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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Talks Overview: Neural networks are high-dimensional systems whose activity forms a basis for learning and memory. Measured activity in biological and artificial neural networks does not uniformly fill the space of all possible activity patterns, instead being constrained to low-dimensional manifolds whose structure is related both to the architecture of the network and the nature of the inputs it receives. I will introduce the notion of the linear embedding dimension as a useful metric for describing neural network activity and discuss its relationship with learning. I will describe work that we have done in feedforward networks computing this quantity and relating it to generalization performance for learning tasks, and the anatomical organization of cerebellum-like systems. I will then describe recent work in which we have begun to analyze the dimension of random recurrent networks in the chaotic state.
Speaker Webpage: http://lk.zuckermaninstitute.columbia.edu/
Host: Hannah Choi
