Generalization properties of spiking neurons trained with ReSuMe method

In this paper we demonstrate the generalization property of spiking neurons trained with ReSuMe method. We show in a set of experiments that the learning neuron can approximate the input-output transformations defined by another reference neuron with a high precision and that the learning process converges very quickly. We discuss the relationship between the neuron I/O properties and the weight distribution of its input connections. Finally, we discuss the conditions under which the neuron can approximate some given I/O transformations.