This paper presents a novel biophysical neuron model that incorporates the concept of double membranes and a memristor to simulate the electrophysiological properties of neurons. The model uses two capacitors connected via a memristor, a nonlinear resistor combined with an inductor, to represent the flexibility and controllability of the cell membrane under external stimuli. The energy function, derived through scale transformation, captures the membrane potential and channel currents, enabling the prediction of coherence resonance and high regularity in neural activities. The model also includes an adaptive criterion to explain how energy levels control parameter shifts and shape deformations on the cell membrane. The introduction highlights the importance of considering energy levels, energy shunting, controllability, and coherence resonance in neural circuits. The memristive synapses and their self-adaptive properties are discussed, along with the physical effects of electromagnetic induction in biological neurons. The model's ability to process information from external signals and its sensitivity to energy injections are emphasized, making it suitable for studying multi-channel excitations and firing patterns. The use of two capacitors and a memristor with magnetic flux dependence further enhances the model's ability to capture the complex dynamics of neural circuits.This paper presents a novel biophysical neuron model that incorporates the concept of double membranes and a memristor to simulate the electrophysiological properties of neurons. The model uses two capacitors connected via a memristor, a nonlinear resistor combined with an inductor, to represent the flexibility and controllability of the cell membrane under external stimuli. The energy function, derived through scale transformation, captures the membrane potential and channel currents, enabling the prediction of coherence resonance and high regularity in neural activities. The model also includes an adaptive criterion to explain how energy levels control parameter shifts and shape deformations on the cell membrane. The introduction highlights the importance of considering energy levels, energy shunting, controllability, and coherence resonance in neural circuits. The memristive synapses and their self-adaptive properties are discussed, along with the physical effects of electromagnetic induction in biological neurons. The model's ability to process information from external signals and its sensitivity to energy injections are emphasized, making it suitable for studying multi-channel excitations and firing patterns. The use of two capacitors and a memristor with magnetic flux dependence further enhances the model's ability to capture the complex dynamics of neural circuits.