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• Recurrent neural networks for cognitive neuroscience

Recurrent Neural Networks for Cognitive Neuroscience

• Robert Guangyu Yang, MIT

Abstract

In this hands-on tutorial, we will work together through a number of coding exercises to see how RNNs can be easily used to study cognitive neuroscience questions. We will train and analyze RNNs on various cognitive neuroscience tasks. Familiarity of Python and basic knowledge of Pytorch are assumed.

There will be a 30 minute lecture followed by a workshop utilizing Google’s Collab Notebooks. Attendees are free to attend any length of the tutorial. Materials and code to complete the workshop will be made available the day of the event.

Speaker Bio

Robert (Guangyu) Yang is an Assistant Professor in the MIT Department of Brain and Cognitive Sciences (BCS), with a joint appointment in the EECS Department in the Schwarzman College of Computing (SCC). He received his B.S. in physics from Peking University and his Ph.D. in neuroscience from New York University working with computational neuroscientist Dr. Xiao-Jing Wang. During his Ph.D., he studied how distinct types of inhibitory neurons in the brain can coordinate the information flow across brain areas. In another line of work, he studied how the same artificial neural network can accomplish many cognitive tasks. He was a postdoctoral research scientist in the Center for Theoretical Neuroscience at Columbia University’s Zuckerman Institute. Currently, he studies how artificial neural networks can become more powerful by incorporating neural architectures discovered in the brain.