Generalized Activating Function manuscript companion: figure data (tabular), input datasets, and Python scripts for figure generation.

This companion dataset contains an archive of figure data and scripts for the scientific manuscript: "A Generalized Activating Function for Rapid and Accurate Neural Response Prediction in Spinal Cord Stimulation". The Generalized Activating Function (GAF) is a novel biophysics-grounded framework that accelerates neural activation predictions by up to three orders of magnitude relative to NEURON simulations of multi-compartment cable models, while maintaining high predictive accuracy. The method is an extension of Rattay's original "activating function" concept (second spatial derivative of the extracellular potential along the orientation of a fiber), but convolved with a diffusive Green's function for enhanced accuracy. The dataset contains a directory for each figure in the manuscript. These directories contain: 1) the original plot data in numerical/tabular form (sourcedata), 2) raw input data needed to regenerated each panel (inputs/), 3) Python scripts for regenerating each panel from the provided inputs, 3) an environment.yml file, 4) output pdfs for each figure (or panel), and 5) README/manifest files documenting the directory structure, usuage, and file content. See READMEs for additional information and/or clarification.     Manuscript authors:Taylor H. Newton 1,2*#Javier García Ordóñez 1,3*#Abdallah Alashqar 4,5Vincent Gemar 4,5Andreas Rowald 4,5Jocelyne Bloch 6,7,8,9,10Gregoire Courtine 6,7,8,9Niels Kuster 1,2Esra Neufeld 11 Foundation for Research on Information Technologies in Society (IT'IS), Zurich43, 8004 Zürich, Switzerland2 Department of Information Technology and Electrical Engineering, Swiss Federal Institute of Technology (ETHZ), 8092 Zürich, Switzerland3 ZMT Zurich MedTech AG, Zurich43, 8004 Zürich, Switzerland4 Department of Medical Informatics, Biometry and Epidemiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany5 Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany6 NeuroRestore, Swiss Federal Institute of Technology (EPFL), Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland7 Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland8 Department of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland9 Department of Neurosurgery, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland10 Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland# These authors contributed equally to this work.* Corresponding authors: [e-mail]; [e-mail]