Temporal pattern recognition using a spiking neural network with delays

Spiking neural networks have been shown to have powerful computation capability, but most results have been restricted to theoretical work. In this paper, we apply a spiking neural network to a time-series prediction problem, i.e., laser amplitude fluctuation data. We formulate the time-series problem as a spatio-temporal pattern recognition problem and present a learning method in which spatio-temporal patterns are recorded as synaptic delays. Experimental results show that the presented model is useful for temporal pattern recognition.