On-Line Learning with Structural Adaptation in a Network of Spiking Neurons for Visual Pattern Recognition

  • Simei Gomes Wysoski
  • Lubica Benuskova
  • Nikola Kasabov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4131)


This paper presents an on-line training procedure for a hierarchical neural network of integrate-and-fire neurons. The training is done through synaptic plasticity and changes in the network structure. Event driven computation optimizes processing speed in order to simulate networks with large number of neurons. The training procedure is applied to the face recognition task. Preliminary experiments on a public available face image dataset show the same performance as the optimized off-line method. A comparison with other classical methods of face recognition demonstrates the properties of the system.


Face Recognition Learning Procedure Structural Adaptation False Acceptance Rate False Rejection Rate 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Simei Gomes Wysoski
    • 1
  • Lubica Benuskova
    • 1
  • Nikola Kasabov
    • 1
  1. 1.Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand

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