Brain Inspired

Paul Middlebrooks
  • 5.0 (3)
  • NATURAL SCIENCES
  • TWICE MONTHLY

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

  1. 1 DAY AGO

    BI 222 Nikolay Kukushkin: Minds and Meaning from Nature’s Ideas

    Support the show to get full episodes, full archive, and join the Discord community. The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists. Read more about our partnership. Sign up for Brain Inspired email alerts to be notified every time a new Brain Inspired episode is released. To explore more neuroscience news and perspectives, visit thetransmitter.org. Nikolay Kukushkin is an associate professor at New York University, and a senior scientist at Thomas Carew’s laboratory at the Center for Neural Science. He describes himself as a "molecular philosopher", owing to his day job as a molecular biologist and his broad perspective on how it "hangs together", in the words of Wilfrid Sellers, who in 1962 wrote, “The aim of philosophy, abstractly formulated, is to understand how things in the broadest possible sense of the term hang together in the broadest possible sense of the term”. That is what Niko does in his book One Hand Clapping: Unraveling the Mystery of the Human Mind. This book is about essences across spatial scales in nature. More precisely, it's about giving names to what is fundamental, or essential, to how things and processes function in nature. Niko argues those essences are where meaning resides. That's very abstract, and we'll spell it out more during the discussion. But as an example at the small scale, the essences of carbon and oxygen, respectively, are creation and destruction, which allows metabolism to occur in biological organisms. Moving way up the scale, following this essence perspective leads Niko to the conclusion that there is no separation between our minds and the world, and that instead we should embrace the relational aspect of mind and world as a unifying principle. On the way, via evolution, we discuss many more examples, plus some of his own work studying how memory works in individual cells, not just neurons or populations of neurons in brains. Niko's website. Twitter: @niko_kukushkin. Book: One Hand Clapping: Unraveling the Mystery of the Human Mind Read the transcript. 0:00 - Intro 9:28 - Studying memory in cells 10:14 - Who the book is for 17:57 - Studying memory in cells 21:53 - What is memory? 29:49 - Book 29:52 - How the book came about 37:56 - Central message of the book 44:07 - Meaning in nature 49:09 - Meaning and essence 51:55 - Multicellularity and ant colonies 57:43 - Eukaryotes and complexification 1:03:38 - Why do we have brains? 1:06:17 - Emergence 1:10:58 - Language 1:12:41 - Human evolution 1:14:41 - Artificial intelligence, meaning and essences 1:25:49 - Consciousness

    1h 28m

  2. 24 SEPT

    BI 221 Ann Kennedy: Theory Beneath the Cortical Surface

    Support the show to get full episodes, full archive, and join the Discord community. Ann Kennedy is Associate Professor at Scripps Research Institute and runs the Laboratory for Theoretical Neuroscience and Behavior. Among other things, Ann has been studying how processes important in life, like survival, threat response, motivation, and pain, are mediated through subcortical brain areas like the hypothalamus. She also pays attention to the time course those life processes require, which has led her to consider how the expression of things like proteins help shape neural processes throughout the brain, so we can behave appropriately in those different contexts. You'll hear us talk about how this is still a pretty open field in theoretical neuroscience, unlike the historically heavy use of theory in popular brain areas throughout the cortex, and the historically narrow focus on spikes or action potentials as the only game in town when it comes to neural computation. We discuss that and I link in the show notes to a commentary piece Ann wrote, in which she argues for both top-down and bottom-up theoretical approaches. I also link to her papers about the early evolution of nervous systems, how heterogeneity or diversity of neurons is an advantage for neural computations, and we discuss a kaggle competition she developed to benchmark automated behavioral labels of behaving organisms, so that despite different researchers using different recording systems and setups, analyzing those data will produce consistent labels to better compare across labs and aggregated bigger and better data sets. Laboratory for Theoretical Neuroscience and Behavior. Social: @antihebbiann.bsky.social @Antihebbiann  The Kaggle competition Ann developed to generalize behavior categorization. Related papersDynamics of neural activity in early nervous system evolution.Theoretical neuroscience has room to grow. Neural heterogeneity controls computations in spiking neural networks. A parabrachial hub for the prioritization of survival behavior. An approximate line attractor in the hypothalamus encodes an aggressive state. Read the transcript. 0:00 - Intro 3:36 - Why study subcortical areas? 13:30 - Evolution 15:06 - Dynamical systems and time scales 21:32 - NeuroAI 28:37 - Before there were brains 33:11 - Endogenous spontaneous activity 40:09 - Natural vs artificial 43:09 - Different is more - heterogeneity 45:32 - Neuromodulators and neuropeptide functions 55:47 - Heterogeneity: manifolds, subspaces, and gain 1:02:43 - Control knobs 1:09:45 - Theoretical neuroscience has room to grow 1:19:59 - Hypothalamus 1:20:57 - Subcortical vs "higher" cognition 1:24:53 - 4E cognition 1:26:56 - Behavior benchmarking 1:37:26 - Current challenges 1:39:46 - Advice to young researchers

