Magneto- and electro-encephalography (MEG/EEG) non-invasively record human brain activity with millisecond resolution, providing reliable markers of healthy and disease states. A downside of these rapidly advancing technologies is a lack of understanding the underlying neural generators, which limits their use in developing new theories of information processing or new methods for treating neuropathology. Human Neocortical Neurosolver (HNN) is a user-friendly software tool designed to bridge these macro-scale signals to the underlying cellular and circuit level activity. The foundation of HNN is a computational neural model that simulates the activity of the neocortical cells and circuits that generate the electrical currents underlying MEG/EEG. HNN’s model is embedded in a graphical user interface and distributed with tutorials that teach a broad community how to study the neural origin of the most commonly measured MEG/EEG signals, including event-related activity and low frequency brain rhythms. HNN is also developed following best practices in open source software design in order to facilitate the engagement of the computational neuroscience community in its use and development.

HNN gives researchers and clinicians the ability to test and develop hypotheses about the circuit mechanism underlying their EEG/MEG data in an easy-to-use environment. HNN is uniquely constructed with the detail necessary to link human recordings to microcircuit details that can be studied in animal models and where advances in genetic engineering can be harnessed. HNN has the potential to revolutionize the rate of principled inference about the function of the human brain and to uncover new ways to diagnose and treat neuropathology.