Frequency-difference-dependent stochastic resonance in neural systems

TL;DR

This paper investigates how neural systems respond to superimposed oscillatory signals with different frequencies, revealing a frequency-difference-dependent stochastic resonance that enhances weak signal detection at the population level.

Contribution

It introduces the concept of frequency-difference-dependent stochastic resonance in neural systems and analyzes how population dynamics and excitation-inhibition balance optimize signal detection.

Findings

Stochastic resonance occurs at the beat frequency of superimposed signals.

Population of neurons detects weak signals more efficiently than single neurons.

Fine-tuning excitation-inhibition balance enhances neural response to superimposed signals.

Abstract

Biological neurons receive multiple noisy oscillatory signals, and their dynamical response to the superposition of these signals is of fundamental importance for information processing in the brain. Here we study the response of neural systems to the weak envelope modulation signal, which is superimposed by two periodic signals with different frequencies. We show that stochastic resonance occurs at the beat frequency in neural systems at the single-neuron as well as the population level. The performance of this frequency-difference-dependent stochastic resonance is influenced by both the beat frequency and the two forcing frequencies. Compared to a single neuron, a population of neurons is more efficient in detecting the information carried by the weak envelope modulation signal at the beat frequency. Furthermore, an appropriate fine-tuning of the excitation-inhibition balance can further optimize the response of a neural ensemble to the superimposed signal. Our results thus introduce and provide insights into the generation and modulation mechanism of the frequency-difference-dependent stochastic resonance in neural systems.