Mechanistic Models of Cognition: from Perception to Navigation to Semantic Development

Presented By: Surya Ganguli, PhD

Speaker Biography: Surya Ganguli triple majored in physics, mathematics, and EECS at MIT, completed a PhD in string theory at Berkeley, and a postdoc in theoretical neuroscience at UCSF. He is now an associate professor of Applied physics at Stanford where he leads the Neural Dynamics and Computation Lab. His research spans the fields of neuroscience, machine learning and physics, focusing on understanding and improving how both biological and artificial neural networks learn striking emergent computations. He has been awarded a Swartz-Fellowship in computational neuroscience, a Burroughs-Wellcome Career Award, a Terman Award, a NeurIPS Outstanding Paper Award, a Sloan fellowship, a James S. McDonnell Foundation scholar award in human cognition, a McKnight Scholar award in Neuroscience, a Simons Investigator Award in the mathematical modeling of living systems, and an NSF career award.

Webinar: Mechanistic Models of Cognition: from Perception to Navigation to Semantic Development

Webinar Abstract: We will show how to combine large scale neural recordings and mechanistic neural network models to advance our conceptual understanding of how neural circuits mediate cognitive functions like perception, navigation and semantic cognition. With additional mathematical analysis, we can also even explain why some neural circuits might be organized the way they are. First, we will describe state of the art models of the retinal response to natural movies, and use explainable artificial intelligence to understand how they recapitulate over 20 years of retinal physiology experiments. Second we will describe how the 4 most dominant cell-types in our retina emerge naturally as a consequence of optimal spatiotemporal processing of natural movies. Third, we will demonstrate how hexagonal grid cell firing fields must obligatorily arise in any biologically plausible neural network that is capable of performing path-integration. And fourth, time permitting, we will describe how the hierarchical differentiation of concepts that unfolds over time in infant semantic development can be accounted for by simple mechanistic neural models.

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