@misc{10481/109078,
year = {2021},
month = {4},
url = {https://hdl.handle.net/10481/109078},
abstract = {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.},
organization = {Human Brain Project Specific Grant Agreement 3 (H2020-RIA. 945539)},
organization = {Spanish Ministry of Economy and Competitiveness (RTI2018-095993-B-I00)},
organization = {National Grant INTSENSO (MICINN-FEDER-PID2019-109991GB-I00)},
organization = {Junta de Andalucía (FEDER-JA P18-FR-2378, P18-RT-1193)},
organization = {University of Almería (UAL18-TIC-A020-B)},
publisher = {Vilnius University},
keywords = {Granule cell},
keywords = {Neuron model},
keywords = {Model tuning},
title = {A Comparative Study of Stochastic Optimizers for Fitting Neuron Models. Application to the Cerebellar Granule Cell},
doi = {10.15388/21-INFOR450},
author = {Calvo Cruz, Nicolás and Marín Alejo, Milagros and López Redondo, Juana and Martínez Ortigosa, Eva and Martínez Ortigosa, Pilar},
}