Noise-tuned bursting in a Hedgehog burster

TL;DR

This paper demonstrates how noise influences bursting behavior in a Hedgehog burster neuron model through stochastic resonance, predicting transition points and revealing staircase-like dependencies on noise strength.

Contribution

It introduces a novel analysis of noise-tuned bursting in the Hedgehog burster, utilizing the distance matching condition to predict transition positions and stochastic periodic orbits.

Findings

Noise can tune spike counts via SISR.

Critical transition positions depend non-monotonically on noise.

Large noise induces trapping of the slow variable.

Abstract

Noise can shape the firing behaviors of neurons. Here, we show that noise acting on the fast variable of the Hedgehog burster can tune the spike counts of bursts via the self-induced stochastic resonance (SISR) phenomenon. Using the distance matching condition, the critical transition positions on the slow manifolds can be predicted and the stochastic periodic orbits for various noise strengths are obtained. The critical transition positions on the slow manifold with non-monotonic potential differences exhibit a staircase-like dependence on the noise strength, which is also revealed by the stepwise change in the period of the stochastic periodic orbit. The noise-tuned bursting is more coherent within each step while displaying mixed-mode oscillations near the boundaries between the steps. When noise is large enough, noise-induced trapping of the slow variable can be observed, where the number of coexisting traps increases with the noise strength. It is argued that the robustness of SISR underlies the generality of the results discovered in this paper.