Coherence resonance, parameter estimation and self-regulation in a thermalsensitive neuron.

In this work, two capacitors connected by a thermistor are used to explore the electrical property of double-layer membrane in a neuron, which the membrane property is sensitive to changes of temperature and two capacitive variables are used to measure the potentials of inner and outer membrane. The circuit characteristics and energy definition for the neural circuit and its equivalent neuron model in oscillator form are clarified from physical aspect. Considering the shape deformation of cell membrane under external physical stimuli and energy injection, intrinsic parameters of the neuron can be controlled with adaptive growth under energy flow, an adaptive control law is proposed to regulate the firing modes accompanying with energy shift. In presence of noisy excitation, coherence resonance can be induced and confirmed by taming the noise intensity carefully. The distributions of CV (coefficient variability) and average energy value < H > vs. noise intensity provide a feasible way to predict the coherence resonance and even stochastic resonance in the neural activities. Adaptive parameter observers are designed to identify the unknown parameters in this neuron model. The research findings of this study lay a foundation for the design of temperature-adaptive biomimetic neuromorphic devices and the research on multi-functional perception neural networks with temperature sensitivity.