BI 245 Dan Levenstein: Neuro-AI, Dynamics, and Model Systems

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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