The Resource Mathematics for neuroscientists, Fabrizio Gabbiani, Steven J. Cox
Mathematics for neuroscientists, Fabrizio Gabbiani, Steven J. Cox
Resource Information
The item Mathematics for neuroscientists, Fabrizio Gabbiani, Steven J. Cox represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of MissouriSt. Louis Libraries.This item is available to borrow from 1 library branch.
Resource Information
The item Mathematics for neuroscientists, Fabrizio Gabbiani, Steven J. Cox represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of MissouriSt. Louis Libraries.
This item is available to borrow from 1 library branch.
 Summary
 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 cuttingedge 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. 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. 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 HodgkinHuxley 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, and consider them in a broader theoretical framework
 Language
 eng
 Edition
 1st ed.
 Extent
 1 online resource (xi, 486 pages)
 Contents

 Passive isopotential cell
 Differential equations
 Active isopotential cell
 Quasiactive 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
 Reversecorrelation and spike train decoding
 Signal detection theory
 Relating neuronal responses and psychophysics
 Population codes
 Neuronal networks
 Solutions to selected exercises
 Isbn
 9780128019061
 Label
 Mathematics for neuroscientists
 Title
 Mathematics for neuroscientists
 Statement of responsibility
 Fabrizio Gabbiani, Steven J. Cox
 Subject

 Computational Biology  methods
 Computational biology
 Computational biology
 Computational neuroscience
 Computational neuroscience
 Electronic book
 MEDICAL  Neuroscience
 Mathematical Concepts
 Models, Neurological
 Nerve Net
 Neurons  physiology
 Neurosciences
 Neurosciences
 Neurosciences  Informatique
 Neurosciences  Modèles mathématiques
 Neurosciences  methods
 PSYCHOLOGY  Neuropsychology
 Synaptic Transmission
 Language
 eng
 Summary
 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 cuttingedge 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. 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. 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 HodgkinHuxley 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, and consider them in a broader theoretical framework
 Cataloging source
 OPELS
 http://library.link/vocab/creatorName
 Gabbiani, Fabrizio
 Dewey number
 612.8
 Illustrations
 illustrations
 Index
 index present
 LC call number
 QP356
 LC item number
 .G22 2010
 Literary form
 non fiction
 Nature of contents

 dictionaries
 bibliography
 NLM call number

 2010 J281
 QU 26.5
 http://library.link/vocab/relatedWorkOrContributorDate
 1960
 http://library.link/vocab/relatedWorkOrContributorName
 Cox, Steven J.
 Series statement
 Elsevier science & technology books
 http://library.link/vocab/subjectName

 Computational neuroscience
 Computational biology
 Neurosciences
 Computational Biology
 Mathematical Concepts
 Models, Neurological
 Nerve Net
 Neurons
 Neurosciences
 Synaptic Transmission
 MEDICAL
 PSYCHOLOGY
 Computational biology
 Computational neuroscience
 Neurosciences
 Neurosciences
 Neurosciences
 Label
 Mathematics for neuroscientists, Fabrizio Gabbiani, Steven J. Cox
 Bibliography note
 Includes bibliographical references (pages 473482) and index
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Color
 multicolored
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent
 Contents
 Passive isopotential cell  Differential equations  Active isopotential cell  Quasiactive 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  Reversecorrelation and spike train decoding  Signal detection theory  Relating neuronal responses and psychophysics  Population codes  Neuronal networks  Solutions to selected exercises
 Control code
 668196264
 Dimensions
 unknown
 Edition
 1st ed.
 Extent
 1 online resource (xi, 486 pages)
 Form of item
 online
 Isbn
 9780128019061
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 Other physical details
 illustrations (some color).
 http://library.link/vocab/ext/overdrive/overdriveId
 167007:167242
 Specific material designation
 remote
 System control number
 (OCoLC)668196264
 Label
 Mathematics for neuroscientists, Fabrizio Gabbiani, Steven J. Cox
 Bibliography note
 Includes bibliographical references (pages 473482) and index
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Color
 multicolored
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent
 Contents
 Passive isopotential cell  Differential equations  Active isopotential cell  Quasiactive 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  Reversecorrelation and spike train decoding  Signal detection theory  Relating neuronal responses and psychophysics  Population codes  Neuronal networks  Solutions to selected exercises
 Control code
 668196264
 Dimensions
 unknown
 Edition
 1st ed.
 Extent
 1 online resource (xi, 486 pages)
 Form of item
 online
 Isbn
 9780128019061
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 Other physical details
 illustrations (some color).
 http://library.link/vocab/ext/overdrive/overdriveId
 167007:167242
 Specific material designation
 remote
 System control number
 (OCoLC)668196264
Subject
 Computational Biology  methods
 Computational biology
 Computational biology
 Computational neuroscience
 Computational neuroscience
 Electronic book
 MEDICAL  Neuroscience
 Mathematical Concepts
 Models, Neurological
 Nerve Net
 Neurons  physiology
 Neurosciences
 Neurosciences
 Neurosciences  Informatique
 Neurosciences  Modèles mathématiques
 Neurosciences  methods
 PSYCHOLOGY  Neuropsychology
 Synaptic Transmission
Genre
Member of
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<div class="citation" vocab="http://schema.org/"><i class="fa faexternallinksquare fafw"></i> Data from <span resource="http://link.umsl.edu/portal/MathematicsforneuroscientistsFabrizio/xfgk6GHKPbE/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.umsl.edu/portal/MathematicsforneuroscientistsFabrizio/xfgk6GHKPbE/">Mathematics for neuroscientists, Fabrizio Gabbiani, Steven J. Cox</a></span>  <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.umsl.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.umsl.edu/">University of MissouriSt. Louis Libraries</a></span></span></span></span></div>