Spike-timing-dependent Hebbian plasticity as temporal difference learning

Neural Comput. 2001 Oct;13(10):2221-37. doi: 10.1162/089976601750541787.

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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Action Potentials / physiology
  • Animals
  • Learning / physiology*
  • Models, Neurological*
  • Neocortex / physiology
  • Neuronal Plasticity / physiology*
  • Reaction Time / physiology
  • Synapses / physiology