Paper on Computational neurogenetic modelling

[Nik Kasabov]

The  following paper on Computational Neurogenetic Modelling:

N.Kasabov, L.Benuskova, Computational Neurogenetics, Journal of
Theoretical and Computational Nanoscience, vol.1 (1) American
Scientific Publisher, 2004, in print, to appear in March, 

is now available as a .pdf file from: 
http://www.aut.ac.nz/research_showcase/research_activity_areas/kedri/downloads/pdf/KasBen_CNG-JCTNanoscience04.pdf



Abstract:

The aim of the paper is to introduce the scope and the problems of a
new research area called Computational Neurogenetics (CNG), along with
some solutions and directions for further research. CNG is concerned
with the study and the development of dynamic neuronal models
integrated with gene models. This area brings together knowledge from
various science disciplines, such as computer and information science,
neuroscience and cognitive study, genetics and molecular biology.  A
computational neurogenetic model is created to model a brain function
or a brain disease manifestation, or to be used as a general
mathematical model for solving complex scientific and engineering
problems. The CNG area goes beyond modelling simple relationship
between a single gene and a single neuronal function or a neuronal
parameter. It is the interaction between hundreds and thousands of
genes in a neuron and their relationship with the functioning of a
neuronal ensemble and the brain as a whole (e.g., learning and memory,
speech and vision, epilepsy, mental retardation, aging, neural stem
cells, etc.). The CNG models constitute a new generation of neural
network models that are closer to biological neural networks in their
complex symbiosis of neuronal learning dynamics and molecular
processes. Concrete models are presented as examples - evolving
connectionist systems (ECOS) with evolutionary parameter optimisation
and the CNG model of a class of spiking neural network ensembles 
(CNG-SNN). 

Keywords: computational neurogenetics; neural networks; gene
regulatory networks; brain study; adaptive learning; computational
modelling. 
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regards
Nik Kasabov


Prof. Nik Kasabov, MSc, PhD
FRSNZ, FNZCS, SrMIEEE
Founding Director and Chief Scientist - 
Knowledge Engineering and Discovery Research Institute, KEDRI
Chair of Knowledge Engineering, School of Computer and Information
Sciences 
Auckland University of Technology 
phone: +64 9 917 9506 ; fax: +64 9 917 9501
WWW http://www.kedri.info
email: nkasabov at aut.ac.nz