Computational Modeling with Spiking Neural Networks

Abstract

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.

Abbreviations

DNA

deoxyribonucleic acid

FPGA

field-programmable gate array

GABA

gamma-aminobutyric acid

LIF

leaky integrate-and-fire neuron

LSM

liquid state machine

LTD

long-term depression

LTP

long-term potentiation

MFCC

mel-frequency cepstral coefficient

MLP

multilayer perceptron

PSP

post-synaptic potential

ReSuMe

remote supervised method

SNN

spiking neural network

SRM

spike response model

STDP

spike-timing dependent plasticity

eSNN

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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