How spiking neurons give rise to a temporal-feature map: From synaptic plasticity to axonal selection

Christian Leibold, Richard Kempter, and J. Leo van Hemmen
Phys. Rev. E 65, 051915 – Published 17 May 2002
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Abstract

A temporal-feature map is a topographic neuronal representation of temporal attributes of phenomena or objects that occur in the outside world. We explain the evolution of such maps by means of a spike-based Hebbian learning rule in conjunction with a presynaptically unspecific contribution in that, if a synapse changes, then all other synapses connected to the same axon change by a small fraction as well. The learning equation is solved for the case of an array of Poisson neurons. We discuss the evolution of a temporal-feature map and the synchronization of the single cells’ synaptic structures, in dependence upon the strength of presynaptic unspecific learning. We also give an upper bound for the magnitude of the presynaptic interaction by estimating its impact on the noise level of synaptic growth. Finally, we compare the results with those obtained from a learning equation for nonlinear neurons and show that synaptic structure formation may profit from the nonlinearity.

  • Received 23 May 2001

DOI:https://doi.org/10.1103/PhysRevE.65.051915

©2002 American Physical Society

Authors & Affiliations

Christian Leibold, Richard Kempter, and J. Leo van Hemmen

  • Physik Department, Technische Universität München, D-85747 Garching bei München, Germany

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Issue

Vol. 65, Iss. 5 — May 2002

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