Stimulus-Dependent Suppression of Chaos in Recurrent Neural Networks

TL;DR

This paper uses mean-field analysis to show how external stimuli can suppress chaos in neural networks, with a resonance effect at specific stimulus frequencies, leading to a phase transition from chaotic to ordered activity.

Contribution

It reveals how stimuli suppress chaos in neural networks and identifies a resonant frequency that optimally reduces neural variability, advancing understanding of neural response regulation.

Findings

External input suppresses ongoing chaotic activity.

Suppression effectiveness peaks at a resonant stimulus frequency.

Neural response variance is minimized at sensory-relevant frequencies.

Abstract

Neuronal activity arises from an interaction between ongoing firing generated spontaneously by neural circuits and responses driven by external stimuli. Using mean-field analysis, we ask how a neural network that intrinsically generates chaotic patterns of activity can remain sensitive to extrinsic input. We find that inputs not only drive network responses, they also actively suppress ongoing activity, ultimately leading to a phase transition in which chaos is completely eliminated. The critical input intensity at the phase transition is a non-monotonic function of stimulus frequency, revealing a "resonant" frequency at which the input is most effective at suppressing chaos even though the power spectrum of the spontaneous activity peaks at zero and falls exponentially. A prediction of our analysis is that the variance of neural responses should be most strongly suppressed at frequencies matching the range over which many sensory systems operate.