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SIAM Chapter Seminar

Chengcheng Huang
University of Pittsburgh
Title: The Space and Time of Neuronal Variability in a Spiking Neuron Network

Abstract: Neural variability has important consequences on neural coding. The mechanism underlying neural variability is still poorly understood. The balanced network of excitation and inhibition successfully reproduces the Poissonian spiking statistics of individual neuron, however, it cannot explain the shared variability among neurons (noise correlation), which is commonly observed for cortical neurons. Recent experiments have shown that attention reduces correlation among neurons within visual cortex area and increases the correlation between cortical areas simultaneously (Ruff & Cohen, submitted). These effects can presumably improve the population code of visual stimuli. The observed opposite trends of change of correlations between-areas and within-area imposes further constraint on circuit mechanisms for attention. We found that a linear model, such as a balanced network, cannot explain the opposite trend of change in correlations. We developed a spiking neuron network with spatiotemporal dynamics, which can generate correlated variability internally. We are able to reproduce the attentional effects on noise correlation by depolarizing both excitatory and inhibitory populations.

Date: Thursday, April 7, 2016
Time: 5:30 pm
Location: Wean Hall 8220
Submitted by:  Samuel Cohn