Tutorial on Neural Systems Modeling.
Shelby A. Montague · Europe PMC (PubMed Central) · 2011
Thomas J. Anastasio states in the preface of Tutorial on Neural Systems Modeling that the book is “intended for readers who want to develop an understanding of neural systems modeling, but who lack specialized backgrounds in mathematics, computer programming, or neuroscience.” This textbook does provide an excellent introduction to modeling for students with little or no mathematical or programming background. However, for a student with a computational background, or even one with working MATLAB knowledge, the text can be tedious at times. Each chapter begins by introducing a topic within neuroscience. For example, the chapter on lateral inhibition begins by introducing general concepts in visual processing before focusing on vision in Limulus, a well-known example of a visual system that utilizes lateral inhibition. These introductions place the neural models to be discussed in the chapter into a biological framework. The chapters then continue on to model the biological system using basic mathematical concepts and MATLAB scripts. But for a student with a working MATLAB knowledge, the line-by-line descriptions of commands and changes to variables are often tedious and take away from the overall point of the modeling exercise. On the other hand, such detailed descriptions would be very useful for a student without MATLAB knowledge. The models described in the text of each chapter are supported by figures, MATLAB boxes, and math boxes. The figures show both the biological data on which a model is based as well as the MATLAB output expected from the modeling of that data. Each MATLAB box contains the text of the script that generates the neural model discussed in that section. These scripts are also available for download from the book’s companion website. Math boxes explain mathematical concepts presented in the text in more detail. These boxes describe an array of topics, from eigenmode analysis to vector multiplication to solving systems of linear equations. While the Math boxes cannot describe these concepts in full detail, they serve their purpose well; they make the reader aware of the mathematics required to create each model and provide a basis for further study, if the reader chooses. Each chapter ends with a list of exercises. These exercises require the student to take the models created in the chapter one step farther and evaluate the models for himself. These exercise questions are well-written and address the main points of each chapter. In the end, Tutorial on Neural Systems Modeling makes neural models both accessible and interesting for students of any background. Just be sure to read the text while sitting at a computer with MATLAB running.