We are pleased to announce the release of HNN-core 0.2. HNN-core is a package that is part of the Human Neocortical Neurosolver (HNN) software suite and offers a Pythonic interface for simulating macroscale human MEG and EEG signals from a biophysically-detailed neocortical model. HNN-core is designed to simulate primary electrical current time courses (i.e., current dipoles) that can be compared with source-localized MEG and EEG data.
Examples of use are provided for simulating commonly measured MEG/EEG signals, including event related potentials and low-frequency brain rhythms.These examples closely follow the more detailed tutorials provided for HNN-GUI.
This release includes several exciting new features including the ability to record local field potentials, optimization routines for experimental data and easy modification of connectivity/cell properties.
On Linux and Mac, it is possible to install HNN-core using a single line:
$ pip install --upgrade hnn_core
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We welcome your bug reports, feature requests, critiques and contributions on our Github page.
HNN-core development team
People who contributed to this release (in alphabetical order)
- Alex Rockhill
- Blake Caldwell
- Christopher Bailey
- Dylan Daniels
- Kenneth Loi
- Mainak Jas
- Nick Tolley
- Ryan Thorpe
- Sarah Pugliese
- Stephanie R. Jones