An Analysis Of The Use Of Hebbian And Anti-hebbian Spike Time Dependent Plasticity Learning Functions Within The Context Of Recurrent Spiking Neural Networks

摘要

It is shown that the application of a form of spike time dependent plasticity (STDP) within a highly recurrent spiking neural net based upon the LSM leads to an approximate convergence of the synaptic weights. Convergence is a desirable property as it signifies a degree of stability within the network. An activity link L is defined which describes the link between the spiking activity on a connection and the weight change of the associated synapse. It is shown that under specific conditions Hebbian and Anti-Hebbian learning can be considered approximately equivalent. Also, it is shown that such a network habituates to a given stimulus and is capable of detecting subtle variations in the structure of the stimuli itself.