From 694c1916ec60c31a60dd77676fa934a8dc8ad879 Mon Sep 17 00:00:00 2001 From: Nicholas Tolley Date: Tue, 21 Nov 2023 17:13:33 -0500 Subject: [PATCH] typo --- paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper.md b/paper.md index ffc2850cb..a77ee9978 100644 --- a/paper.md +++ b/paper.md @@ -131,7 +131,7 @@ HNN-core reproduces the workflows and tutorials provided in the original HNN sof # Statement of need -HNN-core addresses a key need in the fields of computational and experimental neuroscience by providing an extensively documented application programming interface (API) that allows both novel and advanced users to run biophysically-principled neural network simulations out-of-the-box with a few lines of code. HNN-core modularizes the model components originally introduced by HNN and its associated graphical user interaface (GUI) 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 addresses a key need in the fields of computational and experimental neuroscience by providing an extensively documented application programming interface (API) that allows both novel and advanced users to run biophysically-principled neural network simulations out-of-the-box with a few lines of code. HNN-core modularizes the model components originally introduced by HNN and its associated graphical user interface (GUI) 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