational complexity 12:25 - Algorithms 13:45 - Deep learning vs. traditional optimization 17:01 - Evolving view of deep learning 18:33 - Reproducibility crisis in AI? 21:12 - Surprising effectiveness of deep learning 27:50 - "Optimization" isn't the right framework 30:08 - Infinitely wide nets 35:41 - Exponential learning rates 42:39 - Data as the next frontier 44:12 - Neuroscience and AI differences 47:13 - Focus on algorithms, architecture, and objective functions 55:50 - Advice for deep learning theorists 58:05 - Decoding minds | |||
| BI 104 John Kounios and David Rosen: Creativity, Expertise, Insight | 07 May 2021 | 01:50:32 | |
What is creativity? How do we measure it? How do our brains implement it, and how might AI?Those are some of the questions John, David, and I discuss. The neuroscience of creativity is young, in its "wild west" days still. We talk about a few creativity studies they've performed that distinguish different creative processes with respect to different levels of expertise (in this case, in jazz improvisation), and the underlying brain circuits and activity, including using transcranial direct current stimulation to alter the creative process. Related to creativity, we also discuss the phenomenon and neuroscience of insight (the topic of John's book, The Eureka Factor), unconscious automatic type 1 processes versus conscious deliberate type 2 processes, states of flow, creative process versus creative products, and a lot more.
Timestamps 0:00 - Intro 16:20 - Where are we broadly in science of creativity? 18:23 - Origins of creativity research 22:14 - Divergent and convergent thought 26:31 - Secret Chord Labs 32:40 - Familiar surprise 38:55 - The Eureka Factor 42:27 - Dual process model 52:54 - Creativity and jazz expertise 55:53 - "Be creative" behavioral study 59:17 - Stimulating the creative brain 1:02:04 - Brain circuits underlying creativity 1:14:36 - What does this tell us about creativity? 1:16:48 - Intelligence vs. creativity 1:18:25 - Switching between creative modes 1:25:57 - Flow states and insight 1:34:29 - Creativity and insight in AI 1:43:26 - Creative products vs. process | |||
| BI 103 Randal Koene and Ken Hayworth: The Road to Mind Uploading | 26 Apr 2021 | 01:27:26 | |
Randal, Ken, and I discuss a host of topics around the future goal of uploading our minds into non-brain systems, to continue our mental lives and expand our range of experiences. The basic requirement for such a subtrate-independent mind is to implement whole brain emulation. We discuss two basic approaches to whole brain emulation. The "scan and copy" approach proposes we somehow scan the entire structure of our brains (at whatever scale is necessary) and store that scan until some future date when we have figured out how to us that information to build a substrate that can house your mind. The "gradual replacement" approach proposes we slowly replace parts of the brain with functioning alternative machines, eventually replacing the entire brain with non-biological material and yet retaining a functioning mind. Randal and Ken are neuroscientists who understand the magnitude and challenges of a massive project like mind uploading, who also understand what we can do right now, with current technology, to advance toward that lofty goal, and who are thoughtful about what steps we need to take to enable further advancements.
Timestamps 0:00 - Intro 6:14 - What Ken wants 11:22 - What Randal wants 22:29 - Brain preservation 27:18 - Aldehyde stabilized cryopreservation 31:51 - Scan and copy vs. gradual replacement 38:25 - Building a roadmap 49:45 - Limits of current experimental paradigms 53:51 - Our evolved brains 1:06:58 - Counterarguments 1:10:31 - Animal models for whole brain emulation 1:15:01 - Understanding vs. emulating brains 1:22:37 - Current challenges | |||
| BI 102 Mark Humphries: What Is It Like To Be A Spike? | 16 Apr 2021 | 01:32:20 | |
Mark and I discuss his book, The Spike: An Epic Journey Through the Brain in 2.1 Seconds. It chronicles how a series of action potentials fire through the brain in a couple seconds of someone's life. Starting with light hitting the retina as a person looks at a cookie, Mark describes how that light gets translated into spikes, how those spikes get processed in our visual system and eventually transform into motor commands to grab that cookie. Along the way, he describes some of the big ideas throughout the history of studying brains (like the mechanisms to explain how neurons seem to fire so randomly), the big mysteries we currently face (like why do so many neurons do so little?), and some of the main theories to explain those mysteries (we're prediction machines!). A fun read and discussion. This is Mark's second time on the podcast - he was on episode 4 in the early days, talking more in depth about some of the work we discuss in this episode!
