Mathematics for Neuroscientists

Produk Detail:
  • Author : Fabrizio Gabbiani
  • Publisher : Academic Press
  • Pages : 498 pages
  • ISBN : 9780080890494
  • Rating : /5 from reviews
CLICK HERE TO GET THIS BOOK >>>Mathematics for Neuroscientists

Download or Read online Mathematics for Neuroscientists full in PDF, ePub and kindle. this book written by Fabrizio Gabbiani and published by Academic Press which was released on 16 September 2010 with total page 498 pages. We cannot guarantee that Mathematics for Neuroscientists book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. Virtually all scientific problems in neuroscience require mathematical analysis, and all neuroscientists are increasingly required to have a significant understanding of mathematical methods. There is currently no comprehensive, integrated introductory book on the use of mathematics in neuroscience; existing books either concentrate solely on theoretical modeling or discuss mathematical concepts for the treatment of very specific problems. This book fills this need by systematically introducing mathematical and computational tools in precisely the contexts that first established their importance for neuroscience. All mathematical concepts will be introduced from the simple to complex using the most widely used computing environment, Matlab. This book will provide a grounded introduction to the fundamental concepts of mathematics, neuroscience and their combined use, thus providing the reader with a springboard to cutting-edge research topics and fostering a tighter integration of mathematics and neuroscience for future generations of students. A very didactic and systematic introduction to mathematical concepts of importance for the analysis of data and the formulation of concepts based on experimental data in neuroscience Provides introductions to linear algebra, ordinary and partial differential equations, Fourier transforms, probabilities and stochastic processes Introduces numerical methods used to implement algorithms related to each mathematical concept Illustrates numerical methods by applying them to specific topics in neuroscience, including Hodgkin-Huxley equations, probabilities to describe stochastic release, stochastic processes to describe noise in neurons, Fourier transforms to describe the receptive fields of visual neurons Allows the mathematical novice to analyze their results in more sophisticated ways, and consider them in a broader theoretical framework

Mathematics for Neuroscientists

Mathematics for Neuroscientists
  • Author : Fabrizio Gabbiani,Steven James Cox
  • Publisher : Academic Press
  • Release : 16 September 2010
GET THIS BOOK Mathematics for Neuroscientists

Virtually all scientific problems in neuroscience require mathematical analysis, and all neuroscientists are increasingly required to have a significant understanding of mathematical methods. There is currently no comprehensive, integrated introductory book on the use of mathematics in neuroscience; existing books either concentrate solely on theoretical modeling or discuss mathematical concepts for the treatment of very specific problems. This book fills this need by systematically introducing mathematical and computational tools in precisely the contexts that first established their importance for neuroscience.

Mathematics for Neuroscientists

Mathematics for Neuroscientists
  • Author : Fabrizio Gabbiani,Steven James Cox
  • Publisher : Academic Press
  • Release : 23 February 2017
GET THIS BOOK Mathematics for Neuroscientists

Mathematics for Neuroscientists, Second Edition, presents a comprehensive introduction to mathematical and computational methods used in neuroscience to describe and model neural components of the brain from ion channels to single neurons, neural networks and their relation to behavior. The book contains more than 200 figures generated using Matlab code available to the student and scholar. Mathematical concepts are introduced hand in hand with neuroscience, emphasizing the connection between experimental results and theory. Fully revised material and corrected text Additional chapters

Mathematical Foundations of Neuroscience

Mathematical Foundations of Neuroscience
  • Author : G. Bard Ermentrout,David H. Terman
  • Publisher : Springer Science & Business Media
  • Release : 01 July 2010
GET THIS BOOK Mathematical Foundations of Neuroscience

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

Neuroscience

Neuroscience
  • Author : Alwyn Scott
  • Publisher : Springer Science & Business Media
  • Release : 14 December 2007
GET THIS BOOK Neuroscience

This book will be of interest to anyone who wishes to know what role mathematics can play in attempting to comprehend the dynamics of the human brain. It also aims to serve as a general introduction to neuromathematics. The book gives the reader a qualitative understanding and working knowledge of useful mathematical applications to the field of neuroscience. The book is readable by those who have little knowledge of mathematics for neuroscience but are committed to begin acquiring such knowledge.

Mathematical Neuroscience

Mathematical Neuroscience
  • Author : Stanislaw Brzychczy,Roman R. Poznanski
  • Publisher : Academic Press
  • Release : 16 August 2013
GET THIS BOOK Mathematical Neuroscience

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

From Computer to Brain

From Computer to Brain
  • Author : William W. Lytton
  • Publisher : Springer Science & Business Media
  • Release : 08 May 2007
GET THIS BOOK From Computer to Brain

Biology undergraduates, medical students and life-science graduate students often have limited mathematical skills. Similarly, physics, math and engineering students have little patience for the detailed facts that make up much of biological knowledge. Teaching computational neuroscience as an integrated discipline requires that both groups be brought forward onto common ground. This book does this by making ancillary material available in an appendix and providing basic explanations without becoming bogged down in unnecessary details. The book will be suitable for undergraduates

Mathematical Foundations of Neuroscience

Mathematical Foundations of Neuroscience
  • Author : G. Bard Ermentrout,David H. Terman
  • Publisher : Unknown
  • Release : 24 September 2021
GET THIS BOOK Mathematical Foundations of Neuroscience

