Our Mission

Neuromatch Academy’s mission is to provide high-quality training in theoretical modelling and computational techniques related to neuroscience in a maximally accessible fashion to everyone in the world. We achieve this mission through curated, code-first online teaching with paid TA support while keeping participant costs at a minimum and making them waivable with no questions asked.


Our first success was the neuromatch conference held online on March 30-31st, 2020 which brought together nearly 3,000 attendees from around the world as part of 120 talks given by both senior and junior scientists. Reflecting our core philosophy of building connections and community we matched over 500 participants to 6 other partners each, based on interests gleaned from natural language processing of submitted abstracts.

Neuromatch Academy LLC is now part of Neuromatch Inc., a nonprofit (501(c)(3) in the United States) organization including Neuromatch Conference LLC. The overarching goal of Neuromatch Inc. is to foster inclusive global interactions for learning, mentorship, networking, and professional development.

The Neuromatch Academy

In 2020 we organized a three-week-long online summer school for July 13-31, 2020. This was in response to the Covid-19 pandemic which shut down nearly every summer program in the world. At these programs, students, postdocs and faculty would normally gather to acquire crucial skills and build networks that are the lifeblood of academic science. This crisis left an enormous hole in the career prospects of our most valuable and vulnerable scientists. With Neuromatch Academy we aimed to fill that gap, and in the process to build a unique model of individualized, intensive academic training that is available to nearly everyone, rather than to the privileged few. In place of the legacy summer program model, which typically charges ~$ 4000, we offered a three-week intensive program with a small fee for full-time students/academics (with fee waivers available). This program combined features of both legacy summer workshops and online course work to create a unique educational platform that is tailored to furthering academic careers.

Neuromatch Academy represents a unique opportunity to build an inclusive summer school experience that spans the entire globe and provides a fundamental education in modern computational neuroscience. Many of the students who attend the Academy would not have had the opportunity to attend an in-person summer school due to financial or travel restrictions, or to limited space in these schools. The Academy aims to provide equal access to excellent computational neuroscience training for all students regardless of geography, nationality, socioeconomic status, or other factors.


The Neuromatch Academy computational neuroscience curriculum integrates cutting-edge advances in machine learning and causality reasearch with state-of-the-art modeling approaches in neuroscience. We teach in a code-first, hands-on Python tutorial-based format with teaching assistant (TA) support (interactive track only). But we don't just teach techniques: we emphasize interpretability and the process of modeling. Through group projects, participants get hands-on guided experience in how-to-model any observed phenomemon. We thus focus on policies, procedures, and processes: we teach a meta-science approach that translates to all of science! Group projects are supported by TAs and expert mentors in the field. This also results in professional networking opportunities that go beyond the project group.

For summer 2021, we also offered a Neuromatch Academy Deep Learning (DL) curriculum. This grew out of the realization that there is a real need for teaching an ethically responsible hands-on TA-guided code-first DL curriculum that emphasizes how DL can be used to advance science and achieve better scientific insights. Science explains data, allows for generalization, and enables us to understand the goals of systems; it lets us understand causal inks and the mechanisms mediating these causes. DL approaches promise to help each of these aspects of scientific discovery because they are designed to handle the complexity of modern questions and datasets; and DL can thus complement the roles of statistics and scientists themselves.