Timestamps: 0:00 - Intro 3:25 - Writing a book 15:37 - Mark's main interest 19:41 - Future explanation of brain/mind 27:00 - Stochasticity and excitation/inhibition balance 36:56 - Dendritic computation for network dynamics 39:10 - Do details matter for AI? 44:06 - Spike failure 51:12 - Dark neurons 1:07:57 - Intrinsic spontaneous activity 1:16:16 - Best scientific moment 1:23:58 - Failure 1:28:45 - Advice | |||
| BI 101 Steve Potter: Motivating Brains In and Out of Dishes | 06 Apr 2021 | 01:45:22 | |
Steve and I discuss his book, How to Motivate Your Students to Love Learning, which is both a memoir and a guide for teachers and students to optimize the learning experience for intrinsic motivation. Steve taught neuroscience and engineering courses while running his own lab studying the activity of live cultured neural populations (which we discuss at length in his previous episode). He relentlessly tested and tweaked his teaching methods, including constant feedback from the students, to optimize their learning experiences. He settled on real-world, project-based learning approaches, like writing wikipedia articles and helping groups of students design and carry out their own experiments. We discuss that, plus the science behind learning, principles important for motivating students and maintaining that motivation, and many of the other valuable insights he shares in the book. The first half of the episode we discuss diverse neuroscience and AI topics, like brain organoids, mind-uploading, synaptic plasticity, and more. Then we discuss many of the stories and lessons from his book, which I recommend for teachers, mentors, and life-long students who want to ensure they're optimizing their own learning.
0:00 - Intro 6:38 - Brain organoids 18:48 - Glial cell plasticity 24:50 - Whole brain emulation 35:28 - Industry vs. academia 45:32 - Intro to book: How To Motivate Your Students To Love Learning 48:29 - Steve's childhood influences 57:21 - Developing one's own intrinsic motivation 1:02:30 - Real-world assignments 1:08:00 - Keys to motivation 1:11:50 - Peer pressure 1:21:16 - Autonomy 1:25:38 - Wikipedia real-world assignment 1:33:12 - Relation to running a lab | |||
| BI 100.6 Special: Do We Have the Right Vocabulary and Concepts? | 28 Mar 2021 | 00:50:03 | |
We made it to the last bit of our 100th episode celebration. These have been super fun for me, and I hope you've enjoyed the collections as well. If you're wondering where the missing 5th part is, I reserved it exclusively for Brain Inspired's magnificent Patreon supporters (thanks guys!!!!). The final question I sent to previous guests: Do we already have the right vocabulary and concepts to explain how brains and minds are related? Why or why not? Timestamps: 0:00 - Intro 5:04 - Andrew Saxe 7:04 - Thomas Naselaris 7:46 - John Krakauer 9:03 - Federico Turkheimer 11:57 - Steve Potter 13:31 - David Krakauer 17:22 - Dean Buonomano 20:28 - Konrad Kording 22:00 - Uri Hasson 23:15 - Rodrigo Quian Quiroga 24:41 - Jim DiCarlo 25:26 - Marcel van Gerven 28:02 - Mazviita Chirimuuta 29:27 - Brad Love 31:23 - Patrick Mayo 32:30 - György Buzsáki 37:07 - Pieter Roelfsema 37:26 - David Poeppel 40:22 - Paul Cisek 44:52 - Talia Konkle 47:03 - Steve Grossberg | |||
| BI 100.4 Special: What Ideas Are Holding Us Back? | 21 Mar 2021 | 01:04:26 | |
In the 4th installment of our 100th episode celebration, previous guests responded to the question: What ideas, assumptions, or terms do you think is holding back neuroscience/AI, and why? As usual, the responses are varied and wonderful! Timestamps: 0:00 - Intro 6:41 - Pieter Roelfsema 7:52 - Grace Lindsay 10:23 - Marcel van Gerven 11:38 - Andrew Saxe 14:05 - Jane Wang 16:50 - Thomas Naselaris 18:14 - Steve Potter 19:18 - Kendrick Kay 22:17 - Blake Richards 27:52 - Jay McClelland 30:13 - Jim DiCarlo 31:17 - Talia Konkle 33:27 - Uri Hasson 35:37 - Wolfgang Maass 38:48 - Paul Cisek 40:41 - Patrick Mayo 41:51 - Konrad Kording 43:22 - David Poeppel 44:22 - Brad Love 46:47 - Rodrigo Quian Quiroga 47:36 - Steve Grossberg 48:47 - Mark Humphries 52:35 - John Krakauer 55:13 - György Buzsáki 59:50 - Stefan Leijnan 1:02:18 - Nathaniel Daw | |||
| BI 207 Alison Preston: Schemas in our Brains and Minds | 12 Mar 2025 | 01:29:47 | |
