Emergence of resonances in neural systems: the interplay between threshold adaptation and short-term synaptic plasticity

TL;DR

This paper investigates how the interaction between threshold adaptation and short-term synaptic plasticity in neural systems creates multiple optimal noise levels for signal detection, challenging classical stochastic resonance theory.

Contribution

It introduces a novel understanding of multiple noise optima in neural signal detection due to nonlinear dynamics and activity-dependent synaptic mechanisms.

Findings

Two optimal noise levels for signal transmission identified

Classical stochastic resonance predicts only one optimal noise level

Experimental data supports the new phenomenology

Abstract

In this work we study the detection of weak stimuli by spiking neurons in the presence of certain level of noisy background neural activity. Our study has focused in the realistic assumption that the synapses in the network present activity-dependent processes, such as short-term synaptic depression and facilitation. Employing mean-field techniques as well as numerical simulations, we found that there are two possible noise levels which optimize signal transmission. This new finding is in contrast with the classical theory of stochastic resonance which is able to predict only one optimal level of noise. We found that the complex interplay between the nonlinear dynamics of the neuron threshold and the activity-dependent synaptic mechanisms is responsible for this new phenomenology. Our results are confirmed by employing a more realistic FitzHugh-Nagumo neuron model, which displays threshold variability, as well as by considering more realistic synaptic models. We support our findings with recent experimental data of stochastic resonance in the human tactile blink reflex.