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Brain-inspired classical conditioning model

Classical conditioning plays a critical role in the learning process of biological brains, and many computational models have been built to reproduce the related classical experiments. However, these models can reproduce and explain only a limited range of typical phenomena in classical conditioning...

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Detalles Bibliográficos
Autores principales: Zhao, Yuxuan, Zeng, Yi, Qiao, Guang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7808924/
https://www.ncbi.nlm.nih.gov/pubmed/33490893
http://dx.doi.org/10.1016/j.isci.2020.101980
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author Zhao, Yuxuan
Zeng, Yi
Qiao, Guang
author_facet Zhao, Yuxuan
Zeng, Yi
Qiao, Guang
author_sort Zhao, Yuxuan
collection PubMed
description Classical conditioning plays a critical role in the learning process of biological brains, and many computational models have been built to reproduce the related classical experiments. However, these models can reproduce and explain only a limited range of typical phenomena in classical conditioning. Based on existing biological findings concerning classical conditioning, we build a brain-inspired classical conditioning (BICC) model. Compared with other computational models, our BICC model can reproduce as many as 15 classical experiments, explaining a broader set of findings than other models have, and offers better computational explainability for both the experimental phenomena and the biological mechanisms of classical conditioning. Finally, we validate our theoretical model on a humanoid robot in three classical conditioning experiments (acquisition, extinction, and reacquisition) and a speed generalization experiment, and the results show that our model is computationally feasible as a foundation for brain-inspired robot classical conditioning.
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spelling pubmed-78089242021-01-22 Brain-inspired classical conditioning model Zhao, Yuxuan Zeng, Yi Qiao, Guang iScience Article Classical conditioning plays a critical role in the learning process of biological brains, and many computational models have been built to reproduce the related classical experiments. However, these models can reproduce and explain only a limited range of typical phenomena in classical conditioning. Based on existing biological findings concerning classical conditioning, we build a brain-inspired classical conditioning (BICC) model. Compared with other computational models, our BICC model can reproduce as many as 15 classical experiments, explaining a broader set of findings than other models have, and offers better computational explainability for both the experimental phenomena and the biological mechanisms of classical conditioning. Finally, we validate our theoretical model on a humanoid robot in three classical conditioning experiments (acquisition, extinction, and reacquisition) and a speed generalization experiment, and the results show that our model is computationally feasible as a foundation for brain-inspired robot classical conditioning. Elsevier 2020-12-25 /pmc/articles/PMC7808924/ /pubmed/33490893 http://dx.doi.org/10.1016/j.isci.2020.101980 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Zhao, Yuxuan
Zeng, Yi
Qiao, Guang
Brain-inspired classical conditioning model
title Brain-inspired classical conditioning model
title_full Brain-inspired classical conditioning model
title_fullStr Brain-inspired classical conditioning model
title_full_unstemmed Brain-inspired classical conditioning model
title_short Brain-inspired classical conditioning model
title_sort brain-inspired classical conditioning model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7808924/
https://www.ncbi.nlm.nih.gov/pubmed/33490893
http://dx.doi.org/10.1016/j.isci.2020.101980
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