An analysis of the use of Hebbian and Anti-Hebbian spike time dependent plasticity learning functions within the context of recurrent spiking neural networks

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 linkL 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.

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