Spike-timing-dependent Hebbian plasticity as temporal difference learning

A spike-timing-dependent Hebbian mechanism governs the plasticity of recurr ent 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 predicti on of input sequences. Using a biophysical model of a cortical neuron, we s how that a temporal difference rule used in conjunction with dendritic back propagating action potentials reproduces the temporally asymmetric window o f Hebbian plasticity observed physiologically. Furthermore, the size and sh ape of the window vary with the distance of the synapse from the soma. Usin g a simple example, we show how a spike-timing-based temporal difference le arning rule can allow a network of neocortical neurons to predict an input a few milliseconds before the input's expected arrival.