"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.
Chapter 1: IntroductionPART 1: Modeling NeuronsChapter 2: What is a Differential Equation?Chapter 3: Numerical Application of a Differential EquationChapter 4: Intermezzo: Computing With LoopsChapter 5: Integrate & FireChapter 6: Intermezzo: Computing With If StatementsChapter 7: Hodgkin & Huxley: The Men and Their ModelChapter 8: Intermezzo: Computing With FunctionsPART 2: Neural NetworksChapter 9: Neural Network Mathematics: Vectors and MatricesChapter 10: Intermezzo: Interactive ComputingChapter 11: An Introduction to Neural NetworksChapter 12: Intermezzo: Interactive Exploration of the Delta Rule with OctaveChapter 13: Auto-associative Memory and the Hopfield NetPART 3: Probability and Psychological ModelsChapter 14: What are the Odds?Chapter 15: Decisions as Random WalksChapter 16: Intermezzo: Programming Psychophysical Experiments with PythonPART 4: Cognitive Modeling as Logic and RulesChapter 17: Boolean LogicChapter 18: Intermezzo: Computing With Functional LanguagesChapter 19: Production Rules and CognitionChapter 20: Intermezzo: Functional Coding of a Simple Production SystemChapter 21 ACT-R: A Cognitive ArchitectureChapter 22: Agent Based ModelingChapter 23: Concluding Remarks