Performance Evaluation of a Temporal Sequence Learning Spiking Neural Network

Abstract

The performance evaluation of a temporal sequence learning spiking neural network was carried out. Neural network characteristics that were evaluated included: long temporal sequence length recognition, factors that affect size of the neural network, and network robustness against random input noise. Music melodies of various lengths were used as temporal sequential input data for the evaluation. Results have shown that the spiking neural network can be made to learn inter-spike time sequences comprised of as many as 900 inter-spike times. The size of the neural network was influenced by the amount and type of random noise used during the supervised learning phase. The spiking neural network system performance was approximately 90% accurate in recognizing sequences even in the presence of various types of random noise.