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Computational Modeling with Spiking Neural Networks

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Springer Handbook of Bio-/Neuroinformatics

Part of the book series: Springer Handbooks ((SHB))

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.

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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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Authors and Affiliations

  1. School of Computing and Mathematical Sciences, Auckland University of Technology, 1142, Auckland, New Zealand

    Stefan Schliebs

  2. KEDRI – Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, 120 Mayoral Drive, Auckland, New Zealand

    Nikola Kasabov

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  1. Stefan Schliebs
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  1. KEDRI – Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, 120 Mayoral Drive, 1010, Auckland, New Zealand

    Nikola Kasabov

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Schliebs, S., Kasabov, N. (2014). Computational Modeling with Spiking Neural Networks. In: Kasabov, N. (eds) Springer Handbook of Bio-/Neuroinformatics. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30574-0_37

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