Computational Neuroscience and Cognitive Modelling
A Student's Introduction to Methods and Procedures
- Britt Anderson - University of Waterloo, Canada
Cognitive Psychology (General) | Neuro-psychology | Statistical Computing Environments
"For the neuroscientist or psychologist who cringes at the sight of mathematical formulae and whose eyes glaze over at terms like differential equations, linear algebra, vectors, matrices, Bayes’ rule, and Boolean logic, this book just might be the therapy needed."- Anjan Chatterjee, Professor of Neurology, University of Pennsylvania
"Anderson provides a gentle introduction to computational aspects of psychological science, managing to respect the reader’s intelligence while also being completely unintimidating. Using carefully-selected computational demonstrations, he guides students through a wide array of important approaches and tools, with little in the way of prerequisites...I recommend it with enthusiasm."
- Asohan Amarasingham, The City University of New York
This unique, self-contained and accessible textbook provides an introduction to computational modelling neuroscience accessible to readers with little or no background in computing or mathematics. Organized into thematic sections, the book spans from modelling integrate and firing neurons to playing the game Rock, Paper, Scissors in ACT-R. This non-technical guide shows how basic knowledge and modern computers can be combined for interesting simulations, progressing from early exercises utilizing spreadsheets, to simple programs in Python. Key Features include:
- Interleaved chapters that show how traditional computing constructs are simply disguised versions of the spread sheet methods.
- Mathematical facts and notation needed to understand the modelling methods are presented at their most basic and are interleaved with biographical and historical notes for contex.
- Numerous worked examples to demonstrate the themes and procedures of cognitive modelling.
An excellent text for postgraduate students taking courses in research methods, computational neuroscience, computational modelling, cognitive science and neuroscience. It will be especially valuable to psychology students.
For the neuroscientist or psychologist who cringes at the sight of mathematical formulae and whose eyes glaze over at terms like differential equations, linear algebra, vectors, matrices, Bayes’ rule, and Boolean logic, this book just might be the therapy needed. Britt Anderson guides the reader into the world of computational methods; writing lucidly and grounding this journey with elegantly constructed exercises. His slender book is an invitation to use tools that will help students and scientists think about neural and psychological mechanisms with rigor and precision.
The neural and cognitive sciences are increasingly quantitative and computational subjects, and curriculums are now attempting to reflect this emerging reality. Accordingly, an important educational challenge is to inform undergraduate students of the significance of computational thinking, while also preparing them to appreciate and criticize it. An Invitation to Computational Neuroscience and Cognitive Modeling achieves this difficult goal wonderfully. Anderson provides a gentle introduction to computational aspects of psychological science, managing to respect the reader’s intelligence while also being completely unintimidating. Using carefully-selected computational demonstrations, he guides students through a wide array of important approaches and tools, with little in the way of prerequisites. As well as a very practical introduction to computer programming, there is impressive coverage of dynamical systems models of neurons, neural network models of memory, probabilistic models of decision-making, and mathematical models of thought. I recommend it with enthusiasm.
Really good one for introduction. I recommended it especially to first-term master's students.
The authors try to cover a variety of different topics ranging from theoretical neuroscience, to applied computational neuroscience and cognitive modeling.
In the end, this broad approach may provide as a very rough overview, but doesn't leave the reader with much knowledge that is really applicable.
Also, I don't really see the use of Python as a plus; the language is less intuitive to understand than MATLAB, and (although many people don't like to hear it) far away from becoming a standard in our field (which MATLAB still is).
This text would be recommended for M-Level students as a step by step guide to computational neuroscience.
Anderson shares his journey towards developing an understanding in this area which will be of great support to students who are keen to learn more about computation modelling methods and procedures.
Sample Materials & Chapters
Chapter 1: An Introduction to the Ideas and Scope of Computational Methods in Ps