Interpretation of correlated neural variability from models of feed-forward and recurrent circuits.

Volker Pernice,Rava Azeredo Da Silveira,Brent Doiron

doi:10.1371/journal.pcbi.1005979

Abstract

Neural populations respond to the repeated presentations of a sensory stimulus with correlated variability. These correlations have been studied in detail, with respect to their mechanistic origin, as well as their influence on stimulus discrimination and on the performance of population codes. A number of theoretical studies have endeavored to link network architecture to the nature of the correlations in neural activity. Here, we contribute to this effort: in models of circuits of stochastic neurons, we elucidate the implications of various network architectures—recurrent connections, shared feed-forward projections, and shared gain fluctuations—on the stimulus dependence in correlations. Specifically, we derive mathematical relations that specify the dependence of population-averaged covariances on firing rates, for different network architectures. In turn, these relations can be used to analyze data on population activity. We examine recordings from neural populations in mouse auditory cortex. We find that a recurrent network model with random effective connections captures the observed statistics. Furthermore, using our circuit model, we investigate the relation between network parameters, correlations, and how well different stimuli can be discriminated from one another based on the population activity. As such, our approach allows us to relate properties of the neural circuit to information processing.

Highlights

In the search for clues about the function of neural circuits, it has become customary to rely upon recordings of the activity of large populations of neurons
This and similar mechanisms were exploited in recent studies [7, 8], in particular to account for observations in both visual cortex [9,10,11,12] and retina [13, 14]
We analyze experimental data, namely, populations recordings in mouse auditory cortex [35], in the light of our model results, and we show that the structure of noise correlations in auditory cortex is in agreement with predictions from a recurrent network

Summary

Introduction

In the search for clues about the function of neural circuits, it has become customary to rely upon recordings of the activity of large populations of neurons. While the first question is mechanistic and the second is functional, the two are intimately linked We address these questions by relating observed population activity to the output of different circuit models. As an alternative to recurrent connections, shared external input (e.g., from top-down afferents) or shared gain fluctuations can be at the origin of correlations in neural populations This and similar mechanisms were exploited in recent studies [7, 8], in particular to account for observations in both visual cortex [9,10,11,12] and retina [13, 14]

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