Computational Modeling with Spiking Neural Networks

  • Stefan Schliebs
  • Nikola Kasabov
Part of the Springer Handbooks book series (SHB)


This chapter reviews recent developments in the area of spiking neural networks (SNN) and summarizes the main contributions to this research field. We give background information about the functioning of biological neurons, discuss the most important mathematical neural models along with neural encoding techniques, learning algorithms, and applications of spiking neurons. As a specific application, the functioning of the evolving spiking neural network (eSNN) classification method is presented in detail and the principles of numerous eSNN based applications are highlighted and discussed.


Spike Train Output Neuron Synaptic Weight Firing Time Spike Neural Network 
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.



deoxyribonucleic acid


field-programmable gate array


gamma-aminobutyric acid


leaky integrate-and-fire neuron


liquid state machine


long-term depression


long-term potentiation


mel-frequency cepstral coefficient


multilayer perceptron


post-synaptic potential


remote supervised method


spiking neural network


spike response model


spike-timing dependent plasticity


evolving spiking neural network


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Copyright information

© Springer-Verlag 2014

Authors and Affiliations

  1. 1.School of Computing and Mathematical SciencesAuckland University of TechnologyAucklandNew Zealand
  2. 2.KEDRI – Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand

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