HNN-core 0.1 released!

Dear all,

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:

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

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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)

  • Blake Caldwell
  • Christopher Bailey
  • Carmen Kohl
  • Mainak Jas
  • Nick Tolley
  • Ryan Thorpe
  • Samika Kanekar
  • Stephanie Jones
1 Like

awesome work @mainakjas et al. !

congrats !


Congrats from me too, this sounds super interesting!