Learning to discriminate through long-term changes of dynamical synaptic transmission.
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.
Leibold
Christian
C
Division of Neurobiology, University of Munich, 82152 Planegg-Martinsried, Germany, and Bernstein Center for Computational Neuroscience Munich, 82152 Planegg-Martinsried, Germany. leibold@bio.lmu.de
Bendels
Michael H K
MH
D000818:Animals; D004192:Discrimination, Psychological / Q000502:physiology*; D006801:Humans; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D009433:Neural Inhibition / Q000502:physiology; D009434:Neural Pathways / Q000502:physiology; D009473:Neuronal Plasticity / Q000502:physiology; D017711:Nonlinear Dynamics; D010775:Photic Stimulation / Q000379:methods; D025341:Principal Component Analysis; D013569:Synapses / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology*; D014794:Visual Fields / Q000502:physiology