{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T22:36:17Z","timestamp":1763764577616},"reference-count":35,"publisher":"MIT Press - Journals","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neural Computation"],"published-print":{"date-parts":[[2009,12]]},"abstract":"<jats:p> 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. <\/jats:p>","DOI":"10.1162\/neco.2009.12-08-929","type":"journal-article","created":{"date-parts":[[2009,9,18]],"date-time":"2009-09-18T16:59:40Z","timestamp":1253293180000},"page":"3408-3428","source":"Crossref","is-referenced-by-count":8,"title":["Learning to Discriminate Through Long-Term Changes of Dynamical Synaptic Transmission"],"prefix":"10.1162","volume":"21","author":[{"given":"Christian","family":"Leibold","sequence":"first","affiliation":[{"name":"Division of Neurobiology, University of Munich, 82152 Planegg-Martinsried, Germany, and Bernstein Center for Computational Neuroscience Munich, 82152 Planegg-Martinsried, Germany"}]},{"given":"Michael H. 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