gamma oscillations of spiking neural populations enhance signal discrimination伽马振荡神经数量激增的增强信号的歧视.pdfVIP

Gamma Oscillations of Spiking Neural Populations Enhance Signal Discrimination Naoki Masuda1,2*, Brent Doiron3,¤* 1 Graduate School of Information Science and Technology, the University of Tokyo, Bunkyo, Tokyo, Japan, 2 Amari Research Unit, RIKEN Brain Science Institute, Wako, Saitama, Japan, 3 Center for Neural Science, New York University, New York, New York, United States of America Selective attention is an important filter for complex environments where distractions compete with signals. Attention increases both the gamma-band power of cortical local field potentials and the spike-field coherence within the receptive field of an attended object. However, the mechanisms by which gamma-band activity enhances, if at all, the encoding of input signals are not well understood. We propose that gamma oscillations induce binomial-like spike- count statistics across noisy neural populations. Using simplified models of spiking neurons, we show how the discrimination of static signals based on the population spike-count response is improved with gamma induced binomial statistics. These results give an important mechanistic link between the neural correlates of attention and the discrimination tasks where attention is known to enhance performance. Further, they show how a rhythmicity of spike responses can enhance coding schemes that are not temporally sensitive. Citation: Masuda N, Doiron B (2007) Gamma oscillations of spiking neural populations enhance signal discrimination. PLoS Comput Biol 3(11): e236. doi:10.1371/journal.pcbi. 0030236 Introduction A popular framework for neural coding is that the number of spikes produced by a single neuron or a population of Past work with both human and animal sub