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arXiv · 2605.04088

Noise-Induced Transitions and Coherence Resonance in a 5D Conductance-Based Neuronal Model

Yefan Wu
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Abstract

Intrinsic channel noise is an important source of variability in neuronal dynamics, but its state-dependent and boundary-constrained nature can be difficult to represent numerically. Here, we investigate the effects of bounded multiplicative noise applied to the slow M-current gating variable in a five-dimensional conductance-based model of a CA1 pyramidal neuron. We employ a full-truncation semi-implicit Euler scheme to control boundary violations and perform Monte Carlo parameter sweeps, time-step checks, burst-detection sensitivity analyses, and conductance perturbations. In subthreshold regimes, noise induces firing and produces an intermediate-noise minimum in the coefficient of variation, consistent with coherence resonance. In the deep subthreshold regime, the burst rate exhibits moderate Arrhenius-like scaling over a restricted low-noise interval, supporting an interpretation in terms of noise-activated escape. Near the numerically identified onset of sustained oscillations, temporal coherence is particularly sensitive to noise intensity. In the suprathreshold regime, strong multiplicative noise accelerates firing through premature stochastic exits from the hyperpolarized recovery branch, although this behavior does not follow classical Kramers scaling. Control simulations using clipped additive Gaussian noise produce qualitatively different dynamics, indicating that the state dependence and boundary behavior of the diffusion term materially affect the observed transitions. The main trends remain robust under the tested variations in numerical resolution, burst-detection threshold, slow timescale, and M-current conductance. These results characterize model-specific effects of boundary-constrained channel noise and motivate future comparisons with discrete-state ion-channel models.

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Yefan Wu. 2026-07-21. Noise-Induced Transitions and Coherence Resonance in a 5D Conductance-Based Neuronal Model. https://arxiv.org/abs/2605.04088

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