An analysis of the use of Hebbian and Anti-Hebbian spike time dependent plasticity learning functions within the context of recurrent spiking neural networks
An analysis of the use of Hebbian and Anti-Hebbian spike time dependent plasticity learning functions within the context of recurrent spiking neural networks
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DOI：
10.1016/j.neucom.2008.07.012
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发表时间：
2009-01-01
期刊：
影响因子：
6
通讯作者：
Carnell, Andrew
中科院分区：
文献类型：
--
作者：
Carnell, Andrew
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. (C) 2008 Elsevier B.V. All rights reserved.
