Evolutionary Design Of Spiking Neural Networks

Unlike traditional artificial neural networks (ANNs), which use a high abstraction of real neurons, spiking neural networks (SNNs) offer a biologically plausible model of realistic neurons. They differ from classical artificial neural networks in that SNNs handle and communicate information by means of timing of individual pulses, an important feature of neuronal systems being ignored by models based on rate coding scheme. However, in order to make the most of these realistic neuronal models, good training algorithms are required. Most existing learning paradigms tune the synaptic weights in an unsupervised way using an adaptation of the famous Hebbian learning rule, which is based on the correlation between the pre- and post-synaptic neurons activity. Nonetheless, supervised learning is more appropriate when prior knowledge about the outcome of the network is available. In this paper, a new approach for supervised training is presented with a biologically plausible architecture. An adapted evolutionary strategy (ES) is used for adjusting the synaptic strengths and delays, which underlie the learning and memory processes in the nervous system. The algorithm is applied to complex non-linearly separable problems, and the results show that the network is able to perform learning successfully by means of temporal encoding of presented patterns.