Leibold, Christian; Bendels, Michael H. K.
Learning to Discriminate Through Long-Term Changes of Dynamical Synaptic Transmission.
In: Neural Computation, Vol. 21, No. 12: pp. 3408-3428
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 activitiesmay reflect decorrelation of the expanded
stimulus space by intracortical synaptic connections.