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Description
Mathematical Neuroscience is a book for mathematical biologists seeking to discover the complexities of brain dynamics in an integrative way. It is the first research monograph devoted exclusively to the theory and methods of nonlinear analysis of infinite systems based on functional analysis techniques arising in modern mathematics.
Neural models that describe the spatio-temporal evolution of coarse-grained variables—such as synaptic or firing rate activity in populations of neurons —and often take the form of integro-differential equations would not normally reflect an integrative approach. This book examines the solvability of infinite systems of reaction diffusion type equations in partially ordered abstract spaces. It considers various methods and techniques of nonlinear analysis, including comparison theorems, monotone iterative techniques, a truncation method, and topological fixed point methods. Infinite systems of such equations play a crucial role in the integrative aspects of neuroscience modeling.
Key Features
- The first focused introduction to the use of nonlinear analysis with an infinite dimensional approach to theoretical neuroscience
- Combines functional analysis techniques with nonlinear dynamical systems applied to the study of the brain
- Introduces powerful mathematical techniques to manage the dynamics and challenges of infinite systems of equations applied to neuroscience modeling
Readership
neuroscience, theoretical neuroscience, applied mathematics
Table of Contents
About the Authors
Foreword
Preface
I Methods of Nonlinear Analysis
1: Introduction to Part I
2: Preliminary Considerations
2.1 Sets and Domains
2.2 Banach and Hölder Spaces
2.3 Cones and Ordered Spaces
2.4 Ellipticity and Parabolicity
2.5 Notations of Functional Dependence
2.6 Initial and Boundary Conditions
2.7 Fundamental Assumptions and Conditions
2.8 Lower and Upper Solutions
2.9 Stability of Solutions of Infinite Systems
3: Differential Inequalities
3.1 Introduction
3.2 Comparison Theorems for Finite Systems
3.3 Maximum Principles for Finite Systems
3.4 Comparison Theorems for Infinite Systems
3.5 Infinite Systems of Nonlinear Differential Inequalities
3.6 Ellipticity and Parabolicity of Nonlinear Inequalities
3.7 Weak Differential Inequalities for Infinite Systems
3.8 Strong Differential Inequalities for Infinite Systems
4: Monotone Iterative Methods
4.1 Method of Direct Iterations
4.2 Chaplygin Method
4.3 Certain Variants of the Chaplygin Method
4.4 Certain Variants of Monotone Iterative Methods
4.5 Another Variant of the Monotone Iterative Method
4.6 Method of Direct Iterations in Unbounded Domains
5: Methods of Lower and Upper Solutions
5.1 Some Remarks in Connection with Applications of Numerical Methods
5.2 On Constructions of Upper and Lower Solutions
5.3 Positive Solutions
5.4 Some Remarks on Strongly Coupled Systems
5.5 Estimation of Convergence Speed for Different Iterative Methods
6: Truncation Method
6.1 Introduction
6.2 Truncation Method for Infinite Countable Systems
6.3 Truncation Method for Infinite Uncountable Systems
6.4 Relation Between Continuous and Discrete Infinite-Dimensional Models
6.5 Conclusion
<Details
- No. of pages:
- 208
- Language:
- English
- Copyright:
- © 2013
- Published:
- 12th July 2013
- Imprint:
- Academic Press
- eBook ISBN:
- 9780124104822
- Print ISBN:
- 9780124114685
About the authors
Stanislaw Brzychczy
Affiliations and Expertise
Head, Department of Differential Equations, Faculty of Applied Mathematics, AGH University of Science and Technology, Krakow, Poland
Roman Poznanski
Roman R. Poznanski develops theories in neuroscience with mathematics. He recently co-authored the book, “Mathematical Neuroscience.” His passion remains to pinpoint and crack open complex problems holding back our understanding of how the brain works. He is adamant, that a new Einstein in neuroscience will one day produce a complete theory of the brain, not as a hodgepodge of models, but as an integrative theory expressed in terms of modern mathematics. He has over 20 years experience as a theoretician and modeller in the neurosciences. He has edited several contemporary books: Biophysical Neural Networks (2001) and Modeling in the Neurosciences (1999, 2005). He is currently a visiting professor at the Rockefeller University.
Affiliations and Expertise
Chief Editor, Journal of Integrative Neuroscience, Imperial College Press
Reviews
"Brzychczy,… Kraków and Poznanski…present methods of nonlinear functional analysis and their application to neuroscience. This is the first book, they say, to compile methods of nonlinear analysis to better understand the dynamics associated with solutions of infinite systems of equations. It would be suitable as a textbook for a one-semester graduate course in mathematical neuroscience for neuroscience students seeking tools and mathematics students looking for applications."--Reference & Research Book News, December 2013