Parameter estimation in a whole-brain network model of epilepsy: comparison of parallel global optimization solvers

Code and results from the study:

Penas, David R.(*), Meysam Hashemi(*), Viktor K. Jirsa and Julio R. Banga (2023) Parameter estimation in a whole-brain network model of epilepsy: comparison of parallel global optimization solvers. bioRxiv 2023.11.01.565083. https://doi.org/10.1101/2023.11.01.565083

Latest (journal) version:

Penas, D.R.(*), M. Hashemi(*), V.K. Jirsa, J.R. Banga (2024) Parameter estimation in a whole-brain network model of epilepsy: comparison of parallel global optimization solvers. PLOS Computational Biology  20(7): e1011642.  https://doi.org/10.1371/journal.pcbi.1011642

(*) these authors contributed equally to this work

Emails: meysam.HASHEMI@univ-amu.fr, david.rodriguez.penas@csic.es, viktor.jirsa@univ-amu.fr, j.r.banga@csic.es

The code included here is an snapshot (30-Oct-2023) of the Optimization folder in:

https://github.com/ins-amu/BVEP

Instructions to solve the problems considered in the paper:

/Optimization/sacess_VEP_models/README.md

Funding:

The authors acknowledge CESGA (Centro de Supercomputación de Galicia) for providing access to its FinisTerrae III supercomputer.  

J.R.B. and D.R.P. acknowledge financial support from grant PID2020-117271RB-C22  (BIODYNAMICS) funded by MCIN/AEI/10.13039/501100011033.  

M.H and V.J acknowledge the funding by the French National Research Agency (ANR) as part of the second "Investissements d'Avenir" program, ANR-17-RHUS-0004, EPINOV (https://anr.fr), and Human Brain Project SGA3 (https://ec.europa.eu/programmes/horizon2020).