Open Education Resources

Learning without barriers.

We believe in open science and open education. That's why all of our course materials are available for free all the time for everyone, everywhere.

Python

Every computational neuroscientist starts somewhere, and for most, it starts with Python. Master the basics and you unlock the field. Python is an Academy course prerequiste.

Python Workshops

Workshop 1

  • Learn basic operations with Python variables, control flow, plotting, and take a sneak peek at np.array, the workhorse of scientific computation in Python.

Workshop 2

  • Introduction to spikes in our LIF neuron and evaluate the refractory period’s effect in spiking dynamics.

Python for Computational Science Week

  • Every January we run Python Week; a free, intensive introduction to Python for anyone looking to get started in computational neuroscience. Python Week 2027 information coming soon!

Neuroscience

Open-access resources to explore the intersection of neuroscience and machine learning.

Computational Neuroscience - Github Link – Coursebook Link

  • Learn cutting-edge advances in machine learning and causality research with state-of-the-art modeling approaches in neuroscience. You'll get an introduction to modeling and go through modules on machine learning, dynamic systems, stochastic processes, and causality. 
  • Equivalent to 15 full days of instruction plus bonus materials. 

NeuroAI - Github Link – Coursebook Link

  • Neuroscience, cognitive science, and AI are all questing for principles that help generalization. This course goes over major system features that affect generalization include: task structure, microcircuitry, macrocircuitry or architecture, learning rules, and data stream.
  • Equivalent to 10 full days of instruction. 

 

Climate Science

Learn cutting-edge techniques from climate science experts to explore the social and environmental effects of climate change.

Open Science 101 - GitHub Link – Coursebook Link

  • Developed by NASA, this curriculum introduces learners to a basic understanding of open science, its ethos and benefits, and how to actively participate in open science communities.
  • Equivalent to 5 partial days of content.

Machine Learning

Open-access resources to learn about deep learning and neuroAI.

Deep Learning - Github Link – Coursebook Link

  • You’ll learn core topics in DL, including linear DL, optimization, regularization, NLP, generative models, unsupervised learning, and reinforcement learning.
  • Equivalent to 15 full days of instruction plus bonus materials. 

NeuroAI - Github Link – Coursebook Link

  • Neuroscience, cognitive science, and AI are all questing for principles that help generalization. This course goes over major system features that affect generalization include: task structure, microcircuitry, macrocircuitry or architecture, learning rules, and data stream.
  • Equivalent to 10 full days of instruction. 

 

Open Science

Foundational learning resources for understanding open science principles and participation.

Open Science 101 - GitHub Link – Coursebook Link

  • Developed by NASA, this curriculum introduces learners to a basic understanding of open science, its ethos and benefits, and how to actively participate in open science communities.
  • Equivalent to 5 partial days of content.

How to credit Neuromatch materials

We’re thrilled when people reuse and adapt Neuromatch materials to support learning in their communities. While we encourage this, external uses aren’t officially reviewed or endorsed by Neuromatch. If you reuse or adapt them, please include attribution, a link to the source materials, and a link to the CC-BY 4.0 license. If you make modifications, indicate what you changed.

Examples of Attribution:
1Academic/Formal publication citation
“Neuromatch Academy. (Accessed [Month, Day, Year]). [Course Name] course content. Open educational resource. GitHub. [Github link].”
2Syllabus (adapted)
“We adapted materials from Neuromatch Academy’s [Course Name] under a CC‑BY 4.0 license. In our version, we updated the data examples and modified several exercises to better fit our students’ background. Full materials and original license: [Github link].”
3Syllabus (no adaptation):
“We used materials from Neuromatch Academy’s [Course Name] under a CC‑BY 4.0 license. Full materials and original license: [Github link]”
4Webinar/Talk
“The learning materials discussed are adapted from Neuromatch Academy’s [Course Name] materials under a CC‑BY 4.0 license. We will share a link [in the chat/afterwards in an email/in the handout] to the full materials.”