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

Dynamical Systems in Neuroscience

Dynamical Systems in Neuroscience
  • Author : Eugene M. Izhikevich
  • Publisher : MIT Press
  • Release : 24 September 2021
GET THIS BOOK Dynamical Systems in Neuroscience

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

Mathematical and Theoretical Neuroscience

Mathematical and Theoretical Neuroscience
  • Author : Giovanni Naldi,Thierry Nieus
  • Publisher : Springer
  • Release : 21 March 2018
GET THIS BOOK Mathematical and Theoretical Neuroscience

This volume gathers contributions from theoretical, experimental and computational researchers who are working on various topics in theoretical/computational/mathematical neuroscience. The focus is on mathematical modeling, analytical and numerical topics, and statistical analysis in neuroscience with applications. The following subjects are considered: mathematical modelling in Neuroscience, analytical and numerical topics; statistical analysis in Neuroscience; Neural Networks; Theoretical Neuroscience. The book is addressed to researchers involved in mathematical models applied to neuroscience.

Neuroscience of Mathematical Cognitive Development

Neuroscience of Mathematical Cognitive Development
  • Author : Rhonda Douglas Brown
  • Publisher : Springer
  • Release : 13 April 2018
GET THIS BOOK Neuroscience of Mathematical Cognitive Development

​This book examines the neuroscience of mathematical cognitive development from infancy into emerging adulthood, addressing both biological and environmental influences on brain development and plasticity. It begins by presenting major theoretical frameworks for designing and interpreting neuroscience studies of mathematical cognitive development, including developmental evolutionary theory, developmental systems approaches, and the triple-code model of numerical processing. The book includes chapters that discuss findings from studies using neuroscience research methods to examine numerical and visuospatial cognition, calculation, and mathematical difficulties and

Mathematics for Neuroscientists

Mathematics for Neuroscientists
  • Author : Fabrizio Gabbiani,Steven James Cox
  • Publisher : Academic Press
  • Release : 21 March 2017
GET THIS BOOK Mathematics for Neuroscientists

Mathematics for Neuroscientists, Second Edition, presents a comprehensive introduction to mathematical and computational methods used in neuroscience to describe and model neural components of the brain from ion channels to single neurons, neural networks and their relation to behavior. The book contains more than 200 figures generated using Matlab code available to the student and scholar. Mathematical concepts are introduced hand in hand with neuroscience, emphasizing the connection between experimental results and theory. Fully revised material and corrected text Additional chapters

Nonlinear Dynamics in Computational Neuroscience

Nonlinear Dynamics in Computational Neuroscience
  • Author : Fernando Corinto,Alessandro Torcini
  • Publisher : Springer
  • Release : 22 July 2018
GET THIS BOOK Nonlinear Dynamics in Computational Neuroscience

This book provides an essential overview of computational neuroscience. It addresses a broad range of aspects, from physiology to nonlinear dynamical approaches to understanding neural computation, and from the simulation of brain circuits to the development of engineering devices and platforms for neuromorphic computation. Written by leading experts in such diverse fields as neuroscience, physics, psychology, neural engineering, cognitive science and applied mathematics, the book reflects the remarkable advances that have been made in the field of computational neuroscience, an

Computational Neuroscience

Computational Neuroscience
  • Author : Wanpracha Chaovalitwongse,Panos M. Pardalos,Petros Xanthopoulos
  • Publisher : Springer Science & Business Media
  • Release : 03 July 2010
GET THIS BOOK Computational Neuroscience

This volume includes contributions from diverse disciplines including electrical engineering, biomedical engineering, industrial engineering, and medicine, bridging a vital gap between the mathematical sciences and neuroscience research. Covering a wide range of research topics, this volume demonstrates how various methods from data mining, signal processing, optimization and cutting-edge medical techniques can be used to tackle the most challenging problems in modern neuroscience.

Tutorials in Mathematical Biosciences I

Tutorials in Mathematical Biosciences I
  • Author : Alla Borisyuk,G. Bard Ermentrout,Avner Friedman,David H. Terman
  • Publisher : Springer Science & Business Media
  • Release : 18 February 2005
GET THIS BOOK Tutorials in Mathematical Biosciences I

This volume introduces some basic theories on computational neuroscience. Chapter 1 is a brief introduction to neurons, tailored to the subsequent chapters. Chapter 2 is a self-contained introduction to dynamical systems and bifurcation theory, oriented towards neuronal dynamics. The theory is illustrated with a model of Parkinson's disease. Chapter 3 reviews the theory of coupled neural oscillators observed throughout the nervous systems at all levels; it describes how oscillations arise, what pattern they take, and how they depend on excitory or inhibitory synaptic

Signal Processing for Neuroscientists

Signal Processing for Neuroscientists
  • Author : Wim van Drongelen
  • Publisher : Elsevier
  • Release : 18 December 2006
GET THIS BOOK Signal Processing for Neuroscientists

Signal Processing for Neuroscientists introduces analysis techniques primarily aimed at neuroscientists and biomedical engineering students with a reasonable but modest background in mathematics, physics, and computer programming. The focus of this text is on what can be considered the ‘golden trio’ in the signal processing field: averaging, Fourier analysis, and filtering. Techniques such as convolution, correlation, coherence, and wavelet analysis are considered in the context of time and frequency domain analysis. The whole spectrum of signal analysis is covered, ranging