diff --git a/paper.md b/paper.md index 206c58154..2a60c5d2e 100644 --- a/paper.md +++ b/paper.md @@ -125,7 +125,7 @@ bibliography: paper.bib # Summary -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 [@neymotin2020human], 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. 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. +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 [@neymotin2020human], 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. 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. HNN-core reproduces the workflows and tutorials provided in the original HNN software to generate commonly observed MEG/EEG signals including evoked response potentials (ERPs) and alpha (8-10 Hz), beta (15-30 Hz), and gamma (30-80 Hz) rhythms. HNN-core enables simultaneous calculation and visualization of macro- to micro-scale dynamics including MEG/EEG current dipoles, local field potential, laminar current-source density, and cell spiking and intrinsic dynamics. Importantly, HNN-core adopts modern open source development standards including a simplified installation procedure, unit tests, automatic documentation builds, code coverage, continuous integration, and contributing guidelines, supporting community development and long-term sustainability. @@ -155,11 +155,11 @@ HNN-core functionality supports advanced simulations through scripting that are HNN-core code has also enabled the creation of a new and improved web-based GUI based on ipywidgets [@ipywidgets2015] and voila [@voila2019] that can be run remotely with port forwarding. -All of the code associated with HNN-core has been extensively documented at multiple levels, including an API describing basic functions/parameters and examples of use for hypothesis generation and/or testing. Specifically, we distribute tutorials that mimic the original GUI tutorial workflows for simulating ERPs and low frequency rhythms using HNN-core functions, with commentary on the known biophysical mechanisms of these signals. We also provide short and targeted “How to” examples that describe how to use specific functionality, such as plotting firing rates, or recording extracellular LFPs. +All of the code associated with HNN-core has been extensively documented at multiple levels, including an API describing basic functions/parameters and examples of use for hypothesis generation and/or testing. Specifically, we distribute [tutorials](https://jonescompneurolab.github.io/hnn-core/stable/auto_examples/index.html) that mimic the original GUI tutorial workflows for simulating ERPs and low frequency rhythms using HNN-core functions, with commentary on the known biophysical mechanisms of these signals. The HNN-core tutorials include examples of how to study [alpha](https://jonescompneurolab.github.io/hnn-core/stable/auto_examples/workflows/plot_simulate_alpha.html#sphx-glr-auto-examples-workflows-plot-simulate-alpha-py) (8-10 Hz), [beta](https://jonescompneurolab.github.io/hnn-core/stable/auto_examples/workflows/plot_simulate_alpha.html#sphx-glr-auto-examples-workflows-plot-simulate-alpha-py) (15-30 Hz), and [gamma](https://jonescompneurolab.github.io/hnn-core/stable/auto_examples/workflows/plot_simulate_gamma.html#sphx-glr-auto-examples-workflows-plot-simulate-gamma-py) (30-0 Hz) brain rhythms. The tutorials also include an example of directly comparing simulations to real data, i.e. the [median nerve evoked response](https://jonescompneurolab.github.io/hnn-core/stable/auto_examples/workflows/plot_simulate_somato.html#sphx-glr-auto-examples-workflows-plot-simulate-somato-py). We also provide short and targeted “How to” examples that describe how to use specific functionality, such as plotting firing rates, or recording extracellular LFPs. # Use cases and quick example code of running a simulation -As summarized above, HNN-core reproduces the workflows and tutorials provided in the original GUI driven HNN software designed to investigate the origin of commonly observed MEG/EEG signals. The HNN-core [tutorials](https://jonescompneurolab.github.io/hnn-core/stable/auto_examples/index.html) include examples of how to study [alpha](https://jonescompneurolab.github.io/hnn-core/stable/auto_examples/workflows/plot_simulate_alpha.html#sphx-glr-auto-examples-workflows-plot-simulate-alpha-py) (8-10 Hz), [beta](https://jonescompneurolab.github.io/hnn-core/stable/auto_examples/workflows/plot_simulate_alpha.html#sphx-glr-auto-examples-workflows-plot-simulate-alpha-py) (15-30 Hz), and [gamma](https://jonescompneurolab.github.io/hnn-core/stable/auto_examples/workflows/plot_simulate_gamma.html#sphx-glr-auto-examples-workflows-plot-simulate-gamma-py) (30-0 Hz) brain rhythms. The tutorials also include an example of directly comparing simulations to real data, i.e. the [median nerve evoked responses](https://jonescompneurolab.github.io/hnn-core/stable/auto_examples/workflows/plot_simulate_somato.html#sphx-glr-auto-examples-workflows-plot-simulate-somato-py). +As summarized above, HNN-core reproduces the workflows and tutorials provided in the original GUI driven HNN software designed to investigate the origin of commonly observed MEG/EEG signals. In practice, users learn how to study the multi-scale origin of ERPs and low frequency oscillations by first following the tutorials in the HNN-GUI, and then recapitulating these tutorials in HNN-core. The tutorials provide an interactive investigation that gives intuition on how exogenous drives and other parameters in the model impact the outputs of the simulations. From there, users can test hypotheses about what parameters or sets of parameters need to be adjusted to account for their recorded data by directly comparing simulation output to data. Automated parameter inference can be performed to optimize parameters to produce a close fit (i.e., small root mean squared error) to current source ERPs, and more advanced parameter inference methods are in development.