A mathematical neuron model with dynamically adjustable threshold and refractory period

Abstract

A variant of the BvP model is proposed. The mechanisms of threshold and refractory period resulting from the double dynamical processes are qualitatively studied through computer simulation. The results show that the variant neuron model has the property that its threshold, refractory period and response amplitude are dynamically adjustable. This paper also discusses some problems relating to collective property, learning and implementation of the neural network based on the neuron model proposed. It is noted that the implicit way to describe threshold and refractory period is advantageous to the adaptive learning in neural networks and that molecular electronics probably provides an effective approach to implementing the above neuron model.