Sugandha Sharma, masters student graduate of the University of Waterloo's CTN, discusses her research and time in the laboratory of CTN Founding Director Chris Eliasmith as well as her current PhD research at MIT on the Generally Intelligent Podcast. Give it a listen.
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.