Direct coupling and inhomogeneity assist neurons to detect correlation in low amplitude noises

TL;DR

This paper investigates how direct excitatory coupling and inhomogeneity in neurons enhance their ability to detect correlations in low amplitude noisy inputs, revealing optimal synaptic strengths for maximum sensitivity.

Contribution

It demonstrates that direct excitatory connections and inhomogeneity improve neurons' detection of input correlation changes, identifying optimal synaptic strengths for sensitivity enhancement.

Findings

Direct coupling increases correlation detection sensitivity.

Inhomogeneity and connection symmetry affect sensitivity.

An optimal synaptic strength maximizes correlation detection.

Abstract

We address a question on the effect of common stochastic inputs on the correlation of the spikes trains of two neurons when they are possibly nonidentical and are coupled through direct connections. We show that the change in the correlation of low amplitude stochastic inputs can be better detected when the neurons are connected by direct excitatory couplings. Depending on whether the neurons are identical or they are slightly different, symmetric or asymmetric connections can increase the sensitivity of the system to the input correlation by changing the mean slope of correlation transfer function over a given range of input correlation. In either case, there is also an optimum value for synaptic strength which maximizes the sensitivity of the system to the changes in input correlation.