title: Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission.
abstract: Maximization of information transmission by a spiking-neuron model predicts changes of synaptic connections that depend on timing of pre- and postsynaptic spikes and on the postsynaptic membrane potential. Under the assumption of Poisson firing statistics, the synaptic update rule exhibits all of the features of the Bienenstock-Cooper-Munro rule, in particular, regimes of synaptic potentiation and depression separated by a sliding threshold. Moreover, the learning rule is also applicable to the more realistic case of neuron models with refractoriness, and is sensitive to correlations between input spikes, even in the absence of presynaptic rate modulation. The learning rule is found by maximizing the mutual information between presynaptic and postsynaptic spike trains under the constraint that the postsynaptic firing rate stays close to some target firing rate. An interpretation of the synaptic update rule in terms of homeostatic synaptic processes and spike-timing-dependent plasticity is discussed.
authors.affiliation: Ecole Polytechnique Fédérale de Lausanne, School of Computer and Communication Sciences and Brain-Mind Institute, 1015 Lausanne, Switzerland.
authors.forename: Taro
authors.initials: T
authors.lastname: Toyoizumi
authors.forename: Jean-Pascal
authors.initials: JP
authors.lastname: Pfister
authors.forename: Kazuyuki
authors.initials: K
authors.lastname: Aihara
authors.forename: Wulfram
authors.initials: W
authors.lastname: Gerstner
mesh_terms: D000200:Action Potentials; D006706:Homeostasis; D008959:Models, Neurological; D009473:Neuronal Plasticity; D009474:Neurons / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology*