BI 178 Eric Shea-Brown: Neural Dynamics and Dimensions

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Nov 13, 2023

Eric Shea-Brown, a theoretical neuroscientist, discusses dynamics and dimensionality in neural networks, exploring how they change during tasks. He highlights research findings on structural connection motifs and dimensionalities related to different modes of learning. The podcast also covers the impact of model architectures on neural dynamics, the complexity of the biological brain, and the concept of rich brain vs lazy brain. The chapter on paths and motifs in neural networks showcases a student's prediction abilities. Finally, the guest expresses desires for advancements in neuroscience and support for the podcast.

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Introduction

00:00 • 3min

Rock climbing adventure and a passion for carnivorous plants

02:31 • 2min

The Rise of Dynamical Systems in Neuroscience

04:15 • 27min

The Awe-Inspiring Complexity of the Biological Brain

31:14 • 26min

Paths and Motifs in Neural Networks

57:02 • 22min

Rich Brain vs Lazy Brain

01:18:42 • 11min

Desires for Experimental Neuroscience Advancements and Knowledge Gaps

01:29:45 • 3min

Personal Endeavors and Supporting the Podcast

01:32:33 • 3min

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Eric Shea-Brown is a theoretical neuroscientist and principle investigator of the working group on neural dynamics at the University of Washington. In this episode, we talk a lot about dynamics and dimensionality in neural networks... how to think about them, why they matter, how Eric's perspectives have changed through his career. We discuss a handful of his specific research findings about dynamics and dimensionality, like how dimensionality changes when one is performing a task versus when you're just sort of going about your day, what we can say about dynamics just by looking at different structural connection motifs, how different modes of learning can rely on different dimensionalities, and more.We also talk about how he goes about choosing what to work on and how to work on it. You'll hear in our discussion how much credit Eric gives to those surrounding him and those who came before him - he drops tons of references and names, so get ready if you want to follow up on some of the many lines of research he mentions.

0:00 - Intro 4:15 - Reflecting on the rise of dynamical systems in neuroscience 11:15 - DST view on macro scale 15:56 - Intuitions 22:07 - Eric's approach 31:13 - Are brains more or less impressive to you now? 38:45 - Why is dimensionality important? 50:03 - High-D in Low-D 54:14 - Dynamical motifs 1:14:56 - Theory for its own sake 1:18:43 - Rich vs. lazy learning 1:22:58 - Latent variables 1:26:58 - What assumptions give you most pause?