Emergence of spike correlations in periodically forced excitable systems

TL;DR

This study explores how noise and periodic stimuli influence spike timing in neurons, revealing resonance effects that enhance temporal order, which may underpin sensory information encoding.

Contribution

It demonstrates the emergence of preferred spike patterns driven by noise and periodic input, using ordinal analysis on FitzHugh-Nagumo neuron models.

Findings

Preferred ordinal patterns depend on noise strength and input period.

Resonance-like behavior maximizes temporal ordering at specific parameters.

Findings suggest mechanisms for temporal coding in sensory neurons.

Abstract

In sensory neurons the presence of noise can facilitate the detection of weak information-carrying signals, which are encoded and transmitted via correlated sequences of spikes. Here we investigate relative temporal order in spike sequences induced by a subthreshold periodic input, in the presence of white Gaussian noise. To simulate the spikes, we use the FitzHugh-Nagumo model, and to investigate the output sequence of inter-spike intervals (ISIs), we use the symbolic method of ordinal analysis. We find different types of relative temporal order, in the form of preferred ordinal patterns which depend on both, the strength of the noise and the period of the input signal. We also demonstrate a resonance-like behavior, as certain periods and noise levels enhance temporal ordering in the ISI sequence, maximizing the probability of the preferred patterns. Our findings could be relevant for understanding the mechanisms underlying temporal coding, by which single sensory neurons represent in spike sequences the information about weak periodic stimuli.