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

##### Lectures in this study track

This lecture covers an Introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model.

This lecture describes non-spiking simple neuron models used in artificial neural networks and machine learning.

The ionic basis of the action potential, including the Hodgkin Huxley model.

Forms of plasticity on many levels - short-term, long-term, metaplasticity, structural plasticity. With examples related to modelling of…

Introduction to modelling of chemical computation in the brain

Conference presentation on computationally demanding studies of synaptic plasticity on the molecular level

Pattern generation in visual system hallucinations.

Introduction to the role of models in theoretical neuroscience

Different types of models, model complexity, and how to choose an appropriate model.

Balanced E-I networks, stability and gain modulation

Methods for dimensionality reduction of data, with focus on factor analysis.

Methods for dimensionality reduction of data, with focus on factor analysis.

Bayesian neuron models and parameter estimation.

Constraints can help us understand how the brain works.

Approaching neural systems from an evolutionary perspective

##### Courses in this study track

Introductory lectures on different aspects of biochemical models

Introductory lectures on different aspects of Biophysical models.

The CAJAL computational neuroscience courses teaches the central ideas, methods, and practice of modern computational neuroscience through a…

Introductory lectures on different aspects of biochemical models.

Introductory lectures on different aspects of statistical models.