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Modeling in the Neurosciences

From Biological Systems to Neuromimetic Robotics

Edited by G. N. Reeke, R.R. Poznanski, K. A. Lindsay, J.R. Rosenberg, O. Sporns

Contributors: Dennis A. Turner, Jonathan Bell, Jaap van Pelt, Gilbert A. Chauvet, Robert Costalat, Jorn Hounsgaard, Jonathan D. Evans, Loyd L. Glenn, Mel D. Goldfinger, David Halliday, William R. Holmes, Yoshimi Kamiyama, George P. Moore, Roger Orpwood, Shunsuke Sato, Lina A. Shehadeh, Gregory D. Smith, Larry S. Liebovitch, Mike West, Harry B.M. Uylings, Parag G. Patil, Toshihiro Aoyama, Armuntas Baginskas, Helen Bronte-Stewart, Bruno Delord, Shinji Doi, Hidekazu Fukai, Aron M. Gutman, Akito Ishihara, Viktor K. Jirsa, Jeffrey R. Knisley, Taishin Nomura, Natasha Svirskiene, Gytis Svirskis, Gayle Tucker, Shiro Usui, Howard V. Wheal, Jeffrey L. Krichmar

Published March 29th 2005 by CRC Press – 736 pages

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Description

Computational models of neural networks have proven insufficient to accurately model brain function, mainly as a result of simplifications that ignore the physical reality of neuronal structure in favor of mathematically tractable algorithms and rules. Even the more biologically based "integrate and fire" and "compartmental" styles of modeling suffer from oversimplification in the former case and excessive discretization in the second. This book introduces an integrative approach to modeling neurons and neuronal circuits that retains the integrity of the biological units at all hierarchical levels.

With contributions from more than 40 renowned experts, Modeling in the Neurosciences, Second Edition is essential for those interested in constructing more structured and integrative models with greater biological insight. Focusing on new mathematical and computer models, techniques, and methods, this book represents a cohesive and comprehensive treatment of various aspects of the neurosciences from the molecular to the network level. Many state-of-the-art examples illustrate how mathematical and computer modeling can contribute to the understanding of mechanisms and systems in the neurosciences. Each chapter also includes suggestions of possible refinements for future modeling in this rapidly changing and expanding field.

This book will benefit and inspire the advanced modeler, and will give the beginner sufficient confidence to model a wide selection of neuronal systems at the molecular, cellular, and network levels.

Reviews

"The more we learn about the brain, the more we are convinced that understanding its incomputability will be a key to unlocking its functions. Neuroscience is turning a corner, and this book is helping to usher in a new era. The Second Edition is essential reading for those interested in contemporary approaches to neural modeling."

-Paul Bach-y-Rita, M.D., Professor of Rehabilitation Medicine and Biomedical Engineering, University of Wisconsin, USA

"The brain is an integrative organ of adjustment from gene networks to neuronal networks. The vast complexity involved requires mathematical modeling. The Second Edition advances such an integrative approach, and is undoubtedly a mathematical tour de force. This book will radically change our perception of theoretical and computational neuroscience."

-Roman M. Borisyuk, D.Sc., Professor of Computational Neuroscience, Centre for Theoretical and Computational Neuroscience, University of Plymouth, UK

"Physiologically realistic and integrative models of the brain are the only way forward to lift us clear of the jungle of detail about the brain. The essence of brain function dynamics that they embody will serve as the fundamental fulcrum around which new details (parameters) can be added and tested across scale. This book exemplifies a realistic way forward for an explicit 'integrative neuroscience'."

-Evian Gordon, Ph.D., CEO, The Brain Resource Company, and Scientific Chair (and Founding Director), The Brain Dynamic Centre, Westmead Hospital, Australia

Contents

PREFACE

CONTRIBUTORS

FOREWORD

INTRODUCTION TO MODELING IN THE NEUROSCIENCES

George N. Reeke

PATTERNS OF GENETIC INTERACTIONS: ANALYSIS OF MRNA LEVELS FROM CDNA MICROARRAYS

Larry S. Liebovitch, Lina A. Shehadeh, andd Viktor K. Jirsa

CALCIUM SIGNALING IN DENDRITIC SPINES

William R. Holmes

PHYSIOLOGICAL AND STATISTICAL APPROACHES TO MODELING OF SYNAPTIC RESPONSES

Parag G. Patil, Mike West, Howard V. Wheal, and Dennis A. Turner

NATURAL VARIABILITY IN THE GEOMETRY OF DENDRITIC BRANCHING PATTERNS

Jaap van Pelt and Harry B.M. Uylings

MULTICYLINDER MODELS FOR SYNAPTIC AND GAP-JUNCTIONAL INTEGRATION

Jonathan D. Evans

VOLTAGE TRANSIENTS IN BRANCHING MULTIPOLAR NEURONS WITH TAPERING DENDRITES AND SODIUM CHANNELS

Lloyd L. Glenn and Jeffrey R. Knisley

ANALYTICAL SOLUTIONS OF THE FRANKENHAEUSER-HUXLEY EQUATIONS MODIFIED FOR DENDRITIC BACKPROPAGATION OF A SINGLE SODIUM SPIKE

