CBMM Special Seminar: Next-generation recurrent network models for cognitive neuroscience
Speaker
Dept. of Brain and Cognitive Sciences (BCS), EECS Dept., Schwarzman College of Computing (SCC), MIT
Host
Prof. Tomaso A. Poggio
CBMM, CSAIL, MIBR, BCS
Abstract: Recurrent Neural Networks (RNNs) trained with machine learning techniques on cognitive tasks have become a widely accepted tool for neuroscientists. In comparison to traditional computational models in neuroscience, RNNs can offer substantial advantages at explaining complex behavior and neural activity patterns. Their use allows rapid generation of mechanistic hypotheses for cognitive computations. RNNs further provide a natural way to flexibly combine bottom-up biological knowledge with top-down computational goals into network models. However, early works of this approach are faced with fundamental challenges. In this talk, I will discuss some of these challenges, and several recent steps that we took to partly address them and to build next-generation RNN models for cognitive neuroscience.
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Zoom details:
Zoom link: https://mit.zoom.us/j/94734403753?pwd=YW5udzZJdndqVnc1NnkyQ0s3L0hVUT09
Passcode: 080128
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Zoom details:
Zoom link: https://mit.zoom.us/j/94734403753?pwd=YW5udzZJdndqVnc1NnkyQ0s3L0hVUT09
Passcode: 080128