CTN Colloquium Eric Shea-Brown (Washington)

February 20 (virtual) - Eric Shea-Brown (Washington)
 

Title: When do high dimensional networks learn to produce low dimensional dynamics?

Abstract:  Neural networks in biology and in engineering have tremendous numbers of interacting units, yet often produce dynamics with many fewer degrees of freedom — that is, of low dimensionality.  We explore when general network learning rules tend to produce such low dimensional dynamics.  We demonstrate two main applications, in networks producing highly compressed representations that may support generalization, and in networks extracting latent variables that may efficiently describe more complex environments.