Quantitative Neuroscience
Quantitative Neuroscience

Author: Panos M. Pardalos

Publisher: Springer Science & Business Media

Published: 2013-12-01

Total Pages: 263

ISBN-13: 1461302250

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Advances in the field of signal processing, nonlinear dynamics, statistics, and optimization theory, combined with marked improvement in instrumenta tion and development of computers systems, have made it possible to apply the power of mathematics to the task of understanding the human brain. This verita ble revolution already has resulted in widespread availability of high resolution neuroimaging devices in clinical as well as research settings. Breakthroughs in functional imaging are not far behind. Mathematical tech niques developed for the study of complex nonlinear systems and chaos already are being used to explore the complex nonlinear dynamics of human brain phys iology. Global optimization is being applied to data mining expeditions in an effort to find knowledge in the vast amount of information being generated by neuroimaging and neurophysiological investigations. These breakthroughs in the ability to obtain, store and analyze large datasets offer, for the first time, exciting opportunities to explore the mechanisms underlying normal brain func tion as well as the affects of diseases such as epilepsy, sleep disorders, movement disorders, and cognitive disorders that affect millions of people every year. Ap plication of these powerful tools to the study of the human brain requires, by necessity, collaboration among scientists, engineers, neurobiologists and clini cians. Each discipline brings to the table unique knowledge, unique approaches to problem solving, and a unique language.

An Introductory Course in Computational Neuroscience
An Introductory Course in Computational Neuroscience

Author: Paul Miller

Publisher: MIT Press

Published: 2018-10-09

Total Pages: 405

ISBN-13: 0262347563

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A textbook for students with limited background in mathematics and computer coding, emphasizing computer tutorials that guide readers in producing models of neural behavior. This introductory text teaches students to understand, simulate, and analyze the complex behaviors of individual neurons and brain circuits. It is built around computer tutorials that guide students in producing models of neural behavior, with the associated Matlab code freely available online. From these models students learn how individual neurons function and how, when connected, neurons cooperate in a circuit. The book demonstrates through simulated models how oscillations, multistability, post-stimulus rebounds, and chaos can arise within either single neurons or circuits, and it explores their roles in the brain. The book first presents essential background in neuroscience, physics, mathematics, and Matlab, with explanations illustrated by many example problems. Subsequent chapters cover the neuron and spike production; single spike trains and the underlying cognitive processes; conductance-based models; the simulation of synaptic connections; firing-rate models of large-scale circuit operation; dynamical systems and their components; synaptic plasticity; and techniques for analysis of neuron population datasets, including principal components analysis, hidden Markov modeling, and Bayesian decoding. Accessible to undergraduates in life sciences with limited background in mathematics and computer coding, the book can be used in a “flipped” or “inverted” teaching approach, with class time devoted to hands-on work on the computer tutorials. It can also be a resource for graduate students in the life sciences who wish to gain computing skills and a deeper knowledge of neural function and neural circuits.

Quantitative Neuroscience
Quantitative Neuroscience

Author: Panos M. Pardalos

Publisher: Springer Science & Business Media

Published: 2004-01-31

Total Pages: 282

ISBN-13: 9781402077517

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Advances in the field of signal processing, nonlinear dynamics, statistics, and optimization theory, combined with marked improvement in instrumenta tion and development of computers systems, have made it possible to apply the power of mathematics to the task of understanding the human brain. This verita ble revolution already has resulted in widespread availability of high resolution neuroimaging devices in clinical as well as research settings. Breakthroughs in functional imaging are not far behind. Mathematical tech niques developed for the study of complex nonlinear systems and chaos already are being used to explore the complex nonlinear dynamics of human brain phys iology. Global optimization is being applied to data mining expeditions in an effort to find knowledge in the vast amount of information being generated by neuroimaging and neurophysiological investigations. These breakthroughs in the ability to obtain, store and analyze large datasets offer, for the first time, exciting opportunities to explore the mechanisms underlying normal brain func tion as well as the affects of diseases such as epilepsy, sleep disorders, movement disorders, and cognitive disorders that affect millions of people every year. Ap plication of these powerful tools to the study of the human brain requires, by necessity, collaboration among scientists, engineers, neurobiologists and clini cians. Each discipline brings to the table unique knowledge, unique approaches to problem solving, and a unique language.

