Abstract

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.

Highlights

  • Past work with both human and animal subjects has focused on neural correlates of attention

  • We assess the role of gamma oscillations in the signal coding of neural populations participating in gamma oscillatory dynamics

  • We show that gamma oscillations endow population spike counts with binomial-like statistics, which improve signal discrimination over a range of stimuli through reduced spike-count variability

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Summary

Introduction

Past work with both human and animal subjects has focused on neural correlates of attention. Attention is thought to influence cholingergic neuromodulation [12], which presumably affects synchrony of interneuron networks involved in gamma oscillations [11,13,14]. It is well-known that correlated network discharge effectively drives postsynaptic cells [15], making gamma-band activity a signature of efficient signal propagation. This would allow attended objects to increase downstream responses, as compared to nonattended objects. We assess the role of gamma oscillations in the signal coding of neural populations participating in gamma oscillatory dynamics

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