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Mathematics for Neuroscientists

Book Companion

Mathematics for Neuroscientists

Edition 2

Welcome to the Companion site for Gabbiani, Cox: Mathematics for Neuroscientists , 2nd Edition.

Mathematics for Neuroscientists

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. Mathematics for Neuroscientists 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 and presuming no more than calculus through elementary differential equations, the level builds up as increasingly complex techniques are introduced and combined with earlier techniques. 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. Python code is provided for a select subsample of the same figures. 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.

Key features of the second edition:
  • Fully revised material and corrected text

  • Additional chapters on extracellular potentials, motion detection and neurovascular coupling

  • Revised selection of exercises with solutions

  • More than 200 Matlab scripts reproducing the figures as well as a selection of equivalent Python scripts

Disclaimer

Information provided in this document is provided "as is" without warranty of any kind, either express or implied. Every effort has been made to ensure accuracy and conformance to standards accepted at the time of publication. The reader is advised to research other sources of information on these topics.

The user assumes the entire risk as to the accuracy and the use of this document. This document may be copied and distributed subject to the following conditions:

All text must be copied without modification and all pages must be included; All copies must contain the appropriate copyright notice and any other notices provided therein; and This document may not be distributed for profit.

Fabrizio Gabbiani
Baylor College of Medicine, Houston, TX, USA

Dr. Gabbiani is Professor in the Department of Neuroscience at Baylor College of Medicine. Having received the Alexander von Humboldt Foundation research award in 2012, he completed a one-year cross appointment at the Max Planck Institute of Neurobiology in Martinsried and has international experience in the computational neuroscience field. Together with Dr. Cox, Dr. Gabbiani co-authored the first edition of this bestselling book in 2010.

Fabrizio Gabbiani

Fabrizio Gabbiani

Steven J. Cox
Computational and Applied Mathematics, Rice University, Houston, TX, USA

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 Baylor College of Medicine. In addition, Dr. Cox has served as Associate Editor for a number of mathematics journals, including Mathematical Medicine and Biology and Inverse Problems. He previously authored the first edition of this title with Dr. Gabbiani.

Steven J. Cox

Matlab Codes

The web resources for the new edition (except for python code) can be viewed at http://www.caam.rice.edu/~cox/booksite/(opens in new tab/window)

You can click on the ReadMe to see changes and additions.

Python Codes

The corresponding python code for a subset can be viewed at http://www.caam.rice.edu/~cox/booksite/python(opens in new tab/window)

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