Textbook in PDF format

My own efforts to develop a little computational competency have followed a fairly stand-ard path. Whenever I came across some research article with an intriguing title, I would try to read it, usually only be able to get through the introduction and conclusion, and be further convinced that it was just the ticket for some project I was working on. However, problems would begin when I tried to plow through the methods section. This would yield some blur of terminology and formulas that I did not understand. In trying to push on, I would look up a term and my efforts to answer one question would to lead to two others; my route would bifurcate and grow. An article on a model of memory using neural net-works might lead me to read someone’s MATLAB code. My ignorance of MATLAB would lead me to questions about programming. This could lead to questions about “vectorizing.” And that might bring me to learn a bit about vectors and linear algebra. Occasionally, the loop would close. I would realize that vectors were the mathematical “things’’ at the heart of many neural network algorithms. I could envision how they needed to be manipulated, and then I could understand the MATLAB code that had been programmed explicitly for the purpose of making those manipulations. Finally, I could understand the article that had started the whole process.What I am trying to write here, of course, is the book I wished I had had way back when. A book that defined common mathematical notation, covered the basics of computer pro-gramming terminology, and had enough elementary examples that I could see how things were supposed to work in the small, before trying to apply them to some larger problem. No book of any practical size can accomplish this for all the topics that are relevant for psychology and neuroscience, but I do believe that it can be done for a sample of topics, and thereby serve as an introduction and invitation to further study.
My principal goal is for the book to provide an entrance to a world of useful investigatory tools. It has taken me a long time and a lot of work to get to where I am today. I feel my understanding is only a fraction of what I aspire to, but it is still enough for me to appreciate the power and pleasure of computational modelling. You will not be a mathematician when you finish this book, but you should have a foundation, and you should be confident that the additional hard work required to become proficient is well within your grasp. You will be able to decide for yourself if the work required is worth your while, and if you decide not to pursue further studies along this line, you will know that you could have, you will have a deeper understanding of the work required by those who do, and you will be a better collaborator if you work with a computational model-ler. Should you elect to pursue additional studies on any of these topics, I would be very pleased if you would take the time to share your progress with me. In any case, I look forward to receiving feedback on the book.
Introduction
I. Modelling Neurons
II. Neural Networks
III. Probability and Psychological Models
IV. Cognitive Modelling as Logic and Rules
Concluding Remarks