期刊
INFORMATICA
卷 32, 期 3, 页码 477-498出版社
INST MATHEMATICS & INFORMATICS
DOI: 10.15388/21-INFOR450
关键词
granule cell; neuron model; model tuning; optimization; meta-heuristics
资金
- Human Brain Project Specific Grant Agreement 3 [H2020-RIA. 945539]
- Spanish Ministry of Economy and Competitiveness [RTI2018-095993-B-I00]
- National Grant INTSENSO [MICINN-FEDER-PID2019-109991GB-I00]
- Junta de Andalucia [FEDER-JA P18-FR-2378, P18-RT-1193]
- University of Almeria [UAL18-TIC-A020-B]
This study compared different algorithms to replace a genetic optimizer for creating realistic and computationally efficient neuron models, finding that all alternatives outperformed the original method, with the last two performing the best in all scenarios.
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.
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