HNN-core: A Python software for cellular and circuit-level interpretation of human MEG/EEG.
Mainak JasRyan ThorpeNicholas M TolleyChristopher BaileySteven BrandtBlake CaldwellHuzi ChengDylan DanielsCarolina Fernandez PujolMostafa KhalilSamika KanekarCarmen KohlOrsolya B KolozsváriKaisu LankinenKenneth LoiSamuel A NeymotinRajat PartaniMattan PelahAlexander P RockhillMohamed SherifMatti S HämäläinenStephanie R JonesPublished in: Journal of open source software (2023)
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.
Keyphrases
- endothelial cells
- induced pluripotent stem cells
- neural network
- resting state
- pluripotent stem cells
- functional connectivity
- induced apoptosis
- spinal cord
- electronic health record
- oxidative stress
- cell proliferation
- signaling pathway
- mass spectrometry
- cell death
- simultaneous determination
- data analysis
- spinal cord injury
- big data
- reactive oxygen species