Noise-accelerated Kramers Escape and Coherence Resonance in a 5D Neural Manifold
Yefan Wu

TL;DR
This study reveals how boundary-constrained multiplicative noise in a high-dimensional neural model induces diverse dynamical phenomena, including stochastic awakening, coherence resonance, and noise-accelerated escape, impacting neural excitability.
Contribution
It introduces a rigorous computational framework demonstrating how state-dependent boundary noise actively influences neural dynamics and excitability in a 5D Hodgkin-Huxley model.
Findings
Noise triggers stochastic awakening via Kramers escape.
Near bifurcation, noise induces robust coherence resonance.
Extreme noise amplifies escape rates, causing irregular bursting.
Abstract
Intrinsic channel noise is fundamental to neural processing, yet its state-dependent nature, when constrained by strict Feller boundary conditions, is often overlooked. Here, we demonstrate that this bounded multiplicative noise is not merely a source of jitter but an active dynamical force that fundamentally reshapes neural excitability. Investigating a 5D Hodgkin-Huxley-type cortical pacemaker model, we utilize a full-truncation semi-implicit Euler scheme to ensure rigorous probability conservation and domain-preserving integration. Through comprehensive parameter sweeps, we uncover a rich triphasic landscape of noise-induced transitions dictated by the underlying bifurcation structure. Deep in the subthreshold regime, multiplicative noise acts as a constructive force, triggering stochastic awakening via Kramers escape. Near the subcritical Hopf bifurcation, this evolves into highly…
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