Brain, Gene, and Quantum Inspired Computational Intelligence

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


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.


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.



artificial intelligence


(amino-methylisoxazole-propionic acid) receptor


artificial neural network


brain-derived neurotrophic factor


brain-gene ontology


computational intelligence


chloride channel


computational neurogenetic model


dynamic neuro-fuzzy inference system


deoxyribonucleic acid


evolving connectionist system




evolving fuzzy neural network


fast Fourier transformation


fibroblast growth factor


genetic algorithm


gamma-aminobutyric acid


GABAA receptor


GABAB receptor


gene regulatory network


kalium (potassium) voltage-gated channel


local field potential


multilayer perceptron




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




post-synaptic potential




quantum inspired


quantum inspired methods of evolutionary computation


ribonucleic acid


sodium voltage-gated channel


spiking neural network


self-organizing map


spike response model


support vector machine


transductive weighted neuro-fuzzy inference engine


messenger RNA


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Copyright information

© Springer-Verlag 2014

Authors and Affiliations

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

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