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Springer Handbook of Computational Intelligence pp 771–782Cite as

Evolving Connectionist Systems: From Neuro-Fuzzy-, to Spiking- and Neuro-Genetic

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Abstract

This chapter follows the development of a class of neural networks (GlossaryTerm

NN

) called evolving connectionist systems (GlossaryTerm

ECOS

). The term evolving is used here in its meaning of unfolding, developing, changing, revealing (according to the Oxford dictionary) rather than evolutionary. The latter represents processes related to populations and generations of them. An GlossaryTerm

ECOS

is a neural network-based model that evolves its structure and functionality through incremental, adaptive learning and self-organization during its lifetime. In principle, it could be a simple GlossaryTerm

NN

or a hybrid connectionist system. The latter is a system based on neural networks that also integrate other computational principles, such as linguistically meaningful explanation features of fuzzy rules, optimization techniques for structure and parameter optimization, quantum-inspired methods, and gene regulatory networks. The chapter includes definitions and examples of GlossaryTerm

ECOS

such as: evolving neuro-fuzzy and hybrid systems; evolving spiking neural networks, neurogenetic systems, quantum-inspired systems, which are all discussed from the point of view of the structural and functional development of a connectionist-based model and the knowledge that it represents. Applications for knowledge engineering across domain areas, such as in bioinformatics, brain study, and intelligent machines are presented.

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Abbreviations

CI:

computational intelligence

CNGM:

computational neuro-genetic modeling

DENFIS:

dynamic neuro-fuzzy inference system

deSNN:

dynamic eSNN

ECOS:

evolving connectionist system

EEG:

electroencephalogram

EFuNN:

evolving fuzzy neural network

eSNN:

evolving spiking neural network

ESOM:

evolving self-organized map

ETS:

evolving Takagi–Sugeno system

fMRI:

functional magneto-resonance imaging

FNN:

fuzzy neural network

GRN:

gene regulatory network

gene/protein regulatory network

LIFM:

leaky integrate-and-fire

NFI:

neuro-fuzzy inference system

NN:

neural network

QeSNN:

quantum-inspired eSNN

RBF:

radial basis function

SOM:

self-organizing map

SPAN:

spike pattern association neuron

SRM:

spike response model

STDP:

spike-timing dependent plasticity

T1:

type-1

T2:

type-2

TWNFI:

transductive weighted neuro-fuzzy inference system

WWKNN:

weighted-weighted nearest neighbor

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Author information

Authors and Affiliations

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

    Nikola Kasabov

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  1. Nikola Kasabov
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Correspondence to Nikola Kasabov .

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Editors and Affiliations

  1. Systems Research Inst., Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

  2. Dep. Electrical and Computer Engineering, University of Alberta, 116 Street 9107, T6J 2V4, Edmonton, Alberta, Canada

    Witold Pedrycz

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Kasabov, N. (2015). Evolving Connectionist Systems: From Neuro-Fuzzy-, to Spiking- and Neuro-Genetic. In: Kacprzyk, J., Pedrycz, W. (eds) Springer Handbook of Computational Intelligence. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43505-2_40

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  • DOI: https://doi.org/10.1007/978-3-662-43505-2_40

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