Support the show to get full episodes, full archive, and join the Discord community. https://www.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. Sign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released. To explore more neuroscience news and perspectives, visit thetransmitter.org. The concept of a schema goes back at least to the philosopher Immanuel Kant in the 1700s, who use the term to refer to a kind of built-in mental framework to organize sensory experience. But it was the psychologist Frederic Bartlett in the 1930s who used the term schema in a psychological sense, to explain how our memories are organized and how new information gets integrated into our memory. Fast forward another 100 years to today, and we have a podcast episode with my guest today, Alison Preston, who runs the Preston Lab at the University of Texas at Austin. On this episode, we discuss her neuroscience research explaining how our brains might carry out the processing that fits with our modern conception of schemas, and how our brains do that in different ways as we develop from childhood to adulthood. I just said, "our modern conception of schemas," but like everything else, there isn't complete consensus among scientists exactly how to define schema. Ali has her own definition. She shares that, and how it differs from other conceptions commonly used. I like Ali's version and think it should be adopted, in part because it helps distinguish schemas from a related term, cognitive maps, which we've discussed aplenty on brain inspired, and can sometimes be used interchangeably with schemas. So we discuss how to think about schemas versus cognitive maps, versus concepts, versus semantic information, and so on. Last episode Ciara Greene discussed schemas and how they underlie our memories, and learning, and predictions, and how they can lead to inaccurate memories and predictions. Today Ali explains how circuits in the brain might adaptively underlie this process as we develop, and how to go about measuring it in the first place.
Read the transcript. 0:00 - Intro 6:51 - Schemas 20:37 - Schemas and the developing brain 35:03 - Information theory, dimensionality, and detail 41:17 - Geometry of schemas 47:26 - Schemas and creativity 50:29 - Brain connection pruning with development 1:02:46 - Information in brains 1:09:20 - Schemas and development in AI | |||
| BI 100.3 Special: Can We Scale Up to AGI with Current Tech? | 17 Mar 2021 | 01:08:43 | |
Part 3 in our 100th episode celebration. Previous guests answered the question: Given the continual surprising progress in AI powered by scaling up parameters and using more compute, while using fairly generic architectures (eg. GPT-3): Do you think the current trend of scaling compute can lead to human level AGI? If not, what's missing? It likely won't surprise you that the vast majority answer "No." It also likely won't surprise you, there is differing opinion on what's missing. Timestamps: 0:00 - Intro 3:56 - Wolgang Maass 5:34 - Paul Humphreys 9:16 - Chris Eliasmith 12:52 - Andrew Saxe 16:25 - Mazviita Chirimuuta 18:11 - Steve Potter 19:21 - Blake Richards 22:33 - Paul Cisek 26:24 - Brad Love 29:12 - Jay McClelland 34:20 - Megan Peters 37:00 - Dean Buonomano 39:48 - Talia Konkle 40:36 - Steve Grossberg 42:40 - Nathaniel Daw 44:02 - Marcel van Gerven 45:28 - Kanaka Rajan 48:25 - John Krakauer 51:05 - Rodrigo Quian Quiroga 53:03 - Grace Lindsay 55:13 - Konrad Kording 57:30 - Jeff Hawkins 102:12 - Uri Hasson 1:04:08 - Jess Hamrick 1:06:20 - Thomas Naselaris | |||
| BI 100.2 Special: What Are the Biggest Challenges and Disagreements? | 12 Mar 2021 | 01:25:00 | |
In this 2nd special 100th episode installment, many previous guests answer the question: What is currently the most important disagreement or challenge in neuroscience and/or AI, and what do you think the right answer or direction is? The variety of answers is itself revealing, and highlights how many interesting problems there are to work on. Timestamps: 0:00 - Intro 7:10 - Rodrigo Quian Quiroga 8:33 - Mazviita Chirimuuta 9:15 - Chris Eliasmith 12:50 - Jim DiCarlo 13:23 - Paul Cisek 16:42 - Nathaniel Daw 17:58 - Jessica Hamrick 19:07 - Russ Poldrack 20:47 - Pieter Roelfsema 22:21 - Konrad Kording 25:16 - Matt Smith 27:55 - Rafal Bogacz 29:17 - John Krakauer 30:47 - Marcel van Gerven 31:49 - György Buzsáki 35:38 - Thomas Naselaris 36:55 - Steve Grossberg 48:32 - David Poeppel 49:24 - Patrick Mayo 50:31 - Stefan Leijnen 54:24 - David Krakuer 58:13 - Wolfang Maass 59:13 - Uri Hasson 59:50 - Steve Potter 1:01:50 - Talia Konkle 1:04:30 - Matt Botvinick 1:06:36 - Brad Love 1:09:46 - Jon Brennan 1:19:31 - Grace Lindsay 1:22:28 - Andrew Saxe | |||
