A synaptic learning rule based on the temporal coincidence of pre and postsynaptic activity

In biological neural networks, synaptic connections and their modification by Hebbian forms of associative learning have been shown in recent years to have quite complex dynamic characteristics. It is clear that in building neural networks of “spiking” neurons for spatio-temporal pattern learning and recognition, such dynamic characteristics may play an important role. We review the neuroscientific evidence for the dynamic characteristics of learning and memory, and propose a computational associative learning rule which takes account of this evidence. We show that the application of this learning rule allows us to mimic in a computationally simple way certain characteristics of the biological learning process, in particular temporal asymmetry effects similar to those observed experimentally