HNN: Human Neocortical Neurosolver

[samnemo]

We recently built the Human Neocortical Neurosolver (HNN; https://hnn.brown.edu), a user-friendly software tool that gives researchers/clinicians the ability to test and develop hypotheses on circuit mechanisms underlying EEG/MEG data in an easy-to-use environment. The foundation of HNN is a computational neural model that runs in the NEURON simulator with Python interpreter. Our model simulates the electrical activity of the neocortical cells and circuits that generate the primary electrical currents underlying EEG/MEG recordings.

On our website, we provide tutorials on how to import your data and understand the underlying circuit mechanisms. We encourage you to use this forum to ask questions about HNN, offer suggestions for future developments, or to report bugs. We also designed HNN to be expandable and generative. If you know about computational neuroscience, consider contributing to our open-source developments.
We look forward to hearing your feedback and working with you to improve HNN. Thanks for your interest!
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HNN is being developed primarily by the Jones lab at Brown University, in collaboration with Yale University and Massachusetts General Hospital. HNN is funded by NIH BRAIN NIBIB R01 EB022889-02 (PI: Stephanie Jones; Co-PIs: Michael Hines and Matti Hamalainen), NIMH R01 MH106147, Carney Institute for Brain Science, and Providence VA CfNN.