# 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*