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Brain, Gene, and Quantum Inspired Computational Intelligence

  • Nikola Kasabov
Chapter

Abstract

This chapter discusses opportunities and challenges for the creation of methods of computational intelligence (CI) and more specifically – artificial neural networks (ANN), inspired by principles at different levels of information processing in the brain: cognitive, neuronal, genetic, and quantum, and mainly, the issues related to the integration of these principles into more powerful and accurate CI methods. It is demonstrated how some of these methods can be applied to model biological processes and to improve our understanding in the subject area; generic CI methods being applicable to challenging generic AI problems. The chapter first offers a brief presentation of some principles of information processing at different levels of the brain and then presents brain inspired, gene inspired, and quantum inspired CI. The main contribution of the chapter, however, is the introduction of methods inspired by the integration of principles from several levels of information processing, namely:
  1. 1.

    A computational neurogenetic model that in one model combines gene information related to spiking neuronal activities.

     
  2. 2.

    A general framework of a quantum spiking neural network (SNN) model.

     
  3. 3.

    A general framework of a quantum computational neurogenetic model (CNGM).

     
Many open questions and challenges are discussed, along with directions for further research.

Keywords

Artificial Neural Network Artificial Neural Network Model Gene Regulatory Network Local Field Potential Incremental Learning 
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.

Abbreviations

AI

artificial intelligence

AMPAR

(amino-methylisoxazole-propionic acid) receptor

ANN

artificial neural network

BDNF

brain-derived neurotrophic factor

BGO

brain-gene ontology

CI

computational intelligence

CLC

chloride channel

CNGM

computational neurogenetic model

DENFIS

dynamic neuro-fuzzy inference system

DNA

deoxyribonucleic acid

ECOS

evolving connectionist system

EEG

electroencephalography

EFuNN

evolving fuzzy neural network

FFT

fast Fourier transformation

FGF

fibroblast growth factor

GA

genetic algorithm

GABA

gamma-aminobutyric acid

GABRA

GABAA receptor

GABRB

GABAB receptor

GRN

gene regulatory network

KCN

kalium (potassium) voltage-gated channel

LFP

local field potential

MLP

multilayer perceptron

NMDA

N-methyl-d-aspartate

NMDAR

(N-methyl-d-aspartate acid) NMDA receptor

PS

presenilin

PSP

post-synaptic potential

PV

parvalbumin

QI

quantum inspired

QIEC

quantum inspired methods of evolutionary computation

RNA

ribonucleic acid

SCN

sodium voltage-gated channel

SNN

spiking neural network

SOM

self-organizing map

SRM

spike response model

SVM

support vector machine

TWNFI

transductive weighted neuro-fuzzy inference engine

mRNA

messenger RNA

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© Springer-Verlag 2014

Authors and Affiliations

  1. 1.KEDRI – Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand

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