Dynamical Systems in Neuroscience
Dynamical Systems in Neuroscience

Author: Eugene M. Izhikevich

Publisher: MIT Press

Published: 2010-01-22

Total Pages: 459

ISBN-13: 0262514206

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Explains the relationship of electrophysiology, nonlinear dynamics, and the computational properties of neurons, with each concept presented in terms of both neuroscience and mathematics and illustrated using geometrical intuition. In order to model neuronal behavior or to interpret the results of modeling studies, neuroscientists must call upon methods of nonlinear dynamics. This book offers an introduction to nonlinear dynamical systems theory for researchers and graduate students in neuroscience. It also provides an overview of neuroscience for mathematicians who want to learn the basic facts of electrophysiology. Dynamical Systems in Neuroscience presents a systematic study of the relationship of electrophysiology, nonlinear dynamics, and computational properties of neurons. It emphasizes that information processing in the brain depends not only on the electrophysiological properties of neurons but also on their dynamical properties. The book introduces dynamical systems, starting with one- and two-dimensional Hodgkin-Huxley-type models and continuing to a description of bursting systems. Each chapter proceeds from the simple to the complex, and provides sample problems at the end. The book explains all necessary mathematical concepts using geometrical intuition; it includes many figures and few equations, making it especially suitable for non-mathematicians. Each concept is presented in terms of both neuroscience and mathematics, providing a link between the two disciplines. Nonlinear dynamical systems theory is at the core of computational neuroscience research, but it is not a standard part of the graduate neuroscience curriculum—or taught by math or physics department in a way that is suitable for students of biology. This book offers neuroscience students and researchers a comprehensive account of concepts and methods increasingly used in computational neuroscience. An additional chapter on synchronization, with more advanced material, can be found at the author's website, www.izhikevich.com.

Quantitative Methods in Neuroscience
Quantitative Methods in Neuroscience

Author: Stephen M. Evans

Publisher:

Published: 2004

Total Pages: 362

ISBN-13: 9780198505280

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Stereology is a valuable tool for neuroscientists, allowing them to obtain 3-Dimensional information from 2-Dimensional measurements made on appropriately sampled sections (usually obtained from histological sections or MRI/CT/PET scans). This 3-D information is invaluable in correlatingstructural/functional relationships in the pursuit of far greater understanding of the function of the central nervous system. However, in carrying out such measurements, often based on limited data sets, there is a risk of experimenter bias. An important feature of modern design based stereology isto be aware of potential sources of bias and eliminate them during the data collection. With many of the major neuroscience journals now insisting that quantitative data be presented, there is a greater need than ever for neuroscientists to understand the theory and practice behind quantitativemethods, such as those offered by stereology. Quantitative Methods in Neuroscience is a cookbook of stereological methods written especially for neuroscientists. It provides clear and accessible advice about when and when not to use stereology. Throughout the book, the emphasis is on practical guidance, rather than discussions and formulae.Written by leading scientists in the field of stereology, with a Foreword by D.C. Sterio, the book will be a valuable introduction to these methods for neuroscientists, and all those involved in development of new drug programmes.

20 Years of Computational Neuroscience
20 Years of Computational Neuroscience

Author: James M Bower

Publisher: Springer Science & Business Media

Published: 2013-07-11

Total Pages: 294

ISBN-13: 1461414245

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When funding agencies and policy organizations consider the role of modeling and simulation in modern biology, the question is often posed, what has been accomplished ? This book will be organized around a symposium on the 20 year history of the CNS meetings, to be held as part of CNS 2010 in San Antonio Texas in July 2010. The book, like the symposium is intended to summarize progress made in Computational Neuroscience over the last 20 years while also considering current challenges in the field. As described in the table of contents, the chapter’s authors have been selected to provide wide coverage of the applications of computational techniques to a broad range of questions and model systems in neuroscience. The proposed book will include several features that establish the history of the field. For each article, its author will select an article originally appearing in a CNS conference proceedings from 15 – 20 years ago. These short (less than 6 page) articles will provide illustrations of the state of the field 20 years ago. The new articles will describe what has been learned about the subject in the following 20 years, and pose specific challenges for the next 20 years. The second historical mechanism will be the reproduction of the first 12 years of posters from the CNS meeting. These posters in and of themselves have become famous in the field (they hang in the halls of the NIH in Bethesda Maryland) and were constructed as allegories for the state and development of computational neuroscience. The posters were designed by the book’s editor, who will, for the first time, provide a written description of each poster.

Computational Neuroscience in Epilepsy
Computational Neuroscience in Epilepsy

Author: Ivan Soltesz

Publisher: Academic Press

Published: 2011-09-02

Total Pages: 649

ISBN-13: 0080559530

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Epilepsy is a neurological disorder that affects millions of patients worldwide and arises from the concurrent action of multiple pathophysiological processes. The power of mathematical analysis and computational modeling is increasingly utilized in basic and clinical epilepsy research to better understand the relative importance of the multi-faceted, seizure-related changes taking place in the brain during an epileptic seizure. This groundbreaking book is designed to synthesize the current ideas and future directions of the emerging discipline of computational epilepsy research. Chapters address relevant basic questions (e.g., neuronal gain control) as well as long-standing, critically important clinical challenges (e.g., seizure prediction). Computational Neuroscience in Epilepsy should be of high interest to a wide range of readers, including undergraduate and graduate students, postdoctoral fellows and faculty working in the fields of basic or clinical neuroscience, epilepsy research, computational modeling and bioengineering. - Covers a wide range of topics from molecular to seizure predictions and brain implants to control seizures - Contributors are top experts at the forefront of computational epilepsy research - Chapter contents are highly relevant to both basic and clinical epilepsy researchers