Electromagnetic modulation of memristor-based neuronal dynamics

Abstract

The cell fluid around neurons contains ions such as $K^{+}$, $N{a}^{+}$ and $C{l}^{-}$, where ion adsorption on the neuronal membrane creates capacitive characteristics, while stochastic transport through irregular channel proteins across the membrane manifests inductive properties. This study constructs an electromagnetic-sensitive neuron model addressing electromagnetic field fluctuations during cytosol flow, incorporating flux-controlled and charge-controlled memristors to respectively participate in membrane potential voltage division and channel current shunting, with additional periodic stimulation for electrophysiological regulation. Helmholtz theorem verification is implemented for isolated neurons. Accounting for external electromagnetic field disturbances, Gaussian white noise perturbations are introduced to simulate the induced voltage components in charge-controlled memristors and induced current components in flux-controlled memristors. Membrane parameter $c$ undergoes adaptive control considering neuronal deformation. Simulations show that periodic stimulation and memristor parameter adjustments yield chaotic, periodic, bursting, and spiking discharge modes, each with distinct energy storage traits. No significant stochastic resonance emerges under noise intervention, while parameter $c$ achieves energy self-adaptive regulation through stabilization control.