We are pleased to announce the release of HNN-core 0.1. HNN-core is a new package based on the Human Neocortical Neurosolver (HNN) software and offers a Pythonic interface for simulating macroscale human MEG and EEG signals from a biophysically-detailed neocortical model. Similar to HNN, 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. The network model and exogenous driving inputs to activate the network are identical to those in HNN.
The goal of HNN-core is complementary to HNN. It provides an object-oriented design that allows the computational neuroscience community to understand and contribute to development and use of the HNN software analysis toolkit. The command-line utility allows easy batch processing and integration with other Python based packages, such as MNE-Python.
Examples of use are provided for simulating commonly measured MEG/EEG signals, including event related potentials and low-frequency brain rhythms, following the tutorials of use in HNN: https://jonescompneurolab.github.io/hnn-core/stable/auto_examples/index.html.
Notable features of this release cycle include documentation, examples of use, a testing suite and development standards that ensure overall high code quality and robustness.
On Linux and Mac, it is possible to install HNN-core using a single line:
$ pip install hnn_core
Follow us on twitter here: https://twitter.com/HNNsolver
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)
- Blake Caldwell
- Christopher Bailey
- Carmen Kohl
- Mainak Jas
- Nick Tolley
- Ryan Thorpe
- Samika Kanekar
- Stephanie Jones