Neuroscience for machine learners

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A freely available short course on neuroscience for people with a machine learning background. Designed by Dan Goodman and Marcus Ghosh.

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About

This is a freely available online course on neuroscience for people with a machine learning background. The aim is to bring together these two fields that have a shared goal in understanding intelligent processes. Rather than pushing for “neuroscience-inspired” ideas in machine learning, the idea is to broaden the conceptions of both fields to incorporate elements of the other in the hope that this will lead to new, creative thinking. Note that if this page is causing you scrolling issues on iPhone Safari, click here.

The course is given in person at the Department for Electrical and Electronic Engineering, Imperial College London, and made freely available online (although without the practical classes).

Each week there are a series of videos to watch on YouTube, and a set of exercises available as a Jupyter notebook that can be run locally or via Google Colab. Students at Imperial College can discuss on Teams, and for everyone else there is an open Discord server.

We hope you enjoy the course!

Quick links

Week by week

The course is still under development, links to materials will be added as and when they become available. Contents may change.

Week 0 - Introduction and motivation

Week 1 - Neurons

Week 2 - Synapses and networks

Week 3 - Brain structure

Week 4 - Learning rules

Week 5 - Spiking neural networks

Week 6 - Understanding neural networks

Week 7 - Various topics

Only one video this week, more will be added in future.

Week 8 - Neuromorphic computing

This week’s videos feature research by Gabriel Béna (although any errors you find are probably the result of Dan’s interference).

Week 9 - Open issues

Accessibility

The Powerpoint slides contain (in the speaker notes) a complete transcript of everything we say.

Youtube automatic captions are of reasonable quality, and after the course is complete we will update them with manual transcripts. For the moment, the Powerpoint slides and automatic captions can be used.

Extended reading list

There is no required reading for this course, only optional. We will be curating a list of papers that are meant to be about inspiring creativity and joy in this field. For the moment though, you can check out:

Credits