title: Predictive learning of temporal sequences in recurrent neocortical circuits.
abstract: When a spike is initiated near the soma of a cortical pyramidal neuron, it may back-propagate up dendrites toward distal synapses, where strong depolarization can trigger spike-timing dependent Hebbian plasticity at recently activated synapses. We show that (a) these mechanisms can implement a temporal-difference algorithm for sequence learning, and (b) a population of recurrently connected neurons with this form of synaptic plasticity can learn to predict spatiotemporal input patterns. Using biophysical simulations, we demonstrate that a network of cortical neurons can develop direction selectivity similar to that observed in complex cells in alert monkey visual cortex as a consequence of learning to predict moving stimuli.
authors.affiliation: Sloan Center for Theoretical Neurobiology, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA.
authors.forename: R P
authors.initials: RP
authors.lastname: Rao
authors.forename: T J
authors.initials: TJ
authors.lastname: Sejnowski
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D007858:Learning / Q000502:physiology*; D019579:Neocortex / Q000166:cytology / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D013997:Time Factors