A simple mechanism for higher-order correlations in integrate-and-fire neurons

TL;DR

This paper demonstrates that common fluctuating inputs to integrate-and-fire neurons induce significant higher-order correlations, which can be effectively modeled using simplified, tractable descriptions of neural spike dynamics.

Contribution

It reveals that higher-order correlations naturally arise from common input in simple neuron models and shows these can be accurately captured by simplified models.

Findings

Higher-order correlations are induced by common fluctuating input.

Simplified models can accurately describe these correlations.

Higher-order correlations are broadly expected in neural populations.

Abstract

The collective dynamics of neural populations are often characterized in terms of correlations in the spike activity of different neurons. Open questions surround the basic nature of these correlations. In particular, what leads to higher-order correlations -- correlations in the population activity that extend beyond those expected from cell pairs? Here, we examine this question for a simple, but ubiquitous, circuit feature: common fluctuating input arriving to spiking neurons of integrate-and-fire type. We show that leads to strong higher-order correlations, as for earlier work with discrete threshold crossing models. Moreover, we find that the same is true for another widely used, doubly-stochastic model of neural spiking, the linear-nonlinear cascade. We explain the surprisingly strong connection between the collective dynamics produced by these models, and conclude that higher-order correlations are both broadly expected and possible to capture with surprising accuracy by simplified (and tractable) descriptions of neural spiking.