This is an implementation of dynamical state and parameter estimation using the CasADi framework for setting up the sparse optimization problem. Particularly, we utilize a variational data assimilation techinque with a control term to "nudge" the data towards the observations.
The citing reference for this work is: Bano-Otalora, Moye, et al. eLife 2021;10:e68179. DOI: https://doi.org/10.7554/eLife.68179 .
The original reference for DSPE can be found here:
Abarbanel H.D.I., Creveling D.R., Farsian R., Kostuk M. Dynamical state and parameter estimation SIAM Journal of Applied Dynamical Systems, 8 (4) (2009), pp. 1341-1381 https://doi/10.1137/090749761
With additional material presented in : Toth, B.A., Kostuk, M., Meliza, C.D. et al. Dynamical estimation of neuron and network properties I: variational methods. Biol Cybern 105, 217–237 (2011). https://doi.org/10.1007/s00422-011-0459-1
This software, implemented in MATLAB, builds upon CasADi, which can be installed from https://web.casadi.org/get/ . No additional toolboxes are necessary, and all solutions are found using the default installed optimizer, MUMPS. For users with particularly large/ sparse problems, the suggested software repository, which utilizes a colpack driver for coloring the hessian, can be obtained from https://github.com/casadi/binaries/releases/tag/commit-09ad006. If the specific configuration is not available, please create an issue and I will try to contract the CasaADi devs.
Running the main_example_Rhabdomys.m script will produce figures and illustrate how to run the code for a specified model. More details are given in the rhabdomys_neuroDAguide.pdf document.