Getting started in computational neuroscience

How to gain the recommended background knowledge for learning computational neuroscience

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:

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 through 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.

Basics for computational neuroscience
Introduction to modeling the brain

Basic simplified models
Some simple (non-spiking) model of neurons

Statistical models
Tutorial on generalized linear models (pt1, pt2)

Basic biophysical models
Integrate and fire neuron modeling (slides)

Detailed biophysical models
Introduction to modeling the brain
Hodgkin & Huxley (Cellular Mechanisms of Brain Function lecture 8)
Space and dendritic integration
Rate vs temporal coding

Biochemical models
Modelling across scales of analysis
Principles of intracellular modelling and computation
Simulating the long time scales and large molecule numbers involved in synaptic plasticity

Network models
Modelling neural circuits

Dynamical neural systems
Single neurons I, Single neurons II
Oscillations and networks (pt1, pt2, pt3, pt4, pt5)
Waves and pattern formation
Cajal Course in Computational Neuroscience

Systems modelling

Plasticity and learning