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Brain InspiredWhere Neuroscience and AI Converge Author: Paul Middlebrooks
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. Language: en-us Genres: Natural Sciences, Science, Technology Contact email: Get it Feed URL: Get it iTunes ID: Get it |
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BI 247 Maxim Raginsky: A Control Theory View on Brains and AI
Tuesday, 6 October, 2026
Support the show to get full episodes, full archive, and join the Discord community. The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists. Read more about our partnership. Sign up for Brain Inspired email alerts to be notified every time a new Brain Inspired episode is released. To explore more neuroscience news and perspectives, visit thetransmitter.org. Maxim Raginsky is a professor at the University of Illinois at Urbana-Champaign. Max describes himself as interested in probability and stochastic processes, deterministic and stochastic control, machine learning, optimization, and information theory. Today we mostly lean on his control theory expertise, although you'll here his knowledge is vast in many other domains, even some neuroscience. I wanted his control theory perspective on neuroscience, AI, and biological autonomy, so we dance around a lot of topics related to those. Max also writes a substack called The Art of the Realizable, from which I drew during parts of our conversation. Maxim Raginsky Substack: The Art of the Realizable. Related papers Biological Autonomy Control-related episodes BI 143 Rodolphe Sepulchre: Mixed Feedback Control BI 205 Dmitri Chklovskii: Neurons Are Smarter Than You Think Read the transcript. 0:00 - Intro 3:07 - Low energy lifestyle 4:27 - Engineering and philosophy? 13:52 - Brains vs AI 19:45 - Inferring the inside from behavior 30:57 - Analog vs digital 41:32 - Is the brain a control system? 46:50 - Willems control 1:02:12 - Control vs cybernetics 1:13:26 - A control perspective on AI vs brains 1:17:46 - AGI 1:21:48 - Turing 1950 1:29:07 - Perceptual control theory and active inference 1:40:09 - Passive control in the brain? 1:41:48 - Computation 1:43:36 - Counting spikes
