Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Neural Computation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Real-time computation at the edge of chaos in recurrent neural networks
Neural Computation
A Hebbian-based reinforcement learning framework for spike-timing-dependent synapses
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
A reinforcement learning framework for spiking networks with dynamic synapses
Computational Intelligence and Neuroscience
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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.