| BI 100.1 Special: What Has Improved Your Career or Well-being? | 09 Mar 2021 | 00:42:32 | |
Brain Inspired turns 100 (episodes) today! To celebrate, my patreon supporters helped me create a list of questions to ask my previous guests, many of whom contributed by answering any or all of the questions. I've collected all their responses into separate little episodes, one for each question. Starting with a light-hearted (but quite valuable) one, this episode has responses to the question, "In the last five years, what new belief, behavior, or habit has most improved your career or well being?" See below for links to each previous guest. And away we go... Timestamps: 0:00 - Intro 6:13 - David Krakauer 8:50 - David Poeppel 9:32 - Jay McClelland 11:03 - Patrick Mayo 11:45 - Marcel van Gerven 12:11 - Blake Richards 12:25 - John Krakauer 14:22 - Nicole Rust 15:26 - Megan Peters 17:03 - Andrew Saxe 18:11 - Federico Turkheimer 20:03 - Rodrigo Quian Quiroga 22:03 - Thomas Naselaris 23:09 - Steve Potter 24:37 - Brad Love 27:18 - Steve Grossberg 29:04 - Talia Konkle 29:58 - Paul Cisek 32:28 - Kanaka Rajan 34:33 - Grace Lindsay 35:40 - Konrad Kording 36:30 - Mark Humphries | |||
| Quick Announcement: Complexity Group | 05 Mar 2025 | 00:06:47 | |
Here's the link to learn more and sign up: Complexity Group Email List. | |||
| BI 206 Ciara Greene: Memories Are Useful, Not Accurate | 26 Feb 2025 | 01:29:10 | |
Support the show to get full episodes, full archive, and join the Discord community. https://www.patreon.com/braininspiredCiara 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.
Read the transcript. 0:00 - Intro 5:35 - The function of memory 6:41 - Reconstructive nature of memory 13:50 - Memory schemas, highly superior autobiographical memory 20:49 - Misremembering and flashbulb memories 27:52 - Forgetting and schemas 36:06 - What is a "good" memory? 39:35 - Memories and intention 43:47 - Memory and context 49:55 - Implanting false memories 1:04:10 - Memory suggestion during interrogations 1:06:30 - Memory, imagination, and creativity 1:13:45 - Artificial intelligence and memory 1:21:21 - Driven by questions | |||
| BI 205 Dmitri Chklovskii: Neurons Are Smarter Than You Think | 12 Feb 2025 | 01:39:05 | |
Support the show to get full episodes, full archive, and join the Discord community. https://www.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. Sign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: To explore more neuroscience news and perspectives, visit thetransmitter.org. Since the 1940s and 50s, back at the origins of what we now think of as artificial intelligence, there have been lots of ways of conceiving what it is that brains do, or what the function of the brain is. One of those conceptions, going to back to cybernetics, is that the brain is a controller that operates under the principles of feedback control. This view has been carried down in various forms to us in present day. Also since that same time period, when McCulloch and Pitts suggested that single neurons are logical devices, there have been lots of ways of conceiving what it is that single neurons do. Are they logical operators, do they each represent something special, are they trying to maximize efficiency, for example? Dmitri Chklovskii, who goes by Mitya, runs the Neural Circuits and Algorithms lab at the Flatiron Institute. Mitya believes that single neurons themselves are each individual controllers. They're smart agents, each trying to predict their inputs, like in predictive processing, but also functioning as an optimal feedback controller. We talk about historical conceptions of the function of single neurons and how this differs, we talk about how to think of single neurons versus populations of neurons, some of the neuroscience findings that seem to support Mitya's account, the control algorithm that simplifies the neuron's otherwise impossible control task, and other various topics. We also discuss Mitya's early interests, coming from a physics and engineering background, in how to wire up our brains efficiently, given the limited amount of space in our craniums. Obviously evolution produced its own solutions for this problem. This pursuit led Mitya to study the C. elegans worm, because its connectome was nearly complete- actually, Mitya and his team helped complete the connectome so he'd have the whole wiring diagram to study it. So we talk about that work, and what knowing the whole connectome of C. elegans has and has not taught us about how brains work.
