HNN-core: A Python software for cellular and circuit-level interpretation of human MEG/EEG

Mainak Jas, Ryan Thorpe, Nicholas Tolley, Christopher Bailey, Steven Brandt, Blake Caldwell, Huzi Cheng, Dylan Daniels, Carolina Fernandez Pujol, Mostafa Khalil, Samika Kanekar, Carmen Kohl, Orsolya Kolozsvári, Kaisu Lankinen, Kenneth Loi, Sam Neymotin, Rajat Partani, Mattan Pelah, Alex Rockhill, Mohamed SherifMatti Hamalainen, Stephanie Jones*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

HNN-core is a library for circuit and cellular level interpretation of non-invasive human magneto-/electro-encephalography (MEG/EEG) data. It is based on the Human Neocortical Neurosolver (HNN) software (Neymotin et al., 2020), a modeling tool designed to simulate multiscale neural mechanisms generating current dipoles in a localized patch of neocortex. HNN's foundation is a biophysically detailed neural network representing a canonical neocortical column containing populations of pyramidal and inhibitory neurons together with layer-specific exogenous synaptic drive (Figure 1 left). In addition to simulating network-level interactions, HNN produces the intracellular currents in the long apical dendrites of pyramidal cells across the cortical layers known to be responsible for macroscopic current dipole generation.
Original languageEnglish
Article number5848
JournalJournal of Open Source Software
Volume8
Issue number92
DOIs
Publication statusPublished - 15 Dec 2023
MoE publication typeA1 Journal article-refereed

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