Please use this identifier to cite or link to this item: http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/4374
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dc.contributor.authorMi Kieu Trinhen_US
dc.contributor.authorWayland, Matthew T.en_US
dc.contributor.authorPRABAKARAN, SUDHAKARANen_US
dc.date.accessioned2020-01-28T03:46:13Z
dc.date.available2020-01-28T03:46:13Z
dc.date.issued2019-12en_US
dc.identifier.citationJournal of the Royal Society Interface, 16(161).en_US
dc.identifier.issn1742-5689en_US
dc.identifier.issn1742-5662en_US
dc.identifier.urihttp://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/4374-
dc.identifier.urihttps://doi.org/10.1098/rsif.2019.0410en_US
dc.description.abstractThere is still a significant gap between our understanding of neural circuits and the behaviours they compute-i.e. the computations performed by these neural networks (Carandini 2012 Nat. Neurosci. 15, 507-509. (doi:10.1038/nn.3043)). Cellular decision-making processes, learning, behaviour and memory formation-all that have been only associated with animals with neural systems-have also been observed in many unicellular aneural organisms, namely Physarum, Paramecium and Stentor (Tang & Marshall2018 Curr. Biol. 28, R1180-R1184. (doi:10.1016/j.cub.2018.09.015)). As these are fully functioning organisms, yet being unicellular, there is a much better chance to elucidate the detailed mechanisms underlying these learning processes in these organisms without the complications of highly interconnected neural circuits. An intriguing learning behaviour observed in Stentor roeseli (Jennings 1902 Am. J. Physiol. Legacy Content 8, 23-60. (doi:10.1152/ajplegacy.1902.8.1.23)) when stimulated with carmine has left scientists puzzled for more than a century. So far, none of the existing learning paradigm can fully encapsulate this particular series of five characteristic avoidance reactions. Although we were able to observe all responses described in the literature and in a previous study (Dexter et al. 2019), they do not conform to any particular learning model. We then investigated whether models inferred from machine learning approaches, including decision tree, random forest and feed-forward artificial neural networks could infer and predict the behaviour of S. roeseli. Our results showed that an artificial neural network with multiple 'computational' neurons is inefficient at modelling the single-celled ciliate's avoidance reactions. This has highlighted the complexity of behaviours in aneural organisms. Additionally, this report will also discuss the significance of elucidating molecular details underlying learning and decision-making processes in these unicellular organisms, which could offer valuable insights that are applicable to higher animals.en_US
dc.language.isoenen_US
dc.publisherThe Royal Societyen_US
dc.subjectMachine Learningen_US
dc.subjectAneural Organismsen_US
dc.subjectSingle-cell Behaviouren_US
dc.subjectTOC-JAN-2020en_US
dc.subject2019en_US
dc.titleBehavioural analysis of single-cell aneural ciliate, Stentor roeseli, using machine learning approachesen_US
dc.typeArticleen_US
dc.contributor.departmentDept. of Biologyen_US
dc.identifier.sourcetitleJournal of the Royal Society Interfaceen_US
dc.publication.originofpublisherForeignen_US
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