Read the transcript. 0:00 - Intro 7:34 - Physicists approach for neuroscience 12:39 - What's missing in AI and neuroscience? 16:36 - Connectomes 31:51 - Understanding complex systems 33:17 - Earliest models of neurons 39:08 - Smart neurons 42:56 - Neuron theories that influenced Mitya 46:50 - Neuron as a controller 55:03 - How to test the neuron as controller hypothesis 1:00:29 - Direct data-driven control 1:11:09 - Experimental evidence 1:22:25 - Single neuron doctrine and population doctrine 1:25:30 - Neurons as agents 1:28:52 - Implications for AI 1:30:02 - Limits to control perspective | |||
| BI 204 David Robbe: Your Brain Doesn’t Measure Time | 29 Jan 2025 | 01:37:37 | |
Support the show to get full episodes, full archive, and join the Discord community. https://www.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. Sign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: To explore more neuroscience news and perspectives, visit thetransmitter.org. 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 - Intro 3:59 - Why behavior is so important in itself 10:27 - Henri Bergson 21:17 - Bergson's view of life 26:25 - A task to test how animals time things 34:08 - Back to Bergson and duree 39:44 - Externalizing time 44:11 - Internal representation of time 1:03:38 - Cognition as internal movement 1:09:14 - Free will 1:15:27 - Implications for AI | |||
| BI 203 David Krakauer: How To Think Like a Complexity Scientist | 14 Jan 2025 | 01:46:03 | |
Support the show to get full episodes, full archive, and join the Discord community. https://www.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. Sign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released. David Krakauer is the president of the Santa Fe Institute, where their mission is officially "Searching for Order in the Complexity of Evolving Worlds." When I think of the Santa Fe institute, I think of complexity science, because that is the common thread across the many subjects people study at SFI, like societies, economies, brains, machines, and evolution. David has been on before, and I invited him back to discuss some of the topics in his new book The Complex World: An Introduction to the Fundamentals of Complexity Science. The book on the one hand serves as an introduction and a guide to a 4 volume collection of foundational papers in complexity science, which you'll David discuss in a moment. On the other hand, The Complex World became much more, discussing and connecting ideas across the history of complexity science. Where did complexity science come from? How does it fit among other scientific paradigms? How did the breakthroughs come about? Along the way, we discuss the four pillars of complexity science - entropy, evolution, dynamics, and computation, and how complexity scientists draw from these four areas to study what David calls "problem-solving matter." We discuss emergence, the role of time scales, and plenty more all with my own self-serving goal to learn and practice how to think like a complexity scientist to improve my own work on how brains do things. Hopefully our conversation, and David's book, help you do the same.
Read the transcript. 0:00 - Intro 3:45 - Origins of The Complex World 20:10 - 4 pillars of complexity 36:27 - 40s to 70s in complexity 42:33 - How to proceed as a complexity scientist 54:32 - Broken symmetries 1:02:40 - Emergence 1:13:25 - Time scales and complexity 1:18:48 - Consensus and how ideas migrate 1:29:25 - Disciplinary matrix (Kuhn) 1:32:45 - Intelligence vs. life | |||
| BI 202 Eli Sennesh: Divide-and-Conquer to Predict | 03 Jan 2025 | 01:38:11 | |
Support the show to get full episodes, full archive, and join the Discord community. https://www.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. Sign up for the “Brain Inspired” email alerts to be notified every time a new Brain Inspired episode is released. Eli Sennesh is a postdoc at Vanderbilt University, one of my old stomping grounds, currently in the lab of Andre Bastos. Andre’s lab focuses on understanding brain dynamics within cortical circuits, particularly how communication between brain areas is coordinated in perception, cognition, and behavior. So Eli is busy doing work along those lines, as you'll hear more about. But the original impetus for having him on his recently published proposal for how predictive coding might be implemented in brains. So in that sense, this episode builds on the last episode with Rajesh Rao, where we discussed Raj's "active predictive coding" account of predictive coding. As a super brief refresher, predictive coding is the proposal that the brain is constantly predicting what's about the happen, then stuff happens, and the brain uses the mismatch between its predictions and the actual stuff that's happening, to learn how to make better predictions moving forward. I refer you to the previous episode for more details. So Eli's account, along with his co-authors of course, which he calls "divide-and-conquer" predictive coding, uses a probabilistic approach in an attempt to account for how brains might implement predictive coding, and you'll learn more about that in our discussion. But we also talk quite a bit about the difference between practicing theoretical and experimental neuroscience, and Eli's experience moving into the experimental side from the theoretical side.
