Learning to discriminate through long-term changes of dynamical synaptic transmission

Neural Comput. 2009 Dec;21(12):3408-28. doi: 10.1162/neco.2009.12-08-929.

Abstract

Short-term synaptic plasticity is modulated by long-term synaptic changes. There is, however, no general agreement on the computational role of this interaction. Here, we derive a learning rule for the release probability and the maximal synaptic conductance in a circuit model with combined recurrent and feedforward connections that allows learning to discriminate among natural inputs. Short-term synaptic plasticity thereby provides a nonlinear expansion of the input space of a linear classifier, whereas the random recurrent network serves to decorrelate the expanded input space. Computer simulations reveal that the twofold increase in the number of input dimensions through short-term synaptic plasticity improves the performance of a standard perceptron up to 100%. The distributions of release probabilities and maximal synaptic conductances at the capacity limit strongly depend on the balance between excitation and inhibition. The model also suggests a new computational interpretation of spikes evoked by stimuli outside the classical receptive field. These neuronal activities may reflect decorrelation of the expanded stimulus space by intracortical synaptic connections.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Discrimination, Psychological / physiology*
  • Humans
  • Learning / physiology*
  • Models, Neurological*
  • Nerve Net / physiology
  • Neural Inhibition / physiology
  • Neural Pathways / physiology
  • Neuronal Plasticity / physiology
  • Nonlinear Dynamics*
  • Photic Stimulation / methods
  • Principal Component Analysis
  • Synapses / physiology*
  • Synaptic Transmission / physiology*
  • Visual Fields / physiology