hnn-core#
About#
This is a leaner and cleaner version of the code based off the HNN repository.
The Human Neocortical Neurosolver (HNN) is an open-source neural modeling tool designed to help researchers/clinicians interpret human brain imaging data. Based off the original HNN repository, HNN-core provides a convenient way to run simulations of an anatomically and biophysically detailed dynamical system model of human thalamocortical brain circuits with only a few lines of code. Given its modular, object-oriented design, HNN-core makes it easy to generate and evaluate hypotheses on the mechanistic origin of signals measured with magnetoencephalography (MEG), electroencephalography (EEG), or intracranial electrocorticography (ECoG). A unique feature of the HNN 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 (current dipoles in units of nano-Ampere-meters); this enables precise tuning of model parameters to match characteristics of recorded signals. Multimodal neurophysiology data such as local field potential (LFP), current-source density (CSD), and spiking dynamics can also be simulated simultaneously with current dipoles.
While the HNN-core API is designed to be flexible and serve users with varying levels of coding expertise, the HNN-core GUI is designed 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 signals: event related potentials (ERPs) and low frequency rhythms (alpha/beta/gamma).
Contributors are very welcome! Please read our Contributing Guide if you are interested.
Please consider supporting HNN development efforts by voluntarily providing your demographic information here! Note that any demographic information we collect is anonymized and aggregated for reporting on the grants that fund the continued development of HNN. All questions are voluntary.
Installation#
See Installation Guide. To install hnn-core with the minimum dependencies
on Mac or Linux, simply do:
$ pip install hnn_core
hnn-core currently supports Python 3.9 through 3.13, inclusively.
If you want to track the latest developments of hnn-core, you can
install the current version of the code (nightly) with:
$ pip install --upgrade https://api.github.com/repos/jonescompneurolab/hnn-core/zipball/master
If you are interested in features like GUI, Optimization, or Parallel support, or are on Windows, then please see our Installation Guide.
Documentation and examples#
Once you have installed hnn_core and the dependencies for the features you
want, we recommend downloading and executing the example
scripts
provided on the documentation
pages (as well as in the
GitHub repository).
Note that python plots are by default non-interactive (blocking): each
plot must thus be closed before the code execution continues. We
recommend using and ‘interactive’ python interpreter such as
ipython:
$ ipython --matplotlib
and executing the scripts using the %run-magic:
%run plot_simulate_evoked.py
When executed in this manner, the scripts will execute entirely, after which all plots will be shown. For an even more interactive experience, in which you execute code and interrogate plots in sequential blocks, we recommend editors such as VS Code and Spyder.
Bug reports#
Use the GitHub Issues tracker to report bugs. For user questions and scientific discussions, please see our GitHub Discussions page.
Interested in Contributing?#
Please read our Contributing Guide and make sure to abide by our Code of Conduct.
Governance Structure#
Citing#
If you use HNN-core in your work, please cite our publication in JOSS:
Jas et al., (2023). HNN-core: A Python software for cellular and circuit-level interpretation of human MEG/EEG. Journal of Open Source Software, 8(92), 5848, https://doi.org/10.21105/joss.05848