title: Theory of spike timing-based neural classifiers.
abstract: We study the computational capacity of a model neuron, the tempotron, which classifies sequences of spikes by linear-threshold operations. We use statistical mechanics and extreme value theory to derive the capacity of the system in random classification tasks. In contrast with its static analog, the perceptron, the tempotron's solutions space consists of a large number of small clusters of weight vectors. The capacity of the system per synapse is finite in the large size limit and weakly diverges with the stimulus duration relative to the membrane and synaptic time constants.
authors.affiliation: Racah Institute of Physics, Hebrew University, 91904 Jerusalem, Israel.
authors.forename: Ran
authors.initials: R
authors.lastname: Rubin
authors.forename: Rémi
authors.initials: R
authors.lastname: Monasson
authors.forename: Haim
authors.initials: H
authors.lastname: Sompolinsky
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D004560:Electricity; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D013997:Time Factors