Roman R. Poznanski

INVERSE PROBLEMS FOR SOME CABLE MODELS OF DENDRITES

Jonathan Bell

EQUIVALENT CABLES-ANALYSIS AND CONSTRUCTION

Kenneth A. Lindsay, Jay R. Rosenberg, and Gayle Tucker

THE REPRESENTATION OF THREE-DIMENSIONAL DENDRITIC STRUCTURE BY A ONE-DIMENSIONAL MODEL-THE CONVENTIONAL CABLE EQUATION AS THE FIRST MEMBER OF A HIERARCHY OF EQUATIONS

Kenneth A. Lindsay, Jay R. Rosenberg, and Gayle Tucker

SIMULATION ANALYSES OF RETINAL CELL RESPONSES

Yoshimi Kamiyama, Akito Ishihara, Toshihiro Aoyama, and Shiro Usui

MODELING INTRACELLULAR CALCIUM: DIFFUSION, DYNAMICS, AND DOMAINS

Gregory D. Smith

EPHAPTIC INTERACTIONS BETWEEN NEURONS

Robert Costalat and Bruno Delord

CORTICAL PYRAMIDAL CELLS

Roger D. Orpwood

SEMI-QUANTITATIVE THEORY OF BISTABLE DENDRITES WITH POTENTIAL-DEPENDENT FACILITATION OF INWARD CURRENT

Aron Gutman, Armantas Baginskas, Jorn Hounsgaard, Natasha Svirskiene, and Gytis Svirskis

BIFURCATION ANALYSIS OF THE HODGKIN-HUXLEY EQUATIONS

Shunsuke Sato, Hidekazu Fukai, Taishin Nomura, and Shinji Doi

HIGHLY EFFICIENT PROPAGATION OF RANDOM IMPULSE TRAINS ACROSS UNMYELINATED AXONAL BRANCH POINTS: MODIFICATIONS BY PERIAXONAL K+ ACCUMULATION AND SODIUM CHANNEL KINETICS

Mel D. Goldfinger

DENDRITIC INTEGRATION IN A TWO-NEURON RECURRENT EXCITATORY NETWORK MODEL

Roman R. Poznanski

SPIKE-TRAIN ANALYSIS FOR NEURAL SYSTEMS

David M. Halliday

THE POETICS OF TREMOR

G.P. Moore and Helen M. Bronte-Stewart

PRINCIPLES AND METHODS IN THE ANALYSIS OF BRAIN NETWORKS

Olaf Sporns

THE DARWIN BRAIN-BASED AUTOMATA: SYNTHETIC NEURAL MODELS AND REAL-WORLD DEVICES

Jeffrey L. Krichmar and George N. Reeke

TOWARD NEURAL ROBOTICS: FROM SYNTHETIC MODELS TO NEUROMIMETIC IMPLEMENTATIONS

Olaf Sporns

BIBLIOGRAPHY

INDEX

Name: Modeling in the Neurosciences: From Biological Systems to Neuromimetic Robotics (Hardback) – CRC Press 
Description: Edited by G. N. Reeke, R.R. Poznanski, K. A. Lindsay, J.R. Rosenberg, O. SpornsContributors: Dennis A. Turner, Jonathan Bell, Jaap van Pelt, Gilbert A. Chauvet, Robert Costalat, Jorn Hounsgaard, Jonathan D. Evans, Loyd L. Glenn, Mel D. Goldfinger, David Halliday, William R. Holmes, Yoshimi Kamiyama, George P. Moore, Roger Orpwood, Shunsuke Sato, Lina A. Shehadeh, Gregory D. Smith, Larry S. Liebovitch, Mike West, Harry B.M. Uylings, Parag G. Patil, Toshihiro Aoyama, Armuntas Baginskas, Helen Bronte-Stewart, Bruno Delord, Shinji Doi, Hidekazu Fukai, Aron M. Gutman, Akito Ishihara, Viktor K. Jirsa, Jeffrey R. Knisley, Taishin Nomura, Natasha Svirskiene, Gytis Svirskis, Gayle Tucker, Shiro Usui, Howard V. Wheal, Jeffrey L. Krichmar. Computational models of neural networks have proven insufficient to accurately model brain function, mainly as a result of simplifications that ignore the physical reality of neuronal structure in favor of mathematically tractable algorithms and rules...
Categories: Neuroscience, Biomedical Engineering, Mathematical Biology