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Biol Cybern. 2002 Dec;87(5-6):404-15.

Mathematical formulations of Hebbian learning.

Author information

1
Swiss Federal Institute of Technology Lausanne, Laboratory of Computational Neuroscience, EPFL-LCN, 1015 Lausanne EPFL, Switzerland. wulfram.gerstner@epfl.ch

Abstract

Several formulations of correlation-based Hebbian learning are reviewed. On the presynaptic side, activity is described either by a firing rate or by presynaptic spike arrival. The state of the postsynaptic neuron can be described by its membrane potential, its firing rate, or the timing of backpropagating action potentials (BPAPs). It is shown that all of the above formulations can be derived from the point of view of an expansion. In the absence of BPAPs, it is natural to correlate presynaptic spikes with the postsynaptic membrane potential. Time windows of spike-time-dependent plasticity arise naturally if the timing of postsynaptic spikes is available at the site of the synapse, as is the case in the presence of BPAPs. With an appropriate choice of parameters, Hebbian synaptic plasticity has intrinsic normalization properties that stabilizes postsynaptic firing rates and leads to subtractive weight normalization.

PMID:
12461630
DOI:
10.1007/s00422-002-0353-y
[Indexed for MEDLINE]

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