Abstract: Electrical activities in biological neurons show complexity and diversity, and establishing reliable neuron models is crucial for predicting mode transition in neural activities and energy shift in neurons. Modeling of neurons should consider the membrane structure, correlation and controllability in different ion channels, the effect of electromagnetic induction during electrical activities, and physical expression of functional perceptual processes. The resting state and various discharge processes of biological neurons contain inherent electromagnetic field energy, and the energy conversion of internal electric and magnetic field affects the discharge mode and stability of neurons. Considering the basic physical properties and characteristics of biological neurons, constructing equivalent neural circuits can effectively express the main features and functional responses of neuronal electrical activity, and further predict the stochastic resonance of neuronal electrical activity under random excitation or electromagnetic radiation. This article provides energy characterization methods, scaling transformation criteria, quantitative expression of electromagnetic induction in neuronal electrical activity, ion channel diversion control, and energy regulation strategies for neuronal electrical activity in the process of neural modeling and circuit expression from a physical perspective. It has important references for computational neuroscience and neural circuit application control.

Key words: neural circuit, memristor, neuron, Hamilton energy, stochastic resonance