A Comparative Study of Stochastic Optimizers for Fitting Neuron Models. Application to the Cerebellar Granule Cell Calvo Cruz, Nicolás Marín Alejo, Milagros López Redondo, Juana Martínez Ortigosa, Eva Martínez Ortigosa, Pilar Granule cell Neuron model Model tuning This research has been funded by the Human Brain Project Specific Grant Agreement 3 (H2020-RIA. 945539), the Spanish Ministry of Economy and Competitiveness (RTI2018-095993-B-I00), the National Grant INTSENSO (MICINN-FEDER-PID2019-109991GBI00), the Junta de Andalucía (FEDER-JA P18-FR-2378, P18-RT-1193), and the University of Almería (UAL18-TIC-A020-B). This work compares different algorithms to replace the genetic optimizer used in a recent methodology for creating realistic and computationally efficient neuron models. That method focuses on single-neuron processing and has been applied to cerebellar granule cells. It relies on the adaptive-exponential integrate-and-fire (AdEx) model, which must be adjusted with experimental data. The alternatives considered are: i) a memetic extension of the original genetic method, ii) Differential Evolution, iii) Teaching-Learning-Based Optimization, and iv) a local optimizer within a multi-start procedure. All of them ultimately outperform the original method, and the last two do it in all the scenarios considered. 2025-12-22T07:23:13Z 2025-12-22T07:23:13Z 2021-04-12 journal article Cruz, N. C., Marín, M., Redondo, J. L., Ortigosa, E. M., Ortigosa, P. M. A comparative study of stochastic optimizers for fitting neuron models. Application to the cerebellar granule cell. Informatica, 32(3), 477-498 (2021). https://doi.org/10.15388/21-INFOR450 0868-4952 1822-8844 https://hdl.handle.net/10481/109078 10.15388/21-INFOR450 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Vilnius University