An evolutionary strategy for supervised training of biologically plausible neural networks

Spiking neural networks represent a more plausible model of real biological neurons. In contrast to the classical artificial neural networks, which adopt a high abstraction of real neurons, spiking neurons consider time as an important feature for information representation and processing. However, good training algorithms are needed for better exploitation of these realistic models. Most existing learning paradigms adjust the synaptic weights in an unsupervised way based on the adaptation of the famous Hebbian rule. In this paper a new approach for supervised training is presented with a biologically plausible architecture. An adapted evolutionary strategy is used for adjusting the synaptic strengths and delays, which are responsible for learning the model of spike trains fed to the input neurons. The algorithm is applied to complex nonlinearly separable problems, and the results show that the network is able to perform learning successfully using temporal encoding.