Probabilistic versus Incremental Presynaptic Learning in Biologically Plausible Synapses

  • Francisco Javier Ropero Peláez
  • Diego Andina
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6686)

Abstract

In this paper, the presynaptic rule, a classical rule for hebbian learning, is revisited. It is shown that the presynaptic rule exhibits relevant synaptic properties like synaptic directionality, and LTP metaplasticity (long-term potentiation threshold metaplasticity). With slight modifications, the presynaptic model also exhibits metaplasticity of the long-term depression threshold, being also consistent with Artola, Brocher and Singer’s (ABS) influential model. Two asymptotically equivalent versions of the presynaptic rule were adopted for this analysis: the first one uses an incremental equation while the second, conditional probabilities. Despite their simplicity, both types of presynaptic rules exhibit sophisticated biological properties, specially the probabilistic version.

Keywords

Metaplasticity ABS rule NMDA channel BCM rule 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Francisco Javier Ropero Peláez
    • 1
  • Diego Andina
    • 2
  1. 1.Center for Mathematics, Computation and CognitionFederal University of ABCBrazil
  2. 2.Group for automation in signals and communication.UPMSpain

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