摘要
This paper aims at exploring computational properties of dynamic processes in neu-ral systems,studying their mathematical formulation,and applying the results to artificial neuralnetwork modeling.The stimulus-response processes in neurons are first introduced briefly,thenproperties of neurons described by the Hodgkin-Huxley equations are analyzed.After studyinghow to simplify,the Hodgkin-Huxley equations while maintaining its properties,the concept of dy-namic neuron model is proposed.It is pointed out that the neuron model should include internalstates in order to obtain time-variant thresholds,such as refractory periods of neurons.Finallywe discuss problems related to neural network models based on pulse-stream communication andthe contribution of intraneuronal dynamics to collective properties of the neural network.
This paper aims at exploring computational properties of dynamic processes in neu- ral systems,studying their mathematical formulation,and applying the results to artificial neural network modeling.The stimulus-response processes in neurons are first introduced briefly,then properties of neurons described by the Hodgkin-Huxley equations are analyzed.After studying how to simplify,the Hodgkin-Huxley equations while maintaining its properties,the concept of dy- namic neuron model is proposed.It is pointed out that the neuron model should include internal states in order to obtain time-variant thresholds,such as refractory periods of neurons.Finally we discuss problems related to neural network models based on pulse-stream communication and the contribution of intraneuronal dynamics to collective properties of the neural network.
