Biological Cybernetics

, Volume 96, Issue 5, pp 533–546 | Cite as

Spike-timing-dependent plasticity for neurons with recurrent connections

  • A. N. BurkittEmail author
  • M. Gilson
  • J. L. van Hemmen
Original Paper


The dynamics of the learning equation, which describes the evolution of the synaptic weights, is derived in the situation where the network contains recurrent connections. The derivation is carried out for the Poisson neuron model. The spiking-rates of the recurrently connected neurons and their cross-correlations are determined self- consistently as a function of the external synaptic inputs. The solution of the learning equation is illustrated by the analysis of the particular case in which there is no external synaptic input. The general learning equation and the fixed-point structure of its solutions is discussed.


Synaptic Weight Recurrent Network Hebbian Learning Recurrent Connection Output Spike 
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 2007

Authors and Affiliations

  • A. N. Burkitt
    • 1
    • 2
    • 3
    Email author
  • M. Gilson
    • 1
    • 2
  • J. L. van Hemmen
    • 4
  1. 1.The Bionic Ear InstituteEast MelbourneAustralia
  2. 2.Department of Electrical and Electronic EngineeringThe University of MelbourneMelbourneAustralia
  3. 3.Department of OtolaryngologyThe University of MelbourneMelbourneAustralia
  4. 4.Physik DepartmentTU MünchenGarching bei MünchenGermany

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