Integrated Feature and Parameter Optimization for an Evolving Spiking Neural Network

  • Stefan Schliebs
  • Michaël Defoin-Platel
  • Nikola Kasabov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5506)


This study extends the recently proposed Evolving Spiking Neural Network (ESNN) architecture by combining it with an optimization algorithm, namely the Versatile Quantum-inspired Evolutionary Algorithm (vQEA). Following the wrapper approach, the method is used to identify relevant feature subsets and simultaneously evolve an optimal ESNN parameter setting. Applied to carefully designed benchmark data, containing irrelevant and redundant features of varying information quality, the ESNN-based feature selection procedure lead to excellent classification results and an accurate detection of relevant information in the dataset. Redundant and irrelevant features were rejected successively and in the order of the degree of information they contained.


Feature Subset Neural Model Noise Strength Redundant Feature Optimal Feature Subset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Stefan Schliebs
    • 1
  • Michaël Defoin-Platel
    • 2
  • Nikola Kasabov
    • 1
  1. 1.Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyNew Zealand
  2. 2.Department of Computer ScienceUniversity of AucklandNew Zealand

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