Methods And Models In Neurophysics
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Methods And Models In Neurophysics
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Author : Carson Chow
language : en
Publisher: Elsevier
Release Date : 2005
Methods And Models In Neurophysics written by Carson Chow and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Language Arts & Disciplines categories.
1. E. Marder, Experimenting with theory -- 2. A. Borysuk and J. Rinzel, Understanding neuronal dynamics by geometrical dissection of minimal models -- 3. D. Terman, Geometry singular perturbation analysis of neuronal dynamics -- 4. G. Mato, Theory of neural synchrony -- 5. M. Shelley, Some useful numerical techniques for simulating integrate-and-fire networks -- 6. D. Golomb, Propagation of pulses in cortical networks: the single-spike approximation -- 7. M. Tsodyks, Activity-dependent transmission in neocortical synapses -- 8. H. Sompolinsky and J. White, Theory of large recurrent networks: from spikes to behavior -- 9. C. van Vreeswijk, Irregular activity in large networks of neurons -- 10. N. Brunel, Network models of memory -- 11. P. Bressloff, Pattern formation in visual cortex -- 12. F. Wolf, Symmetry breaking and pattern selection in visual cortical development -- 13. A. Treves and Y. Roudi, On the evolution of the brain -- 14. E. Brown, Theory of point processes for neural syst ...
Methods And Models In Neurophysics
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Author :
language : en
Publisher: Elsevier
Release Date : 2004-12-11
Methods And Models In Neurophysics written by and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-12-11 with Science categories.
Neuroscience is an interdisciplinary field that strives to understand the functioning of neural systems at levels ranging from biomolecules and cells to behaviour and higher brain functions (perception, memory, cognition). Neurophysics has flourished over the past three decades, becoming an indelible part of neuroscience, and has arguably entered its maturity. It encompasses a vast array of approaches stemming from theoretical physics, computer science, and applied mathematics. This book provides a detailed review of this field from basic concepts to its most recent development.
Mathematical Neuroscience
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Author : Stanislaw Brzychczy
language : en
Publisher: Academic Press
Release Date : 2013-08-16
Mathematical Neuroscience written by Stanislaw Brzychczy and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-08-16 with Mathematics categories.
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. - 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
Advanced Data Analysis In Neuroscience
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Author : Daniel Durstewitz
language : en
Publisher: Springer
Release Date : 2017-09-15
Advanced Data Analysis In Neuroscience written by Daniel Durstewitz and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-15 with Medical categories.
This book is intended for use in advanced graduate courses in statistics / machine learning, as well as for all experimental neuroscientists seeking to understand statistical methods at a deeper level, and theoretical neuroscientists with a limited background in statistics. It reviews almost all areas of applied statistics, from basic statistical estimation and test theory, linear and nonlinear approaches for regression and classification, to model selection and methods for dimensionality reduction, density estimation and unsupervised clustering. Its focus, however, is linear and nonlinear time series analysis from a dynamical systems perspective, based on which it aims to convey an understanding also of the dynamical mechanisms that could have generated observed time series. Further, it integrates computational modeling of behavioral and neural dynamics with statistical estimation and hypothesis testing. This way computational models in neuroscience are not only explanatory frameworks, but become powerful, quantitative data-analytical tools in themselves that enable researchers to look beyond the data surface and unravel underlying mechanisms. Interactive examples of most methods are provided through a package of MatLab routines, encouraging a playful approach to the subject, and providing readers with a better feel for the practical aspects of the methods covered. "Computational neuroscience is essential for integrating and providing a basis for understanding the myriads of remarkable laboratory data on nervous system functions. Daniel Durstewitz has excellently covered the breadth of computational neuroscience from statistical interpretations of data to biophysically based modeling of the neurobiological sources of those data. His presentation is clear, pedagogically sound, and readily useable by experts and beginners alike. It is a pleasure to recommend this very well crafted discussion to experimental neuroscientists as well as mathematically well versed Physicists. The book acts as a window to the issues, to the questions, and to the tools for finding the answers to interesting inquiries about brains and how they function." Henry D. I. Abarbanel Physics and Scripps Institution of Oceanography, University of California, San Diego “This book delivers a clear and thorough introduction to sophisticated analysis approaches useful in computational neuroscience. The models described and the examples provided will help readers develop critical intuitions into what the methods reveal about data. The overall approach of the book reflects the extensive experience Prof. Durstewitz has developed as a leading practitioner of computational neuroscience. “ Bruno B. Averbeck
Statistical Signal Processing For Neuroscience And Neurotechnology
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Author : Karim G. Oweiss
language : en
Publisher: Academic Press
Release Date : 2010-09-22
Statistical Signal Processing For Neuroscience And Neurotechnology written by Karim G. Oweiss and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-09-22 with Technology & Engineering categories.
This is a uniquely comprehensive reference that summarizes the state of the art of signal processing theory and techniques for solving emerging problems in neuroscience, and which clearly presents new theory, algorithms, software and hardware tools that are specifically tailored to the nature of the neurobiological environment. It gives a broad overview of the basic principles, theories and methods in statistical signal processing for basic and applied neuroscience problems.Written by experts in the field, the book is an ideal reference for researchers working in the field of neural engineering, neural interface, computational neuroscience, neuroinformatics, neuropsychology and neural physiology. By giving a broad overview of the basic principles, theories and methods, it is also an ideal introduction to statistical signal processing in neuroscience. - A comprehensive overview of the specific problems in neuroscience that require application of existing and development of new theory, techniques, and technology by the signal processing community - Contains state-of-the-art signal processing, information theory, and machine learning algorithms and techniques for neuroscience research - Presents quantitative and information-driven science that has been, or can be, applied to basic and translational neuroscience problems
Guide To Research Techniques In Neuroscience
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Author : Matt Carter
language : en
Publisher: Academic Press
Release Date : 2022-03-26
Guide To Research Techniques In Neuroscience written by Matt Carter and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-26 with Medical categories.
