Author(s): Lubica Benuskova (Author), Nikola K. Kasabov (Author)
Publisher: Springer
Publication Date: November 19, 2010
Edition: Softcover reprint of hardcover 1st ed. 2007
Language: English
Print length: 302 pages
ISBN-10: 1441943013
ISBN-13: 9781441943019
Book Description
This book is introducing the scope and problems of a new scientific discipline – Computational Neurogenetic Modeling (CNGM). CNGM is concerned with the study and development of dynamic neuronal models for modeling brain functions with respect to genes and dynamic interactions between genes. These include neural network models and their integration with gene network models. This new area brings together knowledge from various scientific disciplines like artificial neural networks, neuroscience, and molecular biology.
Editorial Reviews
Review
True and effective – and beyond comprehension? Book review by Ladislav Kováč in BioEssays, Volume 31, Issue 3, pages 363-364, March 2009 –can be read at URL: onlinelibrary.wiley.com/doi/10.1002/bies.200800211/full
From the Inside Flap
The book of Benuskova and Kasabov is a substantial reinforcement of the view that genes are not autonomous agents but function within networks.
From the Back Cover
With the presence of a large amount of both brain and gene data related to brain functions and diseases, it will soon be required that sophisticated computational neurogenetic models be created to aid 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 for integration of knowledge, data and information from genetics, bioinfomatics and neuroscience.
Computational Neurogenetic Modeling is a “manifesto” 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.
Key Topics Include: Brain Information Processing Methods of Computational Intelligence, including: Artificial Neural Networks & Evolutionary Computation Gene Information Processing Methodologies for Building Computational Neurogenetic Models Applications of CNGM for modeling brain functions and diseases
Computational Neurogenetic Modeling is recommended for postgraduate students and researchers in the areas of information sciences, artificial intelligence, neurosciences, bioinformatics and cognitive sciences. This volume is structured so that every chapter can be used as a reading material for research oriented courses at a postgraduate level.
About the Author
Lubica Benuskova is currently Senior Research Fellow at the Knowledge Engineering & Discovery Research Institute (KEDRI, kedri.info), Auckland University of Technology (AUT) in Auckland, New Zealand. She is also Associate Professor of Applied Informatics at the Faculty of Mathematics, Physics and Informatics at Comenius (Komensky) University in Bratislava, Slovakia. Her research interests are in the areas of computational neuroscience, cognitive science, neuroinformatics, computer and information sciences.
Nikola Kasabov is the Founding Director and Chief Scientist of KEDRI, and a Professor and Chair of Knowledge Engineering at the School of Computer and Information Sciences at AUT. He is a leading expert in computational intelligence and knowledge engineering and has published more than 400 papers, books and patents in the areas of neural and hybrid intelligent systems, bioinformatics and neuroinformatics, speech-, image and multimodal information processing. He is a Fellow of the Royal Society of New Zealand, Senior Member of IEEE, Vice President of the International Neural Network Society and a Past President of the Asia-Pacific Neural Network Assembly.
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