Temporal data encoding and sequencelearning with spiking neural networks

Authors:
Robert H. Fujii;Kenjyu Oozeki
Affiliations:
School of Computer Science and Engineering, University of Aizu Fukushima Prefecture, Aizu-Wakamatsu;School of Computer Science and Engineering, University of Aizu Fukushima Prefecture, Aizu-Wakamatsu
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Year:
2006

Citing 3
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Abstract

Sequence Learning using a Spiking Neural Network (SNN) was performed. An SNN is a type of Artificial Neural Network (ANN) that uses input signal arrival time information to process temporal data. An SNN can learn not only combinational inputs but also sequential inputs over some limited amount of time without using a recurrent network. Music melodies were encoded using unit amplitude spikes having various inter-spike interval times. These spikes were then fed into an SNN learning system. The SNN learning system was able to recognize various melodies after learning. The SNN could identify the original and noise-added melody versions properly in most cases.