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240628s2023 xx |||||o 00| ||eng c |
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|a 10.21105/joss.05848
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|a (PII)5848
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|a DE-627
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|a eng
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|a Jas, Mainak
|e verfasserin
|4 aut
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|a HNN-core
|b A Python software for cellular and circuit-level interpretation of human MEG/EEG
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|c 2023
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|a Text
|b txt
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Revised 01.10.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a 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
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|a Journal Article
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|a Thorpe, Ryan
|e verfasserin
|4 aut
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|a Tolley, Nicholas
|e verfasserin
|4 aut
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|a Bailey, Christopher
|e verfasserin
|4 aut
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|a Brandt, Steven
|e verfasserin
|4 aut
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|a Caldwell, Blake
|e verfasserin
|4 aut
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|a Cheng, Huzi
|e verfasserin
|4 aut
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|a Daniels, Dylan
|e verfasserin
|4 aut
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|a Pujol, Carolina Fernandez
|e verfasserin
|4 aut
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|a Khalil, Mostafa
|e verfasserin
|4 aut
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|a Kanekar, Samika
|e verfasserin
|4 aut
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|a Kohl, Carmen
|e verfasserin
|4 aut
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|a Kolozsvári, Orsolya
|e verfasserin
|4 aut
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|a Lankinen, Kaisu
|e verfasserin
|4 aut
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|a Loi, Kenneth
|e verfasserin
|4 aut
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|a Neymotin, Sam
|e verfasserin
|4 aut
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|a Partani, Rajat
|e verfasserin
|4 aut
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|a Pelah, Mattan
|e verfasserin
|4 aut
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|a Rockhill, Alex
|e verfasserin
|4 aut
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|a Sherif, Mohamed
|e verfasserin
|4 aut
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|a Hamalainen, Matti
|e verfasserin
|4 aut
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|a Jones, Stephanie
|e verfasserin
|4 aut
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|i Enthalten in
|t Journal of open source software
|d 2017
|g 8(2023), 92 vom: 01.
|w (DE-627)NLM275088057
|x 2475-9066
|7 nnns
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|g volume:8
|g year:2023
|g number:92
|g day:01
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|u http://dx.doi.org/10.21105/joss.05848
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