Biological Cybernetics

, Volume 87, Issue 5, pp 404–415

Mathematical formulations of Hebbian learning

  • Wulfram Gerstner
  • Werner M. Kistler

DOI: 10.1007/s00422-002-0353-y

Cite this article as:
Gerstner, W. & Kistler, W. Biol Cybern (2002) 87: 404. doi:10.1007/s00422-002-0353-y


 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.

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Wulfram Gerstner
    • 1
  • Werner M. Kistler
    • 2
  1. 1.Swiss Federal Institute of Technology Lausanne, Laboratory of Computational Neuroscience, EPFL-LCN, 1015 Lausanne EPFL, SwitzerlandCH
  2. 2.Department of Neuroscience, Erasmus University, Rotterdam, The NetherlandsNL

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