The Human Neocortical Neurosolver (HNN) is an open-source neural modeling tool designed to help researchers/clinicians interpret human brain imaging data. HNN presents a convenient GUI to an anatomically and biophysically detailed model of human thalamocortical brain circuits, which makes it easier to generate and evaluate hypotheses on the mechanistic origin of signals measured with MEG/EEG or intracranial ECoG. A unique feature of HNN's model is that it accounts for the biophysics generating the primary electric currents underlying such data, so simulation results are directly comparable to source localized data (nano-Ampere-meters); this enables precise tuning of model parameters to match characteristics of recorded signals.

We are integrating the circuit-level modeling with the minimum-norm-estimate (MNE) source localization software (https://martinos.org/mne/stable/index.html), so researchers can compute MEG/EEG source estimates and test hypotheses on the circuit origin of their data in one software package. Our goal is to design HNN to be useful to researchers with no formal computational neural modeling or coding experience.

For more information visit https://hnn.brown.edu . There, we describe the use of HNN in studying the circuit-level origin of some of the most commonly measured MEG/EEG and ECoG signal: event related potentials (ERPs) and low frequency rhythms (alpha/beta/gamma).

For questions, comments/feedback, or troubleshooting information please contact us at hnneurosolver@gmail.com, and review our user forum at https://www.neuron.yale.edu/phpBB/viewforum.php?f=46 .

To cite our software please use the following document object identifier: