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2007 | Buch

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Computational Neurogenetic Modeling

verfasst von: Dr. Lubica Benuskova, Professor Nikola Kasabov

Verlag: Springer US

Buchreihe : Topics in Biomedical Engineering. International Book Series

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

Computational Neurogenetic Modeling is a student text, 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, such as computer and information science, neuroscience and cognitive science, genetics and molecular biology, as well as engineering.

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Inhaltsverzeichnis

Frontmatter

1. Computational Neurogenetic Modeling (CNGM): A Brief Introduction

Abstract

This chapter introduces the motivation and the main concepts of computational neurogenetic modeling (CNGM). It argues that with the presence of a large amount of both brain and gene data related to brain functions and diseases, it is required that sophisticated computational models are created to facilitate new knowledge discovery that helps understanding the brain in its complex interaction between genetic and neuronal processes. The chapter points to sources of data, information and knowledge related to neuronal and genetic processes in the brain. CNGM is concerned with the integration of all these diverse information into a computational model that can be used for modeling and prediction purposes. The models integrate knowledge from mathematical and information sciences (e.g. computational intelligence — CI), neurosciences, and genetics. The chapter also discusses what methods can be used for CNGM and how. The concepts and principles introduced in this chapter are presented in detail and illustrated in the rest of the book.

Lubica Benuskova, Nikola Kasabov

2. Organization and Functions of the Brain

Abstract

This chapter gives an overview of the brain organization and functions performed by different parts of the brain. We will try to answer the following questions: How is the human brain organized at the macroscopic and microscopic levels? Which functions are performed by the brain? How is the organization of the human brain related to its functions? These and many more other questions about the brain are still under investigation of thousands of neuroscientists allover the world. The first Nobel Prize for pioneering discoveries related to the brain microscopic organization was given to the Spanish scientist Santiago Ramón y Cajal (1852–1934) and Italian Camillo Golgi (1843–1926). These scientists are considered to be the founders of neuroscience and modem brain study. German physicist Herman Ludwig Ferdinand von Helmholtz (1821–1894) is the founder of psychophysics, that is a quantitative experimental and theoretical research of relations between mental and brain functions. Up to these days, the division of cerebral cortex based on its microstructure introduced by a neuroanatomist Korbinian Brodmann (1868–1918), is being used. Frenchman Paul Broca (1824–1880) and Russian Alexander Romanovich Luriya (1902–1977) pioneered research on brain localization of cognitive functions based upon cognitive deficits caused by brain lesions.

Lubica Benuskova, Nikola Kasabov

3. Neuro-Information Processing in the Brain

Abstract

While Chapter 2 presents the higher-level brain organization, this chapter presents a view on the low level information processing in the brain. Neuro-infonnation processing in the brain depends not only on the organization of the brain and properties of brain neural networks, but also on the properties of processing units — neurons and signal processing networks within neurons. These internal networks involved in signal processing are comprised of second and third messengers, enzymes, transcription factors and genes.

Lubica Benuskova, Nikola Kasabov

4. Artificial Neural Networks (ANN)

Abstract

This chapter introduces the basic principles of artificial neural networks (ANN) as computational models that mimic the brain in its main principles. Theyhavebeenused so far to model brain functions, along with solving complex problems of classification, prediction, etc. in all areas of science, engineering, technology and business. Here we present a classification scheme of the different types of ANN and some main existing models, namely self-organized maps (SOM), multilayer-perceptrons (MLP) and spiking neural networks (SNN). We illustrate their use to model brain functions, for instance the generation of electrical oscillations measured as LFP. Since ANNs are used as models of brain functions, they become an integral part of CNGM where gene interactions are introduced as part of the structure andthe functionality of ANN (see e.g. Chap. 8).

