Reservoir-Based Evolving Spiking Neural Network for Spatio-temporal Pattern Recognition

  • Stefan Schliebs
  • Haza Nuzly Abdull Hamed
  • Nikola Kasabov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7063)


Evolving spiking neural networks (eSNN) are computational models that are trained in an one-pass mode from streams of data. They evolve their structure and functionality from incoming data. The paper presents an extension of eSNN called reservoir-based eSNN (reSNN) that allows efficient processing of spatio-temporal data. By classifying the response of a recurrent spiking neural network that is stimulated by a spatio-temporal input signal, the eSNN acts as a readout function for a Liquid State Machine. The classification characteristics of the extended eSNN are illustrated and investigated using the LIBRAS sign language dataset. The paper provides some practical guidelines for configuring the proposed model and shows a competitive classification performance in the obtained experimental results.


Spiking Neural Networks Evolving Systems Spatio-Temporal Patterns 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Stefan Schliebs
    • 1
  • Haza Nuzly Abdull Hamed
    • 1
    • 2
  • Nikola Kasabov
    • 1
    • 3
    • 4
  1. 1.KEDRI, Auckland University of TechnologyNew Zealand
  2. 2.Soft Computing Research GroupUniversiti Teknologi MalaysiaJohor BahruMalaysia
  3. 3.Institute for NeuroinformaticsETHSwitzerland
  4. 4.University of ZurichSwitzerland

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