Change of Memory Formation According to STDP in a Continuous-Time Neural Network Model

摘要

Gerstner and colleagues have proposed a learning rule in which the incrementation of synaptic weight is adjusted according to the time difference between neuron firing and spike arrival. In this study, a continuous-time associative memory model is constructed by using a learning rule based on that idea, and the functions of the learning rule are investigated. First, a continuous-time associative memory model is constructed on the basis of the learning rule in continuous time, in which the neuron can store memory as the synchronous firing dynamics of the neuron. A result is presented in which multiple memory patterns can be recalled simultaneously under the proposed model. Then, using the proposed learning rule, an attempt is made to compose a nesting structure formed by arbitrary memory patterns. Based on the above series of results, it is shown that the learning rule has the function of modifying the memory storage structure according to changes in the environment.