Pattern recognition with spiking neural networks and dynamic synapses

Spiking neural networks represent a more plausible model of real biological neurons where time is considered as an important feature for information representation and processing in the human brain. In this paper, we apply spiking neural networks with dynamic synapses for pattern recognition in multidimensional data. The neurons are based on the integrate and-fire model, and are connected using a biologically plausible model of dynamic synapses. Unlike the conventional synapse employed in artificial neural networks, which is considered as a static entity with a fixed weight, the dynamic synapse (weightless synapse) efficacy changes upon the arrival of input spikes, and depends on the temporal structure of the impinging spike train. The training of the free parameters of the spiking network is performed using an evolutionary strategy (ES) where real values are used to encode the dynamic synapse parameters, which underlie the learning process.. The results show that spiking neurons with dynamic synapses are capable of pattern recognition by means of spatio-temporal encoding.