Abstract
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.
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© 1997 Springer-Verlag Berlin Heidelberg
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Eurich, C.W., Cowan, J.D., Milton, J.G. (1997). Hebbian delay adaptation in a network of integrate-and-fire neurons. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020149
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DOI: https://doi.org/10.1007/BFb0020149
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