Project D: Neuromatch Academy

You are skimming: the title, the first figure, and the short version. Switch to Read in the header for the full page, or Deep to open every deep dive.

Neuromatch AcademyNeuromatch AcademyA free, online, three-week summer school in computational neuroscience with a large alumni network. Glossary entry started in 2020 as an emergency online replacement for a summer school and became the largest computational neuroscience course in the world. Three weeks, every weekday, in a small pod of students with a teaching assistant, working through tutorials written by leading researchers and building a group project on real data. It is free or nearly so, it is taught in many time zones, and its alumni are now in labs and companies everywhere. For a student on this site, it is the single most efficient way to get the computational neuroscience spine’s fundamentals in one go.

The tracks

Computational Neuroscience is the original: model fitting, linear algebra for neuroscience, dynamical systems, biological neuron models, network dynamics, Bayesian inference, reinforcement learning, and a project with a dataset from the Allen Institute, Steinmetz, or others. Deep Learning covers modern machine learning with a neuroscience flavour. NeuroAI is the newest. For this curriculum, Computational Neuroscience first; Deep Learning the following year if you want the decoding spine sharpened.

Getting in

Applications open in the spring, with a deadline typically in March or April (check the calendar and the site). The interactive track needs commitment to the daily schedule; an observer track lets you follow the materials without a pod. Fee waivers are routine for students. The application asks about your background and why; be specific about what you have already built, which is where this site’s build logs pay off.

Arriving ready

The course assumes comfortable Python with NumPy, basic linear algebra (vectors, matrices, eigenvectors), basic probability, and calculus through derivatives and integrals. It runs pre-course refreshers, which are excellent. If Phase 1 to 3 are behind you, you are more than ready on the coding side; spend the month before on linear algebra and probability from the math thread.

Getting the most out of it

Do the tutorials before the pod session, not during it; the session is for the parts you got stuck on. Pick a project that uses real neural data (spikes or LFP) rather than a purely theoretical one, because that is the experience the next steps need. Talk to your teaching assistant about their lab; they are graduate students and postdocs and they know who is hiring undergraduates. Keep the pod’s chat group afterward.

If you cannot attend

All materials are public year-round on the Neuromatch site, with videos and notebooks. Working through the Computational Neuroscience tutorials alone, one day a week for a semester, gets most of the content without the pod. Pair it with the Coursera Computational Neuroscience course (Rao and Fairhall) for a second angle on the same material.