This chapter by chapter review was inspired by Vaniver's recent chapter by chapter review of Causality. Like with that review, the intention is not so much to summarize but to help readers determine whether or not they should read the book. Reading the review is in no way a substitute for reading the book.

I first read Introduction to Connectionist Modelling of Cognitive Processes (ICMCP) as part of an undergraduate course on cognitive modelling. We were assigned one half of the book to read: I ended up reading every page. Recently I felt like I should read it again, so I bought a used copy off Amazon. That was money well spent: the book was just as good as I remembered.

By their nature, artificial neural networks (referred to as connectionist networks in the book) are a very mathy topic, and it would be easy to write a textbook that was nothing but formulas and very hard to understand. And while ICMCP also spends a lot of time talking about the math behind the various kinds of neural nets, it does its best to explain things as intuitively as possible, sticking to elementary mathematics and elaborating on the reasons of why the equations are what they are. At this, it succeeds – it can be easily understood by someone knowing only high school math. I haven't personally studied ANNs at a more advanced level, but I would imagine that anybody who intended to do so would greatly benefit from the strong conceptual and historical understanding ICMCP provided.

The book also comes with a floppy disk containing a tlearn simulator which can be used to run various exercises given in the book. I haven't tried using this program, so I won't comment on it, nor on the exercises.

The book has 15 chapters, and it is divided into two sections: principles and applications.

Principles

1: ”The basics of connectionist information processing” provides a general overview of how ANNs work. The chapter begins by providing a verbal summary of five assumptions of connectionist modelling: that 1) neurons integrate information, 2) neurons pass information about the level of their input, 3) brain structure is layered, 4) the influence of one neuron on another depends on the strength of the connection between them, and 5) learning is achieved by changing the strengths of connections between neurons. After this verbal introduction, the basic symbols and equations relating to ANNs are introduced simultaneously with an explanation of how the ”neurons” in an ANN model work.