A light-temperature neuron and its adaptive regulation

Abstract

Abstract The appropriate firing modes for a neuron can be excited under the external stimulus. From the viewpoint of physical, the intrinsic biophysical effects, functional encoding, and the mechanism for the transcription of external signals play an extremely important role in building reliable neuron models. In this paper, a light-temperature neuron model is proposed by connecting a phototube and a thermistor into a nonlinear circuit for investigating the information encoding and responses of neurons under the external optical signals and temperature signals. In this neuron model, a phototube is used to encode external light signals, similar to artificial eyes, and a thermistor can encode temperature intensity. Furthermore, the Hamilton energy (HE) function of neurons is calculated based on the Helmholtz’s theorem, and a self-regulation method is designed by applying the ratio of electric field energy to magnetic field energy to estimate the self-regulation of neurons. The results show that the proposed neuron can reproduce the main characteristics of biological neurons by adjusting the external stimulus. The double coherence resonance is induced under noise temperature. These results could be helpful for researching the collective behaviors in functional neural networks.