title: Spike-timing-dependent Hebbian plasticity as temporal difference learning.
abstract: A spike-timing-dependent Hebbian mechanism governs the plasticity of recurrent excitatory synapses in the neocortex: synapses that are activated a few milliseconds before a postsynaptic spike are potentiated, while those that are activated a few milliseconds after are depressed. We show that such a mechanism can implement a form of temporal difference learning for prediction of input sequences. Using a biophysical model of a cortical neuron, we show that a temporal difference rule used in conjunction with dendritic backpropagating action potentials reproduces the temporally asymmetric window of Hebbian plasticity observed physio-logically. Furthermore, the size and shape of the window vary with the distance of the synapse from the soma. Using a simple example, we show how a spike-timing-based temporal difference learning rule can allow a network of neocortical neurons to predict an input a few milliseconds before the input's expected arrival.
authors.affiliation: Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195-2350, 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*; D008959:Models, Neurological; D019579:Neocortex / Q000502:physiology; D009473:Neuronal Plasticity / Q000502:physiology*; D011930:Reaction Time / Q000502:physiology; D013569:Synapses / Q000502:physiology