Taught by: Robert Guangyu Yang, CBMM (August 30, 2021)
Video: Recurrent Neural Networks for Cognitive Neuroscience
Description: 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 with Python and basic knowledge of Pytorch are assumed.
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.
Additional Resources:
Collab notebooks:
