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Hebbian delay adaptation in a network of integrate-and-fire neurons

  • Christian W. Eurich
  • Jack D. Cowan
  • John G. Milton
Part I: Coding and Learning in Biology
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1327)

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.

Keywords

Delay Line Learning Rule Postsynaptic Neuron Interaural Time Difference Presynaptic Neuron 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Christian W. Eurich
    • 1
  • Jack D. Cowan
    • 2
  • John G. Milton
    • 3
  1. 1.Institut für Theoretische PhysikUniversität BremenBremenGermany
  2. 2.Departments of Mathematics and NeurologyThe University of ChicagoChicagoUSA
  3. 3.Department of Neurology and Committee on NeurobiologyThe University of ChicagoChicagoUSA

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