MATLAB for Neuroscientists

An Introduction to Scientific Computing in MATLAB


  • Pascal Wallisch, New York University, NY, USA
  • Michael Lusignan, The University of Chicago, IL, USA
  • Marc Benayoun, The University of Chicago, IL, USA
  • Tanya Baker, The Salk Institute for Biological Studies, La Jolla, CA, USA
  • Adam Dickey, The University of Chicago, IL, USA
  • Nicholas Hatsopoulos, The University of Chicago, IL, USA

This is the first comprehensive teaching resource and textbook for the teaching of MATLAB in the Neurosciences and in Psychology. MATLAB is unique in that it can be used to learn the entire empirical and experimental process, including stimulus generation, experimental control, data collection, data analysis and modeling. Thus a wide variety of computational problems can be addressed in a single programming environment. The idea is to empower advanced undergraduates and beginning graduate students by allowing them to design and implement their own analytical tools. As students advance in their research careers, they will have achieved the fluency required to understand and adapt more specialized tools as opposed to treating them as "black boxes".

Virtually all computational approaches in the book are covered by using genuine experimental data that are either collected as part of the lab project or were collected in the labs of the authors, providing the casual student with the look and feel of real data. In some cases, published data from classical papers are used to illustrate important concepts, giving students a computational understanding of critically important research.

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Undergraduate and graduate students in systems, cognitive, and behavioral neuroscience, cognitive psychology, and related fields, as well as researchers in these fields who use Matlab.


Book information

  • Published: October 2008
  • ISBN: 978-0-12-374551-4


“The book is clear, cogent, and systematic. It provides much more than the essential nuts-and-bolts—it also leads the reader to learn to think about the empirical enterprise writ large...This book should be given a privileged spot on the bookshelf of every teacher, student, and researcher in the behavioral and cognitive sciences.” — Stephen M. Kosslyn, John Lindsley Professor of Psychology, Dean of Social Science, Harvard University, Cambridge, MA, USA “This is an excellent book that should be on the desk of any neuroscientist or psychologist who wants to analyze and understand his or her own data by using MATLAB...Several books with MATLAB toolboxes exist; I find this one special both for its clarity and its focus on problems related to neuroscience and cognitive psychology.” — Nikos Logothetis, Director, Max Planck Institute for Biological Cybernetics, Tübingen, Germany “MATLAB for Neuroscientists provides a unique and relatively comprehensive introduction to the MATLAB programming language in the context of brain sciences...The book would work well as a supplementary source for an introductory coursein computational analysis and modeling in visual neuroscience, for graduate students or advanced undergraduates.” — Eero P. Simoncelli, Investigator, Howard Hughes Medical Institute; Professor, Neural Science, Mathematics, and Psychology, New York University, New York, USA

Table of Contents

PrefacePart I: FundamentalsIntroductionTutorialPart II: Data Collection with MatlabVisual Search and Pop OutAttentionPsychophysicsSignal Detection Theory Part III: Data Analysis with MatlabFrequency Analysis Part IFrequency Analysis Part II: Non-stationary Signals and SpectrogramsWaveletsConvolutionIntroduction to Phase Plane AnalysisExploring the Fitzhugh-Nagumo ModelNeural Data Analysis: EncodingPrincipal Components AnalysisInformation TheoryNeural Decoding: Discrete variablesNeural Decoding: Continuous variablesFunctional Magnetic ImagingPart IV: Data Modeling with MatlabVoltage-Gated Ion ChannelsModels of a Single NeuronModels of the RetinaSimplified Models of Spiking NeuronsFitzhugh-Nagumo Model: Traveling WavesDecision TheoryMarkov ModelModeling Spike Trains as a Poisson ProcessSynaptic TransmissionNeural Networks: Unsupervised learningNeural Network: Supervised LearningAppendicesAppendix 1: Thinking in MatlabAppendix 2: Linear Algebra Review