Lubica Benuskova, Nikola Kasabov

5. Evolving Connectionist Systems (ECOS)

Abstract

This chapter extends Chap. 4 and presents another type of ANNs that evolve their structure and functionality over time from incoming data and learn rules in an adaptive mode. They are called ECOS (Kasabov 2002b, Kasabov 2006). ECOS learn local models allocated to clusters of data that can be modified and created in an adaptive mode, incrementally. Several ECOS models are presented along with examples of their use to model brain and gene data.

Lubica Benuskova, Nikola Kasabov

6. Evolutionary Computation for Model and Feature Optimization

Abstract

This chapter introduces the main principles of evolutionary computation (EC) and presents a methodology for using it to optimize the parameters and the set of features (e.g. genes, brain signals) in a computational model. Evolutionary computation (EC) methods adopt principles from the evolution in Nature (Darwin 1859). EC methods are used in Chaps. 7 and 8 of the book to optimize gene interaction networks as part of a CNGM.

Lubica Benuskova, Nikola Kasabov

7. Gene/Protein Interactions — Modeling Gene Regulatory Networks (GRN)

Abstract

This chapter presents background knowledge from Bioinformatics on gene and protein information processing in a biological cell with the emphasis on their dynamic interaction. In a cell and in a neuron in particular, DNA, RNA and proteins interact continuously, affecting the functioning of the whole cell and the phenotype of an organism. Here, the main principles of these interactions are presented. Some of these interactions are subject to modeling in a CNGM when related to the output signals/functions of a cell/neuron.

Lubica Benuskova, Nikola Kasabov

8. CNGM as Integration of GPRN, ANN and Evolving Processes

Abstract

This chapter presents a methodology for CNGM that integrates gene regulatory networks with models of artificial neural networks to model different functions of neural system. Properties of all cell types, including neurons, are determined by proteins they contain (Lodish et al. 2000). In tum, the types and amounts of proteins are determined by differential transcription of different genes in response to internal and external signals. Eventually, the properties of neurons determine the structure and dynamics of the whole neural network they are part of. Interaction of genes in neurons affects the dynamics of the whole neural network model through neuronal parameters, which are no longer constant, but change as a function of gene expression. Through optimization of the gene interaction network, initial gene/protein expression values and neuronal parameters, particular target states of the neural network operation can be achieved, and meaningful relationships between genes, proteins and neural functions can be extracted.

Lubica Benuskova, Nikola Kasabov

9. Application of CNGM to Learning and Memory

Abstract

Before we introduce a tentative CNGM of learning and memory at a cellular level, we need to consider the relevant rules of synaptic plasticity, which represent knowledge about the basic mechanisms of cellular and molecular memory storage. Then we need to enhance these rules with connections to gene/protein regulatory network (GPRN) of genes and proteins that are relevant to learning and memory. Finally, a provisional CNGM of learning and memory will be constructed. In what follows, we will discuss only the activity-dependent plasticity of excitatory synaptic connections of the brain, because they are thought to mediate the information storage within biological neural networks (Kandel et al. 2000).

Lubica Benuskova, Nikola Kasabov

10. Applications of CNGM and Future Development

Abstract

This chapter presents information on neurogenetic causes of brain diseases and points to future directions of building CNGM for these diseases. With the advancement of molecular research technologies more and more data and information is available about the genetic basis of neuronal functions and diseases (see Table A.l in Appendix 1). This information can be utilized to create models of brain functions and diseases that include models of gene and protein interactions, i.e. computational neurogenetic models (CNGM) (Kasabov and Benuskova 2004,2005). This research has many open questions, some of them listed below we will attempt to address in this chapter

Lubica Benuskova, Nikola Kasabov

Backmatter

Titel: Computational Neurogenetic Modeling
verfasst von: Dr. Lubica Benuskova
Professor Nikola Kasabov
Copyright-Jahr: 2007
Verlag: Springer US
Electronic ISBN: 978-0-387-48355-9
Print ISBN: 978-0-387-48353-5
DOI: https://doi.org/10.1007/978-0-387-48355-9