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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.




Eric's website.



Related papers

Predictive learning as a network mechanism for extracting low-dimensional latent space representations.



A scale-dependent measure of system dimensionality.



From lazy to rich to exclusive task representations in neural networks and neural codes.



Feedback through graph motifs relates structure and function in complex networks.






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?