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Reconsidering the evidence for learning in single cells

The question of whether single cells can learn led to much debate in the early 20th century. The view prevailed that they were capable of non-associative learning but not of associative learning, such as Pavlovian conditioning. Experiments indicating the contrary were considered either non-reproduci...

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Autores principales: Gershman, Samuel J, Balbi, Petra EM, Gallistel, C Randy, Gunawardena, Jeremy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7781593/
https://www.ncbi.nlm.nih.gov/pubmed/33395388
http://dx.doi.org/10.7554/eLife.61907
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author Gershman, Samuel J
Balbi, Petra EM
Gallistel, C Randy
Gunawardena, Jeremy
author_facet Gershman, Samuel J
Balbi, Petra EM
Gallistel, C Randy
Gunawardena, Jeremy
author_sort Gershman, Samuel J
collection PubMed
description The question of whether single cells can learn led to much debate in the early 20th century. The view prevailed that they were capable of non-associative learning but not of associative learning, such as Pavlovian conditioning. Experiments indicating the contrary were considered either non-reproducible or subject to more acceptable interpretations. Recent developments suggest that the time is right to reconsider this consensus. We exhume the experiments of Beatrice Gelber on Pavlovian conditioning in the ciliate Paramecium aurelia, and suggest that criticisms of her findings can now be reinterpreted. Gelber was a remarkable scientist whose absence from the historical record testifies to the prevailing orthodoxy that single cells cannot learn. Her work, and more recent studies, suggest that such learning may be evolutionarily more widespread and fundamental to life than previously thought and we discuss the implications for different aspects of biology.
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spelling pubmed-77815932021-01-06 Reconsidering the evidence for learning in single cells Gershman, Samuel J Balbi, Petra EM Gallistel, C Randy Gunawardena, Jeremy eLife Computational and Systems Biology The question of whether single cells can learn led to much debate in the early 20th century. The view prevailed that they were capable of non-associative learning but not of associative learning, such as Pavlovian conditioning. Experiments indicating the contrary were considered either non-reproducible or subject to more acceptable interpretations. Recent developments suggest that the time is right to reconsider this consensus. We exhume the experiments of Beatrice Gelber on Pavlovian conditioning in the ciliate Paramecium aurelia, and suggest that criticisms of her findings can now be reinterpreted. Gelber was a remarkable scientist whose absence from the historical record testifies to the prevailing orthodoxy that single cells cannot learn. Her work, and more recent studies, suggest that such learning may be evolutionarily more widespread and fundamental to life than previously thought and we discuss the implications for different aspects of biology. eLife Sciences Publications, Ltd 2021-01-04 /pmc/articles/PMC7781593/ /pubmed/33395388 http://dx.doi.org/10.7554/eLife.61907 Text en © 2021, Gershman et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Computational and Systems Biology
Gershman, Samuel J
Balbi, Petra EM
Gallistel, C Randy
Gunawardena, Jeremy
Reconsidering the evidence for learning in single cells
title Reconsidering the evidence for learning in single cells
title_full Reconsidering the evidence for learning in single cells
title_fullStr Reconsidering the evidence for learning in single cells
title_full_unstemmed Reconsidering the evidence for learning in single cells
title_short Reconsidering the evidence for learning in single cells
title_sort reconsidering the evidence for learning in single cells
topic Computational and Systems Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7781593/
https://www.ncbi.nlm.nih.gov/pubmed/33395388
http://dx.doi.org/10.7554/eLife.61907
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