Course materials

Our goal is to educate undergraduate, graduate, and postdoc researchers from diverse backgrounds in the philosophy and methods of computational neuroscience. We have enlisted a team of leading researchers to provide lectures and tutorial materials for each of the concepts we aim to teach.

We have produced 15 days of content for NMA 2020, in the form of lectures and interactive coding tutorials hosted in Google Colab. Each day has been tested in front a live audience and edited to create polished materials. More than 300 person-hours have been spent writing, testing, editing and polishing each day of content. We make them freely available to the community under a CC-BY-4.0 license.

W1D1 - Model TypesIntro videoOutro video
W1D2 - Modeling PracticeIntro videoOutro video
W1D3 - Model FittingIntro videoOutro video
W1D4 - Machine LearningIntro videoOutro video
W1D5 - Dimensionality ReductionIntro videoOutro video
W2D1 - Bayesian StatisticsIntro videoOutro video
W2D2 - Linear SystemsIntro videoOutro video
W2D3 - Decision MakingIntro videoOutro video
W2D4 - Optimal ControlIntro videoOutro video
W2D5 - Reinforcement LearningIntro videoOutro video
W3D1 - Real NeuronsIntro videoOutro video
W3D2 - Dynamic NetworksIntro videoOutro video
W3D3 - Network CausalityIntro videoOutro video
W3D4 - Deep Learning 1Intro videoOutro video
W3D5 - Deep Learning 2Intro videoOutro video