Hyphens and typos

paper.md

@@ -125,19 +125,19 @@ 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 rhythms (30-80 Hz). 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.
 
 # Statement of need
 
-The HNN GUI and its corresponding tutorials are beneficial for novice users to learn how to interact with the neocortical model to study the multiscale origin of the estimated sources of MEG/EEG signals. The interactive GUI allows users to quickly visualize how changes in parameters impact the simulated current dipole along with simultaneous changes in layer specific cell activity to test hypotheses on the mechanistic origins of recorded MEG/EEG waveforms. While the GUI is advantageous for learning how to study the multiscale origin of MEG/EEG sources, its functionality is limited as it only enables manipulation of a subset of GUI exposed parameters.
+The original HNN GUI and its corresponding tutorials are beneficial for novice users to learn how to interact with the neocortical model to study the multiscale origin of the estimated sources of MEG/EEG signals. The interactive GUI allows users to quickly visualize how changes in parameters impact the simulated current dipole along with simultaneous changes in layer-specific cell activity to test hypotheses on the mechanistic origins of recorded MEG/EEG waveforms.
 
-The original HNN software was designed monolithically with a Graphical User Interface (GUI), making expansion and maintenance difficult. HNN-core modularizes the model components and provides an interface to modify it directly from Python. This has allowed for significant expansion of the HNN functionality through scripting, including the ability to modify additional features of local network connectivity and cell properties, record voltages in extracellular arrays, and more advanced parameter optimization and batch processing. A new web-based GUI has been developed as a thin layer over the Python interface making the overall software more maintainable.
+While the GUI is advantageous for learning how to study the multiscale origin of MEG/EEG sources, its functionality is limited as it only enables manipulation of a subset of GUI-exposed parameters. Unfortunately the original HNN software was designed monolithically with a Graphical User Interface (GUI), making expansion and maintenance difficult. HNN-core modularizes the model components and provides an interface to modify it directly from Python. This has allowed for significant expansion of the HNN functionality through scripting, including the ability to modify additional features of local network connectivity and cell properties, record voltages in extracellular arrays, and more advanced parameter optimization and batch processing. A new web-based GUI has been developed as a thin layer over the Python interface making the overall software more maintainable.
 
 # HNN-core implements a biophysically detailed model to interpret MEG/EEG primary current sources
 
-MEG/EEG are the two electrophysiological methods to non-invasively study the human brain. They have been used in developing biomarkers for healthy and pathological brain processes. Yet, the underlying cellular and circuit level generators of MEG/EEG signals have been difficult to infer. This detailed understanding is critical to develop theories of information processing based on these signals, or to use these techniques to develop new therapeutics. Computational neural modeling is a powerful technique to hypothesize the neural origin of these signals and several modeling frameworks have been developed. Since MEG/EEG recordings are dominated by neocortical sources, all models developed so far simulate neocortical activity, but with different levels of biophysical details. One class of models known as neural mass models (NMMs) uses simplified representations to simulate net population dynamics, where hypothesized connectivity among neural “nodes” can be inferred from recordings. The Virtual Brain Project [@sanz2013virtual] and Dynamic Causal Modeling from the SPM software [@friston2003dynamic; @litvak2011eeg] are prominent examples of software that implement NMMs. While NMMs are computationally tractable and advantageous for studying brain-wide interactions, they do not provide detailed interpretation of cell and circuit level phenomena underlying MEG/EEG. The primary electrical currents that create MEG/EEG sensor signals are known to oriented along the long and spatially aligned cortical pyramidal neuron dendrites, and their direction corresponds to that of the intracellular current flow [@hamalainen1993magnetoencephalography]. For a detailed discussion see @neymotin2020human. Further, source localization methods such as minimum-norm estimate (MNE) calculate the primary currents (assuming constraints defined by the technique [@gramfort2013meg]). As such, models created to study the cell and circuit origin of these signals are designed with detailed pyramidal neuron morphology and physiology, and are often embedded in a full neocortical column model. HNN is one such detailed neocortical column model [@neymotin2020human], and other examples have been employed using the software LFPy [@linden2014lfpy]. A unique feature of HNN is its workflows for interacting with the template neocortical model through layer specific activations to study ERPs and low frequency brain rhythms. HNN also enables direct comparison between simulation output and the waveforms of estimated sources in the same units of measure and supports parameter inference. HNN-core was created to maintain all of the functionality of the original HNN software with additional utility (described below) and a well-defined, well-tested and documented application programming interface (API). Its adoption of open source development standards, including a simplified installation procedure, unit tests, automatic documentation builds, code coverage, and continuous integration, enables community development and long term sustainability.
+MEG/EEG are the two electrophysiological methods to non-invasively study the human brain. They have been used in developing biomarkers for healthy and pathological brain processes. Yet, the underlying cellular and circuit level generators of MEG/EEG signals have been difficult to infer. This detailed understanding is critical to develop theories of information processing based on these signals, or to use these techniques to develop new therapeutics. Computational neural modeling is a powerful technique to hypothesize the neural origin of these signals and several modeling frameworks have been developed. Since MEG/EEG recordings are dominated by neocortical sources, all models developed so far simulate neocortical activity, but with different levels of biophysical details. One class of models known as neural mass models (NMMs) uses simplified representations to simulate net population dynamics, where hypothesized connectivity among neural “nodes” can be inferred from recordings. The Virtual Brain Project [@sanz2013virtual] and Dynamic Causal Modeling from the SPM software [@friston2003dynamic; @litvak2011eeg] are prominent examples of software that implement NMMs. While NMMs are computationally tractable and advantageous for studying brain-wide interactions, they do not provide detailed interpretation of cell and circuit level phenomena underlying MEG/EEG. The primary electrical currents that create MEG/EEG sensor signals are known to be oriented along the long and spatially aligned cortical pyramidal neuron dendrites, and their direction corresponds to that of the intracellular current flow [@hamalainen1993magnetoencephalography]. For a detailed discussion see @neymotin2020human. Further, source localization methods such as minimum-norm estimate (MNE) calculate the primary currents (assuming constraints defined by the technique [@gramfort2013meg]). As such, models created to study the cell and circuit origin of these signals are designed with detailed pyramidal neuron morphology and physiology, and are often embedded in a full neocortical column model. HNN is one such detailed neocortical column model [@neymotin2020human], and other examples have been employed using the software LFPy [@linden2014lfpy]. A unique feature of HNN is its workflows for interacting with the template neocortical model through layer-specific activations to study ERPs and low frequency brain rhythms. HNN also enables direct comparison between simulation output and the waveforms of estimated sources in the same units of measure and supports parameter inference. HNN-core was created to maintain all of the functionality of the original HNN software with additional utility (described below) and a well-defined, well-tested and documented application programming interface (API). Its adoption of open source development standards, including a simplified installation procedure, unit tests, automatic documentation builds, code coverage, and continuous integration, enables community development and long-term sustainability.
 
