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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 215 Xiao-Jing Wang: Theoretical Neuroscience Comes of Age
Wednesday, 2 July, 2025
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
