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 Sherif; Matti Hamalainen; Stephanie Jones

doi:10.21105/joss.05848

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 Sherif

Matti Hamalainen, Stephanie Jones^*

^*Corresponding author for this work

Department of Neuroscience and Biomedical Engineering

Research output: Contribution to journal › Article › Scientific › peer-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 language	English
Article number	5848
Journal	Journal of Open Source Software
Volume	8
Issue number	92
DOIs	https://doi.org/10.21105/joss.05848
Publication status	Published - 15 Dec 2023
MoE publication type	A1 Journal article-refereed

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SCI_Jas_etal_JOSS_2023Final published version, 691 KBLicence: CC BY

HNN-core: A Python software for cellular and circuit-level interpretation of human MEG/EEG
Jas, M. (Creator), Thorpe, R. (Creator), Tolley, N. (Creator), Bailey, C. (Creator), Brandt, S. (Creator), Caldwell, B. (Creator), Cheng, H. (Creator), Daniels, D. (Creator), Pujol, C. F. (Creator), Khalil, M. (Creator), Kanekar, S. (Creator), Kolozsvári, O. (Creator), Lankinen, K. (Creator), Loi, K. (Creator), Neymotin, S. (Creator), Partani, R. (Creator), Pelah, M. (Creator), Rockhill, A. (Creator), Sherif, M. (Creator), Hamalainen, M. (Creator) & Jones, S. (Creator), Zenodo, 7 Dec 2023
DOI: 10.5281/zenodo.10288598, https://zenodo.org10289164
Dataset: Software or code

Cite this

Jas, M., Thorpe, R., Tolley, N., Bailey, C., Brandt, S., Caldwell, B., Cheng, H., Daniels, D., Pujol, C. F., Khalil, M., Kanekar, S., Kohl, C., Kolozsvári, O., Lankinen, K., Loi, K., Neymotin, S., Partani, R., Pelah, M., Rockhill, A., ... Jones, S. (2023). HNN-core: A Python software for cellular and circuit-level interpretation of human MEG/EEG. Journal of Open Source Software, 8(92), Article 5848. https://doi.org/10.21105/joss.05848

@article{bf29471e9c1b4c959cdd65d266b0ae51,

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

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. ",

author = "Mainak Jas and Ryan Thorpe and Nicholas Tolley and Christopher Bailey and Steven Brandt and Blake Caldwell and Huzi Cheng and Dylan Daniels and Pujol, \{Carolina Fernandez\} and Mostafa Khalil and Samika Kanekar and Carmen Kohl and Orsolya Kolozsv{\'a}ri and Kaisu Lankinen and Kenneth Loi and Sam Neymotin and Rajat Partani and Mattan Pelah and Alex Rockhill and Mohamed Sherif and Matti Hamalainen and Stephanie Jones",

year = "2023",

month = dec,

day = "15",

doi = "10.21105/joss.05848",

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volume = "8",

journal = "Journal of Open Source Software",

issn = "2475-9066",

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Jas, M, Thorpe, R, Tolley, N, Bailey, C, Brandt, S, Caldwell, B, Cheng, H, Daniels, D, Pujol, CF, Khalil, M, Kanekar, S, Kohl, C, Kolozsvári, O, Lankinen, K, Loi, K, Neymotin, S, Partani, R, Pelah, M, Rockhill, A, Sherif, M, Hamalainen, M & Jones, S 2023, 'HNN-core: A Python software for cellular and circuit-level interpretation of human MEG/EEG', Journal of Open Source Software, vol. 8, no. 92, 5848. https://doi.org/10.21105/joss.05848

TY - JOUR

T1 - HNN-core: A Python software for cellular and circuit-level interpretation of human MEG/EEG

AU - Jas, Mainak

AU - Thorpe, Ryan

AU - Tolley, Nicholas

AU - Bailey, Christopher

AU - Brandt, Steven

AU - Caldwell, Blake

AU - Cheng, Huzi

AU - Daniels, Dylan

AU - Pujol, Carolina Fernandez

AU - Khalil, Mostafa

AU - Kanekar, Samika

AU - Kohl, Carmen

AU - Kolozsvári, Orsolya

AU - Lankinen, Kaisu

AU - Loi, Kenneth

AU - Neymotin, Sam

AU - Partani, Rajat

AU - Pelah, Mattan

AU - Rockhill, Alex

AU - Sherif, Mohamed

AU - Hamalainen, Matti

AU - Jones, Stephanie

PY - 2023/12/15

Y1 - 2023/12/15

N2 - 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.

AB - 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.

U2 - 10.21105/joss.05848

DO - 10.21105/joss.05848

M3 - Article

SN - 2475-9066

VL - 8

JO - Journal of Open Source Software

JF - Journal of Open Source Software

IS - 92

M1 - 5848

ER -

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

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

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