Theory of input spike auto- and cross-correlations and their effect on the response of spiking neurons

TL;DR

This paper investigates how input spike correlations influence the firing response of leaky integrate-and-fire neurons, revealing that presynaptic spike trains are not well modeled as Poisson processes and analyzing responses across different correlation timescales.

Contribution

It provides a theoretical framework for understanding the impact of input spike correlations on neuron responses, including solutions for different correlation timescales.

Findings

Presynaptic spike trains cannot be accurately modeled as Poisson processes.

Output firing rates depend on input correlation characteristics.

Analytical solutions are derived for short and long correlation timescales.

Abstract

Spike correlations between neurons are ubiquitous in the cortex, but their role is at present not understood. Here we describe the firing response of a leaky integrate-and-fire neuron (LIF) when it receives a temporarily correlated input generated by presynaptic correlated neuronal populations. Input correlations are characterized in terms of the firing rates, Fano factors, correlation coefficients and correlation timescale of the neurons driving the target neuron. We show that the sum of the presynaptic spike trains cannot be well described by a Poisson process. Solutions of the output firing rate are found in the limit of short and long correlation time scales.