Computational neuroscience is a branch of neuroscience which employs mathematical models, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology, and cognitive abilities of the nervous system.
Most who enter the field of computational neuroscience have a prior background in either mathematics, physics, computer science, or (neuro)biology. Since computational neuroscience requires a bit of knowledge from all these fields, with some basic knowledge of neurons and a familiarity with certain types of equations and mathematical concepts, we recommend two different "starting tracks" depending on the student's background before you begin the lectures listed below:
- Intro to computational neuroscience for a computer sci/math background. The student should learn basic concepts and equations for how neurons generate signals, either a more thorough introduction via the Cellular Mechanisms of Brain Function course or a quick reminder via the Basic mathematics for computational neuroscience tutorials
- Intro to computational neuroscience for a biology background. Here the student is assumed to already have basic knowledge of neurons. We recommend some orientation in mathematics (differential equations, linear algebra, dynamical systems) and computer science. There are a number of possible online courses openly available, for instance the MIT OpenCourseware course on Differential Equations. After that, we recommend a quick orientation on how these mathematics apply to neuroscience by viewing the Basic mathematics for computational neuroscience tutorials