Stephanie Palmer, PhD (Uchicago) – AI+Science Schmidt Fellows Speaker Series

Organized by the University of Chicago’s Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship Program.

Meeting location
William Eckhardt Research Center. Room 401
5640 S Ellis Avenue, Chicago, IL 60637
Map It

Agenda
4:30pm – 5:15pm: Presentation
5:15pm – 5:30pm: Q&A
5:30pm – 6:00pm: Reception

Abstract: Biological systems must selectively encode partial information about the environment, as dictated by the capacity constraints at work in all living organisms. For example, we cannot see every feature of the light field that reaches our eyes; temporal resolution is limited by transmission noise and delays, and spatial resolution is limited by the finite number of photoreceptors and output cells in the retina. Classical efficient coding theory describes how sensory systems can maximize information transmission given such capacity constraints, but it treats all input features equally. Not all inputs are, however, of equal value to the organism. Our work quantifies whether and how the brain selectively encodes stimulus features, specifically predictive features, that are most useful for fast and effective movements. We have shown that efficient predictive computation starts at the earliest stages of the visual system, in the retina. We borrow techniques from machine learning, statistical physics, and information theory to assess how we get terrific, predictive vision from these imperfect (lagged and noisy) component parts. In broader terms, we aim to build a more complete theory of efficient encoding in the brain, and along the way have found some intriguing connections between approaches to coarse graining in biology, machine learning, and physics

Stephanie Palmer, PhD, Associate Professor of Organismal Biology and Anatomy, University of Chicago. “I study how populations of neurons collectively encode information present in their inputs and how they perform computations on these signals. The brain performs several classes of computation including signal comparison, prediction, error correction, and learning. To investigate these phenomena, I work with experimentalists on a variety of systems: predictive coding in the retina and visual cortex of the rodent, motion coding in area MT, and temporal coding in the zebra finch song system. From these studies, several general principles have emerged, which guide my current research: the hypothesis that neurons are optimized to predict their future inputs, that information in neural populations is represented combinatorially, and that coding in sensori-motor systems is highly dynamic and behaviorally dependent. By working closely with experimentalists, we constrain and test these theories of neural population coding with detailed measurements.”

Parking
Campus North Parking
5505 S Ellis Ave
Map It