Mathematics for Neuroscientists

Other | September 16, 2010

byFabrizio Gabbiani, Fabrizio Gabbiani, Steven James Cox

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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. All code will be available via a companion website, which will be continuously updated with additional code and updates necessitated by software releases.
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
  • 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

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From the Publisher

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 so...

Dr. Cox is Professor of Computational and Applied Mathematics at Rice University. Affiliated with the Center for Neuroscience, Cognitive Sciences Program, and the Ken Kennedy Institute for Information Technology, he is also Adjunct Professor of Neuroscience at the Baylor College of Medicine. In addition, Dr. Cox has served as Associate...

other books by Fabrizio Gabbiani

Mathematics For Neuroscientists
Mathematics For Neuroscientists

Hardcover|Mar 1 2017

$172.50

Format:OtherDimensions:498 pages, 1 × 1 × 1 inPublished:September 16, 2010Publisher:Academic PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0080890490

ISBN - 13:9780080890494

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Table of Contents

1 Introduction
2 The Passive Isopotential Cell
3 Differential Equations
4 The Active Isopotential Cell
5 The Quasi-Active Isopotential Cell
6 The Passive Cable
7 Fourier Series and Transforms
8 The Passive Dendritic Tree
9 The Active Dendritic Tree
10 Reduced Single Neuron Models
11 Probability and Random Variables
12 Synaptic Transmission and Quantal Release
13 Neuronal Calcium Signaling
14 The Singular Value Decomposition and Applications
15 Quantification of Spike Train Variability
16 Stochastic Processes
17 Membrane Noise
18 Power and Cross Spectra
19 Natural Light Signals and Phototransduction
20 Firing Rate Codes and Early Vision
21 Models of Simple and Complex Cells
22 Stochastic Estimation Theory
23 Reverse-Correlation and Spike Train Decoding
24 Signal Detection Theory
25 Relating Neuronal Responses and Psychophysics
26 Population Codes
27 Neuronal Networks
28 Solutions to Selected Exercises