Mathematics for neuroscientists

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, probabilit...

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Bibliographic Details
Main Author: Gabbiani, Fabrizio
Other Authors: Cox, Steven J.
Format: eBook
Language:English
Published: Amsterdam Elsevier Academic Press 2010, 2010
Edition:1st ed
Series:Elsevier science & technology books
Subjects:
Online Access:
Collection: Elsevier ScienceDirect eBooks - Collection details see MPG.ReNa
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100 1 |a Gabbiani, Fabrizio 
245 0 0 |a Mathematics for neuroscientists  |h [electronic resource]  |h Elektronische Ressource  |c Fabrizio Gabbiani, Steven J. Cox 
250 |a 1st ed 
260 |a Amsterdam  |b Elsevier Academic Press  |c 2010, 2010 
300 |a online resource (xi, 486 p.)  |b ill. (some col.) 
505 0 |a Includes bibliographical references (p. 473-482) and index 
505 0 |a Passive isopotential cell -- Differential equations -- Active isopotential cell -- Quasi-active isopotential cell -- Passive cable -- Fourier series and transforms -- Passive dendritic tree -- Active dendritic tree -- Reduced single neuron models -- Probability and random variables -- Synaptic transmission and quantal release -- Neuronal calcium signaling -- Singular value decomposition and applications -- Quantification of spike train variability -- Stochastic processes -- Membrane noise -- Power and cross spectra -- Natural light signals and phototransduction -- Firing rate codes and early vision -- Models of simple and complex cells -- Stochastic estimation theory -- Reverse-correlation and spike train decoding -- Signal detection theory -- Relating neuronal responses and psychophysics -- Population codes -- Neuronal networks -- Solutions to selected exercises 
653 |a Computational Biology / methods 
653 |a Computational biology 
653 |a Neurosciences 
653 |a Computational biology / fast / (OCoLC)fst00871990 
653 |a PSYCHOLOGY / Neuropsychology / bisacsh 
653 |a Computational neuroscience 
653 |a Neurosciences / fast / (OCoLC)fst01036509 
653 |a Computational neuroscience / fast / (OCoLC)fst00872004 
653 |a MEDICAL / Neuroscience / bisacsh 
700 1 |a Cox, Steven J. 
041 0 7 |a eng  |2 ISO 639-2 
989 |b ESD  |a Elsevier ScienceDirect eBooks 
490 0 |a Elsevier science & technology books 
856 4 0 |u http://www.sciencedirect.com/science/book/9780123748829  |x Verlag  |3 Volltext 
082 0 |a 612.8 
520 |a 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 Provides implementation examples in MATLAB code, also included for download on the accompanying support website (which will be updated with additional code and in line with major MATLAB releases) Allows the mathematical novice to analyze their results in more sophisticated ways,  
520 |a This book provides 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. The book alternates between mathematical chapters, introducing important concepts and numerical methods, and neurobiological chapters, applying these concepts and methods to specific topics. It covers topics ranging from classical cellular biophysics and proceeding up to systems level neuroscience. Starting at an introductory mathematical level, presuming no more than calculus through elementary differential equations, the level will build up as increasingly complex techniques are introduced and combined with earlier ones. Each chapter includes a comprehensive series of exercises with solutions, taken from the set developed by the authors in their course lectures.  
520 |a and consider them in a broader theoretical framework 
520 |a MATLAB code is included for each computational figure, to allow the reader to reproduce them. Biographical notes referring the reader to more specialized literature and additional mathematical material that may be needed either to deepen the reader's understanding or to introduce basic concepts for less mathematically inclined readers completes each chapter.