Read the transcript. 0:00 - Intro 3:59 - Eli's worldview 17:56 - NeuroAI is hard 24:38 - Prediction errors vs surprise 55:16 - Divide and conquer 1:13:24 - Challenges 1:18:44 - How to build AI 1:25:56 - Affect 1:31:55 - Abolish the value function | |||
| BI 218 Chris Rozell: Brain Stimulation and AI for Mental Disorders | 13 Aug 2025 | 01:46:39 | |
Support the show to get full episodes, full archive, and join the Discord community. https://www.patreon.com/braininspiredWe are in an exciting time in the cross-fertilization of the neurotech industry and the cognitive sciences. My guest today is Chris Rozell, who sits in that space that connects neurotech and brain research. Chris runs the Structured Information for Precision Neuroengineering Lab at Georgia Tech University, and he was just named the inaugural director of Georgia Tech’s Institute for Neuroscience, Neurotechnology, and Society. I think this is the first time on brain inspired we've discussed stimulating brains to treat mental disorders. I think. Today we talk about Chris's work establishing a biomarker from brain recordings of patients with treatment resistant depression, a specific form of depression. These are patients who have deep brain stimulation electrodes implanted in an effort to treat their depression. Chris and his team used that stimulation in conjunction with brain recordings and machine learning tools to predict how effective the treatment will be under what circumstances, and so on, to help psychiatrists better treat their patients. We'll get into the details and surrounding issues. Toward the end we also talk about Chris's unique background and path and approach, and why he thinks interdisciplinary research is so important. He's one of the most genuinely well intentioned people I've met, and I hope you're inspired by his research and his story.
0:00 - Intro 3:20 - Overview of the study 17:11 - Closed and open loop stimulation 19:34 - Predicting recovery 28:45 - Control knob for treatment 39:04 - Historical and modern brain stimulation 49:07 - Treatment resistant depression 53:44 - Control nodes complex systems 1:01:06 - Explainable generative AI for a biomarker 1:16:40 - Where are we and what are the obstacles? 1:21:32 - Interface Neuro 1:24:55 - Why Chris cares Read the transcript. | |||
| BI 201 Rajesh Rao: From Predictive Coding to Brain Co-Processors | 18 Dec 2024 | 01:37:22 | |
Support the show to get full episodes, full archive, and join the Discord community. https://www.patreon.com/braininspiredToday 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.
Read the transcript. 0:00 - Intro 7:40 - Predictive coding origins 16:14 - Early appreciation of recurrence 17:08 - Prediction as a general theory of the brain 18:38 - Rao and Ballard 1999 26:32 - Prediction as a general theory of the brain 33:24 - Perception vs action 33:28 - Active predictive coding 45:04 - Evolving to augment our brains 53:03 - BrainNet 57:12 - Neural co-processors 1:11:19 - Decoding the Indus Script 1:20:18 - Transformer models relation to active predictive coding | |||
| BI 200 Grace Hwang and Joe Monaco: The Future of NeuroAI | 04 Dec 2024 | 01:37:11 | |
Support the show to get full episodes, full archive, and join the Discord community. https://www.patreon.com/braininspiredJoe Monaco and Grace Hwang co-organized a recent workshop I participated in, the 2024 BRAIN NeuroAI Workshop. You may have heard of the BRAIN Initiative, but in case not, BRAIN is is huge funding effort across many agencies, one of which is the National Institutes of Health, where this recent workshop was held. The BRAIN Initiative began in 2013 under the Obama administration, with the goal to support developing technologies to help understand the human brain, so we can cure brain based diseases. BRAIN Initiative just became a decade old, with many successes like recent whole brain connectomes, and discovering the vast array of cell types. Now the question is how to move forward, and one area they are curious about, that perhaps has a lot of potential to support their mission, is the recent convergence of neuroscience and AI... or NeuroAI. The workshop was designed to explore how NeuroAI might contribute moving forward, and to hear from NeuroAI folks how they envision the field moving forward. You'll hear more about that in a moment. That's one reason I invited Grace and Joe on. Another reason is because they co-wrote a position paper a while back that is impressive as a synthesis of lots of cognitive sciences concepts, but also proposes a specific level of abstraction and scale in brain processes that may serve as a base layer for computation. The paper is called Neurodynamical Computing at the Information Boundaries, of Intelligent Systems, and you'll learn more about that in this episode.
