9780123745514-0123745519-MATLAB for Neuroscientists: An Introduction to Scientific Computing in MATLAB

MATLAB for Neuroscientists: An Introduction to Scientific Computing in MATLAB

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Summary

MATLAB for Neuroscientists: An Introduction to Scientific Computing in MATLAB (ISBN-13: 9780123745514 and ISBN-10: 0123745519), written by authors Pascal Wallisch PhD, Michael E. Lusignan, Marc D. Benayoun, Tanya I. Baker, Adam Seth Dickey, Nicholas G. Hatsopoulos, was published by Academic Press in 2008. With an overall rating of 4.2 stars, it's a notable title among other Neuropsychology (Psychology & Counseling, Physiological Aspects, Cognitive Psychology, Behavioral Sciences, Cognitive, Psychology, Neuropsychology, Physiological Aspects) books. You can easily purchase or rent MATLAB for Neuroscientists: An Introduction to Scientific Computing in MATLAB (Hardcover) from BooksRun, along with many other new and used Neuropsychology books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.49.

Description

MATLAB for Neuroscientists: An Introduction to Scientific Computing in MATLAB 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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