Five key factors determining pairwise correlations in visual cortex.

David P A Schulz,Maneesh Sahani,Matteo Carandini

doi:10.1152/jn.00094.2015

Abstract

The responses of cortical neurons to repeated presentation of a stimulus are highly variable, yet correlated. These “noise correlations” reflect a low-dimensional structure of population dynamics. Here, we examine noise correlations in 22,705 pairs of neurons in primary visual cortex (V1) of anesthetized cats, during ongoing activity and in response to artificial and natural visual stimuli. We measured how noise correlations depend on 11 factors. Because these factors are themselves not independent, we distinguished their influences using a nonlinear additive model. The model revealed that five key factors play a predominant role in determining pairwise correlations. Two of these are distance in cortex and difference in sensory tuning: these are known to decrease correlation. A third factor is firing rate: confirming most earlier observations, it markedly increased pairwise correlations. A fourth factor is spike width: cells with a broad spike were more strongly correlated amongst each other. A fifth factor is spike isolation: neurons with worse isolation were more correlated, even if they were recorded on different electrodes. For pairs of neurons with poor isolation, this last factor was the main determinant of correlations. These results were generally independent of stimulus type and timescale of analysis, but there were exceptions. For instance, pairwise correlations depended on difference in orientation tuning more during responses to gratings than to natural stimuli. These results consolidate disjoint observations in a vast literature on pairwise correlations and point towards regularities of population coding in sensory cortex.

Highlights

THE FIRING OF DIFFERENT CORTICAL neurons is often coordinated, sometimes over large distances and even across different areas (Volgushev et al 2011; Hipp et al 2012)
A debate has centered on the strength of noise correlations, especially in sensory areas such as primary visual cortex (V1)
An influential study in area V1 reported extremely small average noise correlations, in the order of 0.001 (Ecker et al 2010), matching similar measurements in auditory cortex (Renart et al 2010). These results contrast with earlier studies from multiple laboratories, which had found average noise correlations in area V1 to be much larger at ϳ0.1 (Benucci et al 2013; Schölvinck et al 2015), ϳ0.20 (Kohn and Smith 2005), 0.18 (Smith and Kohn 2008), ϳ0.25 (Reich et al 2001), or even ϳ0.35 (Gutnisky and Dragoi 2008; see Cohen and Kohn 2011 for an extensive review)

Summary

Introduction

THE FIRING OF DIFFERENT CORTICAL neurons is often coordinated, sometimes over large distances and even across different areas (Volgushev et al 2011; Hipp et al 2012). An influential study in area V1 reported extremely small average noise correlations, in the order of 0.001 (Ecker et al 2010), matching similar measurements in auditory cortex (Renart et al 2010) These results contrast with earlier studies from multiple laboratories, which had found average noise correlations in area V1 to be much larger at ϳ0.1 (Benucci et al 2013; Schölvinck et al 2015), ϳ0.20 (Kohn and Smith 2005), 0.18 (Smith and Kohn 2008), ϳ0.25 (Reich et al 2001), or even ϳ0.35 (Gutnisky and Dragoi 2008; see Cohen and Kohn 2011 for an extensive review). Either at the level of populations or at the level of individual neurons (Kohn and Smith 2005), or only weak dependence, mostly due to slow covariations (Ecker et al 2010)

Methods

Results

Conclusion