Analyzing artificial neural networks to understand the brain
In the first part of this talk I will present work showing that recurrent neural networks can replicate broad behavioral patterns associated with dynamic visual object recognition in humans. An analysis of these networks shows that different types of recurrence use different strategies to solve the object recognition problem. The similarities between artificial neural networks and the brain presents another opportunity, beyond using them just as models of biological processing. In the second part of this talk, I will discuss—and solicit feedback on—a proposed research plan for testing a wide range of analysis tools frequently applied to neural data on artificial neural networks. I will present the motivation for this approach as well as the form the results could take and how this would benefit neuroscience and the field of interpretable AI.
New York University
Grace Lindsay is an Assistant Professor of Psychology and Data Science at New York University. She uses bio-inspired artificial neural networks to probe the relationship between neural activity and behavior. She earned her PhD at the Center for Theoretical Neuroscience at Columbia University and was a post-doctoral fellow at the Gatsby Computational Neuroscience Unit at University College London. She is also the author of “Models of the Mind: How physics, engineering, and mathematics have shaped our understanding of the brain” (Bloomsbury Sigma, 2021).
About the Wu Tsai Neuro MBCT Seminar Series The Stanford Center for Mind, Brain, Computation and Technology Seminars (MBCT) explores ways in which computational and technical approaches are being used to advance the frontiers of neuroscience. It features speakers from other institutions, Stanford faculty and senior training program trainees.
The MBCT Seminar Series is not offered via Zoom at this time. If given speaker permission, recordings will be available on our YouTube channel after the talk.