Behavioural analysis of single-cell aneural ciliate, Stentor roeseli, using machine learning approaches

Mi Kieu Trinh; Matthew T Wayland; Sudhakaran Prabakaran

doi:10.1098/rsif.2019.0410

Behavioural analysis of single-cell aneural ciliate, Stentor roeseli, using machine learning approaches

J R Soc Interface. 2019 Dec;16(161):20190410. doi: 10.1098/rsif.2019.0410. Epub 2019 Dec 4.

Authors

Mi Kieu Trinh^{1

2}, Matthew T Wayland³, Sudhakaran Prabakaran^{2

4

5}

Affiliations

¹ Trinity College, University of Cambridge, Cambridge CB2 1TQ, UK.
² Department of Genetics, University of Cambridge, Downing Site, Cambridge CB2 3EH, UK.
³ Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK.
⁴ Department of Biology, Indian Institute of Science Education and Research, Pune, Maharashtra 411008, India.
⁵ St Edmund's College, University of Cambridge, Cambridge CB3 0BN, UK.

Abstract

There 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 Content8, 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.

Keywords: aneural organisms; machine learning; single-cell behaviour.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Animals
Behavior, Animal*
Ciliophora / physiology*
Machine Learning*
Models, Biological
Nerve Net*
Video Recording