 # HNN-core facilitates reproducibility and computationally expensive workflows
 
@@ -150,7 +150,7 @@ HNN-core code enables the creation of a new and improved web-based GUI based on
 - The ability to record extracellular local field potentials from user defined positions, as well as voltages and synaptic currents from any compartment in the model
 - The ability to modify all features of the morphology and biophysical properties of any cell in the network
 - An API that enables complete control of cell-cell and drive-cell connectivity in the network
-- An API that allows for flexibility in defining the exogenous layer specific drive to the neocortical network
+- An API that allows for flexibility in defining the exogenous layer-specific drive to the neocortical network
 - The ability to choose from multiple template models based on previous publications (e.g., `jones_2009_model()`{.python} [@jones2009quantitative], `law_2021_model()`{.python} [@law2022thalamocortical], and `calcium_model()`{.python} [@kohl2022neural])
 - Built-in ERP optimization functionality designed for faster convergence 
 - The choice of two parallel backends for either parallelizing across cells to speed up individual simulations (MPI), or across trials to speed up batches of simulations (Joblib)
@@ -179,11 +179,11 @@ dpl = simulate_dipole(net, tstop=100.0)
 
 
 ```
-![**Left**: Reduced schematic of HNN model detailing the cell types, layer specific synaptic connectivity structure, and locations of proximal drive synapses. The default size of the full network is a grid of 100 pyramidal neurons, and 35 inhibitory neurons, synaptically connected in each layer. Figure adapted from @neymotin2020human. **Right**: Plots of the network and simulated results can be generated using the HNN-core visualization API. The drive input histogram with `net.cell_response.plot_spikes_hist()`, the net current dipole with `plot_dipole(dpl)`, and the spike raster with `net.cell_response.plot_spikes_raster()`.\label{fig:fig1}](joss_figure.pdf)
+![**Left**: Reduced schematic of HNN model detailing the cell types, layer-specific synaptic connectivity structure, and locations of proximal drive synapses. The default size of the full network is a grid of 100 pyramidal neurons, and 35 inhibitory neurons, synaptically connected in each layer. Figure adapted from @neymotin2020human. **Right**: Plots of the network and simulated results can be generated using the HNN-core visualization API. The drive input histogram with `net.cell_response.plot_spikes_hist()`, the net current dipole with `plot_dipole(dpl)`, and the spike raster with `net.cell_response.plot_spikes_raster()`.\label{fig:fig1}](joss_figure.pdf)
 
 # Ongoing research using HNN-core
 
-The scripted interface of HNN-core has enabled the development of advanced parameter inference techniques [@tolley2023methods] using Simulation Based Inference [@tejero-cantero2020sbi]. It has been used in @thorpe2021distinct to propose new mechanisms of innocuous versus noxious sensory processing in the primary somatosensory neocortex. @Lankinen2023.06.16.545371 have used HNN-core to study crossmodal interactions between auditory and visual cortices. They performed group analysis on multiple subjects along with optimization and nonparametric statistical testing. Additionally, @szul2022diverse used it for understanding features of beta bursts in motor cortex and @fernandez2023laminar to study auditory perception.
+The scripted interface of HNN-core has enabled the development of advanced parameter inference techniques [@tolley2023methods] using Simulation-Based Inference [@tejero-cantero2020sbi]. It has been used in @thorpe2021distinct to propose new mechanisms of innocuous versus noxious sensory processing in the primary somatosensory neocortex. @Lankinen2023.06.16.545371 have used HNN-core to study crossmodal interactions between auditory and visual cortices. They performed group analysis on multiple subjects along with optimization and nonparametric statistical testing. Additionally, @szul2022diverse used it for understanding features of beta bursts in motor cortex and @fernandez2023laminar to study auditory perception.
 
 Overall, HNN-core provides an expandable and sustainable Python-based software package that can help advance understanding of the cellular and circuit mechanisms of MEG/EEG signal generation and ultimately lead to new neuroscience discoveries.