    1h 44m

  3. 10 SEPT

    BI 220 Michael Breakspear and Mac Shine: Dynamic Systems from Neurons to Brains

    Support the show to get full episodes, full archive, and join the Discord community. The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists. Read more about our partnership: 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. Breakspear: Systems Neuroscience Group. @DrBreaky. Shine: Shine Lab. @jmacshine. Related papers Dynamic models of large-scale brain activity Metastable brain waves The modulation of neural gain facilitates a transition between functional segregation and integration in the brain Multiscale Organization of Neuronal Activity Unifies Scale-Dependent Theories of Brain Function. The brain that controls itself. Metastability demystified — the foundational past, the pragmatic present and the promising future. Generation of surrogate brain maps preserving spatial autocorrelation through random rotation of geometric eigenmodes. Related episodes BI 212 John Beggs: Why Brains Seek the Edge of Chaos BI 216 Woodrow Shew and Keith Hengen: The Nature of Brain Criticality BI 121 Mac Shine: Systems Neurobiology Read the transcript. 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

    1h 25m

  4. 27 AUG

    BI 219 Xaq Pitkow: Principles and Constraints of Cognition

    Support the show to get full episodes, full archive, and join the Discord community. The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists. Read more about our partnership. Sign up for Brain Inspired email alerts to be notified every time a new Brain Inspired episode is released. To explore more neuroscience news and perspectives, visit thetransmitter.org. 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: How when we present tasks for organisms to solve, they use strategies that are suboptimal relative to the task, but nearly optimal relative to their beliefs about what they need to do - something Xaq calls inverse rational control. Probabilistic graph networks. How brains use probabilities to compute. A new ecological neuroscience project Xaq has started with multiple collaborators. LAB: Lab for the Algorithmic Brain. Related papers How does the brain compute with probabilities? Rational thoughts in neural codes. Control when confidence is costly Generalization of graph network inferences in higher-order graphical models. Attention when you need. 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

    1h 47m

  5. 13 AUG

    BI 218 Chris Rozell: Brain Stimulation and AI for Mental Disorders

    Support the show to get full episodes, full archive, and join the Discord community. We 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. Structured Information for Precision Neuroengineering Lab. Twitter: @crozSciTech. Related papers Cingulate dynamics track depression recovery with deep brain stimulation. Story Collider: Wired Lives 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.

    1h 47m

  6. 30 JUL

    BI 217 Jennifer Prendki: Consciousness, Life, AI, and Quantum Physics

    Support the show to get full episodes, full archive, and join the Discord community. The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists. Read more about our partnership. Sign up for Brain Inspired email alerts to be notified every time a new Brain Inspired episode is released. To explore more neuroscience news and perspectives, visit thetransmitter.org. 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 race Jennifer's website: Quantum of Data. The blog posts we discuss: The Myth of Emergence Embodiment & Sentience: Why the Body still Matters The Architecture of Synthetic Consciousness On Time and Consciousness Superalignment and the Question of AI Personhood. Read the transcript. 0:00 - Intro 3:25 - Jennifer's background 13:10 - Consciousness 16:38 - Life and consciousness 23:16 - Superalignment 40:11 - Quantum 1:04:45 - Wetware and biological mimicry 1:15:03 - Neural interfaces 1:16:48 - AI ethics 1:2:35 - AI models are not models 1:27:13 - What scaling will get us 1:39:53 - Current roadblocks 1:43:19 - Philosophy

