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Research Article Open access CC BY 4.0

Spiking Neural Network Learning Models for Spike Sequence Learning and Data Classification

Moses Apambila Agebure, Edward Yellakuor Baagyere, Elkanah Olaosebikan Oyetunji

Asian Journal of Research in Computer Science · pp. 1–17 · Published 9 Dec 2020

10.9734/ajrcos/2020/v6i430163

Abstract

Supervised learning in Spiking Neural Network (SNN) is a hotbed for researchers due to the advantages temporal coded networks provide over that of rate-coded networks with respect to efficiency in information processing and transfer rates. Supervised learning in rate-coded networks though well established, it is difficult to directly apply such models to SNN due to difference in information coding schemes. In this paper, we seek to exploit the advantages of spiking neural networks for spike sequence learning in order to establish two (2) models; batch and sequential learning models for solving data classification tasks. The models are built using the least squares approach leveraging on its approximation abilities. The first set of experiments are on spike sequence learning in which an extensive evaluation of the model is performed using different inputoutput firing rates and learning periods. Results from these experiments show that the proposed model for spike sequence learning produced better performance than some existing models derived for spike sequence learning, particularly, at higher learning periods. The proposed models for data classification are also tested on some selected benchmark datasets most of which had imbalance class distributions and also on real-world road condition datasets for anomaly classification collected by the authors as part of a larger study. While the proposed models generalised very well to all datasets including those with the class imbalance problem where F1 and Recall values above 0.90 were recorded, some well-know machine learning algorithms applied to the datasets yielded lower F1 and Recall values and in some cases recorded 0.0 Recall.

Spiking neural network Spike sequence learning data classification class imbalance learning least squares method.

References (7)

  1. 1 Deep learning in spiking neural networks [DOI]
  2. 2 Supervised Learning in Multilayer Spiking Neural Networks [DOI]
  3. 3 A Supervised Learning Algorithm for Learning Precise Timing of Multiple Spikes in Multilayer Spiking Neural Networks [DOI]
  4. 4 Evolving Spiking Neural Networks for Recognition of Aged Voices [DOI]
  5. 5 EVOLUTIONARY DESIGN OF SPIKING NEURAL NETWORKS [DOI]
  6. 6 EDL: An Extended Delay Learning Based Remote Supervised Method for Spiking Neurons [DOI]
  7. 7 ADAPTIVE SWITCHING CIRCUITS [DOI]

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