Hebbian delay adaptation in a network of integrate-and-fire neurons
We study the synchronization properties of a neural network which incorporates time delays. Two layers of integrate-and-fire neurons are connected by delay lines and a Hebbian-type learning rule is applied to allow a self-organizing, adaptive modification of the delays. It is shown that when the network synchronizes to a periodic input of period T, the delays differ by multiples of T. The delay dynamics possess an (N + 1)-parameter set of fixed points which is locally attracting. Neural networks with delay adaptation may have applications as noise reduction algorithms and for the control of time-delayed dynamical systems.
KeywordsDelay Line Learning Rule Postsynaptic Neuron Interaural Time Difference Presynaptic Neuron
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