Neural modeling with dynamically adjustable threshold and refractory period.

A variant of the FitzHugh-Nagumo model is proposed in order to fully make use of the computational properties of intraneuronal dynamics. The mechanisms of threshold and refractory periods 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 has also discussed 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 adaptive learning in neural networks and that molecular electronics probably provides an effective approach to implementing the above neuron model.