Chengcheng Huang, University of Pittsburgh
601, Pao Yue-Kong Library
How neuronal variability impacts neural codes is a central question in systems neuroscience, often with complex and model dependent answers. Most population models are parametric, with tacitly assumed structure of neuronal tuning and population variability. While these models provide key insights, they cannot inform how the physiology and circuit wiring of cortical networks impact information flow. In this work, we study information propagation in spatially ordered neuronal networks. We focus on the effects of feedforward and recurrent projection widths relative to columnar width, as well as attentional modulation. We show that narrower feedforward projection width increases the saturation rate of information. In contrast, the recurrent projection width with spatially balanced excitation and inhibition has small effects on information. Further, we show that attention improves information flow by suppressing the internal dynamics of the recurrent network.
Dr. Chengcheng Huang is a postdoctoral associate in the group of Brent Doiron from the Department of Mathematics, University of Pittsburgh. IShe obtained her doctoral degree from the Courant Institute of Mathematical Sciences, New York University on 2015. Her PhD thesis was on neuromechanistic modeling in auditory neuroscience under the supervision of John Rinzel. She obtained her B.S. in mathematics from Nanjing University in China.