1 Introduction to the book

First and foremost, this book is an active work in progress and is liable to change somehwat from week to week. If you find errors, or if you find any section particularly confusion, please feel free to reach out to me for help.

Python competency: We will spend a little time going over Python but we will move fast. There are too many other excellent resources available on this topic for me to devote much time and space to it here.

Computational literacy: The main objective of the book is for you to be able to spend time with published computational modelling papers, and replicate most or all of the results in those papers. This is a super power among cognitive neuroscientist.

Mathematical literacy: Math is beautiful and useful and I encourage to learn as much of it as you can. A good goal for a casual computational modeller is to have made it through linear algebra, calculus, and a first year differential equations unit. However, you can still have a great time and learn a lot from this book without much of a math background. You will certainly have to tolerate looking at some fancy looking math — e.g., derivatives (\(\frac{d}{dx}f(x)\)), integrals (\(\int x dx\)), and differential equations (\(\frac{d}{dx}f(x) = g(x)\)) — but the emphasis will be on translating these things into code, not on interacting with them much in the abstract.