Modern neuroscience research is inherently multidisciplinary, with a wide variety of cutting edge new techniques to explore multiple levels of investigation. This Third Edition of Guide to Research Techniques in Neuroscience provides a comprehensive overview of classical and cutting edge methods including their utility, limitations, and how data are presented in the literature. This book can be used as an introduction to neuroscience techniques for anyone new to the field or as a reference for any neuroscientist while reading papers or attending talks. - Nearly 200 updated full-color illustrations to clearly convey the theory and practice of neuroscience methods - Expands on techniques from previous editions and covers many new techniques including in vivo calcium imaging, fiber photometry, RNA-Seq, brain spheroids, CRISPR-Cas9 genome editing, and more - Clear, straightforward explanations of each technique for anyone new to the field - A broad scope of methods, from noninvasive brain imaging in human subjects, to electrophysiology in animal models, to recombinant DNA technology in test tubes, to transfection of neurons in cell culture - Detailed recommendations on where to find protocols and other resources for specific techniques - "Walk-through" boxes that guide readers through experiments step-by-step
Mathematical Foundations Of Neuroscience
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Author : G. Bard Ermentrout
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-07-01
Mathematical Foundations Of Neuroscience written by G. Bard Ermentrout and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-07-01 with Mathematics categories.
This book applies methods from nonlinear dynamics to problems in neuroscience. It uses modern mathematical approaches to understand patterns of neuronal activity seen in experiments and models of neuronal behavior. The intended audience is researchers interested in applying mathematics to important problems in neuroscience, and neuroscientists who would like to understand how to create models, as well as the mathematical and computational methods for analyzing them. The authors take a very broad approach and use many different methods to solve and understand complex models of neurons and circuits. They explain and combine numerical, analytical, dynamical systems and perturbation methods to produce a modern approach to the types of model equations that arise in neuroscience. There are extensive chapters on the role of noise, multiple time scales and spatial interactions in generating complex activity patterns found in experiments. The early chapters require little more than basic calculus and some elementary differential equations and can form the core of a computational neuroscience course. Later chapters can be used as a basis for a graduate class and as a source for current research in mathematical neuroscience. The book contains a large number of illustrations, chapter summaries and hundreds of exercises which are motivated by issues that arise in biology, and involve both computation and analysis. Bard Ermentrout is Professor of Computational Biology and Professor of Mathematics at the University of Pittsburgh. David Terman is Professor of Mathematics at the Ohio State University.
Theoretical Neuroscience
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Author : Peter Dayan
language : en
Publisher: MIT Press
Release Date : 2005-08-12
Theoretical Neuroscience written by Peter Dayan and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-08-12 with Medical categories.
Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory. The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book's Web site.
Computational Neuroscience For Advancing Artificial Intelligence Models Methods And Applications
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Author : Alonso, Eduardo
language : en
Publisher: IGI Global
Release Date : 2010-11-30
Computational Neuroscience For Advancing Artificial Intelligence Models Methods And Applications written by Alonso, Eduardo and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-11-30 with Computers categories.
"This book argues that computational models in behavioral neuroscience must be taken with caution, and advocates for the study of mathematical models of existing theories as complementary to neuro-psychological models and computational models"--
Biophysics Of Computation
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Author : Christof Koch
language : en
Publisher: Oxford University Press
Release Date : 2004-10-28
Biophysics Of Computation written by Christof Koch and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-10-28 with Medical categories.
Neural network research often builds on the fiction that neurons are simple linear threshold units, completely neglecting the highly dynamic and complex nature of synapses, dendrites, and voltage-dependent ionic currents. Biophysics of Computation: Information Processing in Single Neurons challenges this notion, using richly detailed experimental and theoretical findings from cellular biophysics to explain the repertoire of computational functions available to single neurons. The author shows how individual nerve cells can multiply, integrate, or delay synaptic inputs and how information can be encoded in the voltage across the membrane, in the intracellular calcium concentration, or in the timing of individual spikes.Key topics covered include the linear cable equation; cable theory as applied to passive dendritic trees and dendritic spines; chemical and electrical synapses and how to treat them from a computational point of view; nonlinear interactions of synaptic input in passive and active dendritic trees; the Hodgkin-Huxley model of action potential generation and propagation; phase space analysis; linking stochastic ionic channels to membrane-dependent currents; calcium and potassium currents and their role in information processing; the role of diffusion, buffering and binding of calcium, and other messenger systems in information processing and storage; short- and long-term models of synaptic plasticity; simplified models of single cells; stochastic aspects of neuronal firing; the nature of the neuronal code; and unconventional models of sub-cellular computation.Biophysics of Computation: Information Processing in Single Neurons serves as an ideal text for advanced undergraduate and graduate courses in cellular biophysics, computational neuroscience, and neural networks, and will appeal to students and professionals in neuroscience, electrical and computer engineering, and physics.