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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 245 Dan Levenstein: Neuro-AI, Dynamics, and Model Systems
Tuesday, 1 September, 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. Daniel Levenstein started his NeuroAI and Dynamics Lab at Yale University about a year ago. We briefly discuss what it's like to transition from a postdoc to a principle investigator, i.e. head of the lab. But most mostly we discuss his work and ideas. Dan studies spontaneous neural activity during sleep, specially in brain areas like hippocampus and cortex, and how this internally generated spontaneous activity is related to learning and memory and navigation. Really, he used to study those processes directly through experimental brain recording datasets. These days he builds and studies models of those processes, using AI models and seeing how their dynamics and functions match what we see in brains. Levenstein Lab Social: @dlevenstein.bsky.social Related papers On the Role of Theory and Modeling in Neuroscience The problem-ladenness of theory Sequential predictive learning is a unifying theory for hippocampal representation and replay 0:00 - Intro 9:12 - Neuro-AI 18:23 - Experiment vs theory 20:36 - Ground vs active state neuron activity 25:38 - Beginning a lab 31:34 - Sleep and Internally generated activity 40:02 - Spiking neural networks 52:13 - Naturalistic neuro-AI 59:52 - Cognitive maps, world models 1:04:16 - Weasel words and motifs 1:08:17 - Transformers and brains 1:18:53 - AI vs biology 1:24:32 - Neuroscience theory
