Computational Neurogenetic Modeling / Edition 1

Computational Neurogenetic Modeling / Edition 1

by Lubica Benuskova, Nikola Kasabov
     
 

ISBN-10: 0387483535

ISBN-13: 9780387483535

Pub. Date: 05/28/2007

Publisher: Springer US

Lubica Benuskova and Nikola Kasabov With the presence of a great amount of both brain and gene data related to brain functions and diseases, it is required that sophisticated computational neurogenetic models be created to facilitate new discoveries that will help researchers in understanding the brain in its complex interaction between genetic and neuronal

Overview

Lubica Benuskova and Nikola Kasabov With the presence of a great amount of both brain and gene data related to brain functions and diseases, it is required that sophisticated computational neurogenetic models be created to facilitate new discoveries that will help researchers in understanding the brain in its complex interaction between genetic and neuronal processes. Initial steps in this direction are underway, using the methods of computational intelligence to integrate knowledge, data and information from genetics, bioinfomatics and neuroscience. Computational Neurogenetic Modeling offers the knowledge base for creating such models covering the areas of neuroscience, genetics, bioinformatics and computational intelligence. This multidisciplinary background is then integrated into a generic computational neurogenetic modeling methodology. computational neurogenetic models offer vital applications for learning and memory, brain aging and Alzheimer's disease, Parkinson's disease, mental retardation, schizophrenia and epilepsy.

Product Details

ISBN-13:
9780387483535
Publisher:
Springer US
Publication date:
05/28/2007
Series:
Topics in Biomedical Engineering. International Book Series
Edition description:
2007
Pages:
290
Product dimensions:
6.10(w) x 9.25(h) x 0.03(d)

Table of Contents

Computational Neurogenetic Modeling (CNGM): A Brief Introduction.- Organization and Functions of the Brain.- Neuro-Information Processing in the Brain.- Artificial Neural Networks (ANN).- Evolving Connectionist Systems (ECOS).- Evolutionary Computation for Model and Feature Optimization.- Gene/Protein Interactions — Modeling Gene Regulatory Networks (GRN).- CNGM as Integration of GPRN, ANN and Evolving Processes.- Application of CNGM to Learning and Memory.- Applications of CNGM and Future Development.

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