    1h 49m

  7. 16 JUL

    BI 216 Woodrow Shew and Keith Hengen: The Nature of Brain Criticality

    Support the show to get full episodes, full archive, and join the Discord community. The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists. Read more about our partnership. Sign up for Brain Inspired email alerts to be notified every time a new Brain Inspired episode is released. To explore more neuroscience news and perspectives, visit thetransmitter.org. 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. Today 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 associate 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. Shew Lab. @ShewLab Hengen Lab. Is criticality a unified setpoint of brain function? Read the transcript. 0:00 - Intro 3:41 - Collaborating 6:22 - Criticality community 14:47 - Tasks vs. Naturalistic 20:50 - Nature of criticality 25:47 - Deviating from criticality 33:45 - Sleep for criticality 38:41 - Neuromodulation for criticality 40:45 - Criticality Definition part 1: scale invariance 43:14 - Criticality Definition part 2: At a boundary 51:56 - New method to assess criticality 56:12 - Types of criticality 1:02:23 - Value of criticality versus other metrics 1:15:21 - Manifolds and criticality 1:26:06 - Current challenges

    1h 34m

  8. 2 JUL

    BI 215 Xiao-Jing Wang: Theoretical Neuroscience Comes of Age

    Support the show to get full episodes, full archive, and join the Discord community. The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists. Read more about our partnership. Sign up for Brain Inspired email alerts to be notified every time a new Brain Inspired episode is released. To explore more neuroscience news and perspectives, visit thetransmitter.org. Xiao-Jing Wang is a Distinguished Global Professor of Neuroscience at NYU Xiao-Jing was born and grew up in China, spent 8 years in Belgium studying theoretical physics like nonlinear dynamical systems and deterministic chaos. And as he says it, he arrived from Brussels to California as a postdoc, and in one day switched from French to English, from European to American culture, and physics to neuroscience. I know Xiao-Jing as a legend in non-human primate neurophysiology and modeling, paving the way for the rest of us to study brain activity related cognitive functions like working memory and decision-making. He has just released his new textbook, Theoretical Neuroscience: Understanding Cognition, which covers the history and current research on modeling cognitive functions from the very simple to the very cognitive. The book is also somewhat philosophical, arguing that we need to update our approach to explaining how brains function, to go beyond Marr's levels and enter a cross-level mechanistic explanatory pursuit, which we discuss. I just learned he even cites my own PhD research, studying metacognition in nonhuman primates - so you know it's a great book. Learn more about Xiao-Jing and the book in the show notes. It was fun having one of my heroes on the podcast, and I hope you enjoy our discussion. Computational Laboratory of Cortical Dynamics Book: Theoretical Neuroscience: Understanding Cognition. Related papers Division of labor among distinct subtypes of inhibitory neurons in a cortical microcircuit of working memory. Macroscopic gradients of synaptic excitation and inhibition across the neocortex. Theory of the multiregional neocortex: large-scale neural dynamics and distributed cognition. 0:00 - Intro 3:08 - Why the book now? 11:00 - Modularity in neuro vs AI 14:01 - Working memory and modularity 22:37 - Canonical cortical microcircuits 25:53 - Gradient of inhibitory neurons 27:47 - Comp neuro then and now 45:35 - Cross-level mechanistic understanding 1:13:38 - Bifurcation 1:24:51 - Bifurcation and degeneracy 1:34:02 - Control theory 1:35:41 - Psychiatric disorders 1:39:14 - Beyond dynamical systems 1:43:447 - Mouse as a model 1:48:11 - AI needs a PFC

    1h 52m
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Ratings & Reviews

5
out of 5
3 Ratings

About

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

Information

  • Creator
    Paul Middlebrooks
  • Years Active
    2018 - 2025
  • Episodes
    135
  • Rating
    Clean
  • Copyright
    © 2019 Brain-Inspired
  • Show Website
    Brain Inspired

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