title: Matching recall and storage in sequence learning with spiking neural networks.
abstract: Storing and recalling spiking sequences is a general problem the brain needs to solve. It is, however, unclear what type of biologically plausible learning rule is suited to learn a wide class of spatiotemporal activity patterns in a robust way. Here we consider a recurrent network of stochastic spiking neurons composed of both visible and hidden neurons. We derive a generic learning rule that is matched to the neural dynamics by minimizing an upper bound on the Kullback-Leibler divergence from the target distribution to the model distribution. The derived learning rule is consistent with spike-timing dependent plasticity in that a presynaptic spike preceding a postsynaptic spike elicits potentiation while otherwise depression emerges. Furthermore, the learning rule for synapses that target visible neurons can be matched to the recently proposed voltage-triplet rule. The learning rule for synapses that target hidden neurons is modulated by a global factor, which shares properties with astrocytes and gives rise to testable predictions.
authors.affiliation: Department of Physiology, and Center for Cognition, Learning, and Memory, University of Bern, CH-3012 Bern, Switzerland. johannibrea@gmail.com
authors.forename: Johanni
authors.initials: J
authors.lastname: Brea
authors.forename: Walter
authors.initials: W
authors.lastname: Senn
authors.forename: Jean-Pascal
authors.initials: JP
authors.lastname: Pfister
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D007858:Learning / Q000502:physiology; D011939:Mental Recall / Q000502:physiology*; D008959:Models, Neurological; D016571:Neural Networks, Computer; D013569:Synapses / Q000502:physiology