This project explores how information processing by neural circuits is organized to use the resources of the brain efficiently. The proposed theoretical studies apply fundamentally new approaches to analyzing the organization of cortical maps, by investigating how emerging principles of efficient design pertain to the computational mechanisms employed by the central brain. One aim studies how the "place map" in the hippocampus (where individual cells are tuned to fire in particular locations of an environment) should be organized to efficiently support general goal-directed navigation. A second aim studies how the "shape map" in Inferotemporal Cortex (where individual cells are tuned to fire in response to particular visual shapes) should be organized to efficiently support shape perception, given the distribution of shapes in natural visual scenes. These theoretical studies will lead to directly testable predictions of the distribution of tuning curves in cortical area IT and hippocampus. In this way, the theory will provide a lever for further experimental exploration of the architecture of form vision and spatial navigation in the brain. Population codes also involve interactions between the different neurons, but techniques are not yet available to comprehensively study these interactions in cortex. Thus, a third aim uses the retina (a piece of the central brain that has projected out into the eye) as a model system to theoretically and experimentally ask two basic, and as yet unanswered questions: (a) Do neural networks adapt their interactions to stimulus statistics and noise as predicted by optimization theory?; (b) Is noise, as measured from single neurons, simply a mis-reading of correlated activity? By asking and answering these questions, this project will also explain key aspects of how the retina prepares visual input for central processing. Knowledge of how retinal circuits respond to natural and synthetic stimuli will be useful in designing effective prosthetic devices.
This project strengthens the research connections between disciplines by bringing together analytical and theoretical methods from physics and machine learning with experimental techniques from neuroscience. Students and postdocs who thus develop proficiency with both biological and quantitative physical techniques will be better able to cope with scientific and industrial challenges of coming decades. The educational component of this proposal also addresses this national need directly by developing pedagogical materials for a course on "Theoretical and Computational Neuroscience". The PI will give presentations to K-8 and high school students and to the general public with a view to broadening public knowledge of the field. Outreach to historically disadvantaged communities will be carried out through established programs at Penn. Finally, the PI is active in organizing lecture series and conferences that engage physicists to work within quantitative systems neuroscience.
