Hebbian Learning in Networks of Spiking Neurons Using Temporal Coding

Computational tasks in biological systems that require short response times can be implemented in a straightforward way by networks of spiking neurons that encode analogue values in temporal coding. We investigate the question how spiking neurons can learn on the basis of differences between firing times. In particular, we provide learning rules of the Hebbian type in terms of single spiking events of the pre- and postsynaptic neuron and show that the weights approach some value given by the difference between pre- and postsynaptic firing times with arbitrary high precision. Our learning rules give rise to a straightforward possibility for realizing very fast pattern analysis tasks with spiking neurons.