Evolving Probabilistic Spiking Neural Networks for Spatio-temporal Pattern Recognition: A Preliminary Study on Moving Object Recognition

  • Nikola Kasabov
  • Kshitij Dhoble
  • Nuttapod Nuntalid
  • Ammar Mohemmed
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7064)

Abstract

This paper proposes a novel architecture for continuous spatio-temporal data modeling and pattern recognition utilizing evolving probabilistic spiking neural network ‘reservoirs’ (epSNNr). The paper demonstrates on a simple experimental data for moving object recognition that: (1) The epSNNr approach is more accurate and flexible than using standard SNN; (2) The use of probabilistic neuronal models is superior in several aspects when compared with the traditional deterministic SNN models, including a better performance on noisy data.

Keywords

Spatio-Temporal Patterns Spiking Neural Network Reservoir Computing Liquid State Machine 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Nikola Kasabov
    • 1
    • 2
    • 3
  • Kshitij Dhoble
    • 1
  • Nuttapod Nuntalid
    • 1
  • Ammar Mohemmed
    • 1
  1. 1.Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand
  2. 2.Institute for NeuroinformaticsUniversity of ZurichSwitzerland
  3. 3.ETH ZurichSwitzerland

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