Read the transcript. 0:00 - Intro 25:45 - NeuroAI Workshop - neuromorphics 33:31 - Neuromorphics and theory 49:19 - Reflections on the workshop 54:22 - Neurodynamical computing and information boundaries 1:01:04 - Perceptual control theory 1:08:56 - Digital twins and neural foundation models 1:14:02 - Base layer of computation | |||
| BI 199 Hessam Akhlaghpour: Natural Universal Computation | 26 Nov 2024 | 01:49:07 | |
Support the show to get full episodes, full archive, and join the Discord community. https://www.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. Sign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: https://www.thetransmitter.org/newsletters/ To explore more neuroscience news and perspectives, visit thetransmitter.org. Hessam Akhlaghpour is a postdoctoral researcher at Rockefeller University in the Maimon lab. His experimental work is in fly neuroscience mostly studying spatial memories in fruit flies. However, we are going to be talking about a different (although somewhat related) side of his postdoctoral research. This aspect of his work involves theoretical explorations of molecular computation, which are deeply inspired by Randy Gallistel and Adam King's book Memory and the Computational Brain. Randy has been on the podcast before to discuss his ideas that memory needs to be stored in something more stable than the synapses between neurons, and how that something could be genetic material like RNA. When Hessam read this book, he was re-inspired to think of the brain the way he used to think of it before experimental neuroscience challenged his views. It re-inspired him to think of the brain as a computational system. But it also led to what we discuss today, the idea that RNA has the capacity for universal computation, and Hessam's development of how that might happen. So we discuss that background and story, why universal computation has been discovered in organisms yet since surely evolution has stumbled upon it, and how RNA might and combinatory logic could implement universal computation in nature.
Read the transcript. 0:00 - Intro 4:44 - Hessam's background 11:50 - Randy Gallistel's book 14:43 - Information in the brain 17:51 - Hessam's turn to universal computation 35:30 - AI and universal computation 40:09 - Universal computation to solve intelligence 44:22 - Connecting sub and super molecular 50:10 - Junk DNA 56:42 - Genetic material for coding 1:06:37 - RNA and combinatory logic 1:35:14 - Outlook 1:42:11 - Reflecting on the molecular world | |||
| BI 198 Tony Zador: Neuroscience Principles to Improve AI | 11 Nov 2024 | 01:35:04 | |
Support the show to get full episodes, full archive, and join the Discord community. https://www.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. Sign up for the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: https://www.thetransmitter.org/newsletters/ To explore more neuroscience news and perspectives, visit thetransmitter.org. Tony Zador runs the Zador lab at Cold Spring Harbor Laboratory. You've heard him on Brain Inspired a few times in the past, most recently in a panel discussion I moderated at this past COSYNE conference - a conference Tony co-founded 20 years ago. As you'll hear, Tony's current and past interests and research endeavors are of a wide variety, but today we focus mostly on his thoughts on NeuroAI. We're in a huge AI hype cycle right now, for good reason, and there's a lot of talk in the neuroscience world about whether neuroscience has anything of value to provide AI engineers - and how much value, if any, neuroscience has provided in the past. Tony is team neuroscience. You'll hear him discuss why in this episode, especially when it comes to ways in which development and evolution might inspire better data efficiency, looking to animals in general to understand how they coordinate numerous objective functions to achieve their intelligent behaviors - something Tony calls alignment - and using spikes in AI models to increase energy efficiency.
Read the transcript. 0:00 - Intro 3:28 - "Neuro-AI" 12:48 - Visual cognition history 18:24 - Information theory in neuroscience 20:47 - Necessary steps for progress 24:34 - Neuro-AI models and cognition 35:47 - Animals for inspiring AI 41:48 - What we want AI to do 46:01 - Development and AI 59:03 - Robots 1:25:10 - Catalyzing the next generation of AI | |||