Robert Guangyu Yang
Monday 16th March 2020
Time: 12 Midday
Ground Floor Seminar Room
25 Howland Street, London, W1T 4JG
Understanding brains by building artificial networks
There is a long tradition in neuroscience (and other sciences) to gain insights into one system by studying other relevant, more-accessible model systems. Much we have learned about the human brain are from studying brains of monkeys, mice, flies, and other model animals. In this talk, I will demonstrate how we can gain understandings of real neural circuits by studying artificial ones. In particular, I will focus on how machine learning can be used to recapitulate structural principles observed in the olfactory system. I will also discuss how machine learning can help us understand and discover biologically-plausible plasticity rules. Finally, I will discuss our efforts to study neural mechanisms of recurrent neural networks performing many cognitive tasks.
Robert Yang is a Simons Junior Fellow and a postdoctoral research scientist in the Zuckerman Institute of Mind, Brain, Behavior at Columbia University. He is advised by Larry Abbott. Robert obtained his PhD at New York University, advised by computational neuroscientist Xiao-Jing Wang.