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Using patterns of firing neurons in spiking neural networks for learning and early recognition of spatio-temporal patterns

  • Computational Intelligence for Vision and Robotics
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Abstract

In this paper, we propose a novel unsupervised learning approach for spatio-temporal pattern classification. We use a spike timing neural network with axonal conductance delays to learn the structure of spatio-temporal patterns from a small set of training samples and then use this network for classifying unseen patterns. The method also enables early classification of patterns, before they are completely observed. To transform a spatio-temporal pattern into a suitable form for the spiking network, we create a mapping process that transforms it into a spike train that can be used to train the network through spike-timing-dependent plasticity. Based on the trained network, we build models of the training samples as strings of characters in which each character represents a set of neurons that fire at a particular time step, in response to the pattern. For classification, we compute the longest common subsequence between the model strings corresponding to the input and training samples and choose the class of the training sample with highest similarity. This method is evaluated on a handwritten digits dataset with spatio-temporal information. We show that this method is robustly detecting the correct class early on. Comparison with one unsupervised and eight other supervised approaches shows that our proposed approach could compete or has better performance rather than all of them. Further analysis show that even for misclassified samples, our system can detect the correct class among the top three class labels.

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Acknowledgment

This work has been supported by ONR Award N000141210860.

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Authors and Affiliations

  1. Computer Science and Engineering Department, University of Nevada, Reno (UNR), Reno, NV, USA

    Banafsheh Rekabdar, Monica Nicolescu, Mircea Nicolescu & Sushil Louis

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  1. Banafsheh Rekabdar
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  2. Monica Nicolescu
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  3. Mircea Nicolescu
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  4. Sushil Louis
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Correspondence to Banafsheh Rekabdar.

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Rekabdar, B., Nicolescu, M., Nicolescu, M. et al. Using patterns of firing neurons in spiking neural networks for learning and early recognition of spatio-temporal patterns. Neural Comput & Applic 28, 881–897 (2017). https://doi.org/10.1007/s00521-016-2283-y

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  • DOI: https://doi.org/10.1007/s00521-016-2283-y

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