Uni-cellular integration of complex spatial information in slime moulds and ciliates

Schenz, Daniel; Nishigami, Yukinori; Sato, Kazushi; Nakagaki, Toshiyuki

doi:10.1016/j.gde.2019.06.012

Cited by 9 publications

(5 citation statements)

References 48 publications

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“…Although slime moulds lack the complex dedicated neural hardware of neurozoans, the environment they evolved in is no less complex and they face the same challenges: they must search for and choose between resources, adapt to changing conditions, avoid dangers and find suitable microclimates to inhabit. Physarum polycephalum demonstrates key aspects of complex decision-making [24–27,47,48] (figure 1). It can find its way in a maze [49], construct efficient transport networks [50], distinguish how different masses distort the substrate [46], avoid obstacles [51] and risky environments [52], optimize its nutrient intake [53,54], and use conspecifics' cues to choose what substrate to exploit [55,56].…”

Section: Cognitive Abilities Of Physarum Polycephalummentioning

confidence: 99%

Adaptive behaviour and learning in slime moulds: the role of oscillations

Boussard

Fessel

Oettmeier

et al. 2021

Phil. Trans. R. Soc. B

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The slime mould Physarum polycephalum , an aneural organism, uses information from previous experiences to adjust its behaviour, but the mechanisms by which this is accomplished remain unknown. This article examines the possible role of oscillations in learning and memory in slime moulds. Slime moulds share surprising similarities with the network of synaptic connections in animal brains. First, their topology derives from a network of interconnected, vein-like tubes in which signalling molecules are transported. Second, network motility, which generates slime mould behaviour, is driven by distinct oscillations that organize into spatio-temporal wave patterns. Likewise, neural activity in the brain is organized in a variety of oscillations characterized by different frequencies. Interestingly, the oscillating networks of slime moulds are not precursors of nervous systems but, rather, an alternative architecture. Here, we argue that comparable information-processing operations can be realized on different architectures sharing similar oscillatory properties. After describing learning abilities and oscillatory activities of P. polycephalum , we explore the relation between network oscillations and learning, and evaluate the organism's global architecture with respect to information-processing potential. We hypothesize that, as in the brain, modulation of spontaneous oscillations may sustain learning in slime mould. This article is part of the theme issue ‘Basal cognition: conceptual tools and the view from the single cell’.

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Section: Cognitive Abilities Of Physarum Polycephalummentioning

confidence: 99%

Adaptive behaviour and learning in slime moulds: the role of oscillations

Boussard

Fessel

Oettmeier

et al. 2021

Phil. Trans. R. Soc. B

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“…Physarum polycephalum is well known for its ability to solve a large variety of problems [4,[29][30][31][32]. It can find its way in a maze [33], avoid obstacles [27] and risky environments [34], build efficient networks [35], optimize its nutrient intake [34,36], learn to ignore repellents [37] and transfer learned information to clone mates [38].…”

Section: Introductionmentioning

confidence: 99%

Stress signalling in acellular slime moulds and its detection by conspecifics

Briard

Goujarde

Bousquet

et al. 2020

Phil. Trans. R. Soc. B

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Unicellular organisms live in unpredictable environments. Therefore, they need to continuously assess environmental conditions and respond appropriately to survive and thrive. When subjected to rapid changes in their environment or to cellular damages, unicellular organisms such as bacteria exhibit strong physiological reactions called stress responses that can be sensed by conspecifics. The ability to detect and use stress-related cues released by conspecifics to acquire information about the environment constitutes an adaptive survival response by prompting the organism to avoid potential dangers. Here, we investigate stress signalling and its detection by conspecifics in a unicellular organism, Physarum polycephalum . Slime moulds were subjected to either biotic (i.e. nutritional) or abiotic (i.e. chemical and light) stressors or left undisturbed while they were exploring a homogeneous environment. Then, we observed the responses of slime moulds facing a choice between cues released by stressed clone mates and cues released by undisturbed ones. We found that slime moulds actively avoided environments previously explored by stressed clone mates. These results suggest that slime moulds, like bacteria or social amoeba, exhibit physiological responses to biotic and abiotic stresses that can be sensed by conspecifics. Our results establish slime moulds as a promising new model to investigate the use of social information in unicellular organisms. This article is part of the theme issue ‘Signal detection theory in recognition systems: from evolving models to experimental tests’.

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“…Stentor is not the only example of a unicellular organism displaying learning behaviours; many more aneural organisms [5,7,[18][19][20] have been extensively studied, leading to surprising results. For example, the slime mould, Physarum, has also taught us immensely on the ability of single-cell organism to exhibit learning [21,22]. Even the pathways regulating Escherichia coli's chemotaxis behaviour-possibly the most well-characterized pathway in biology to datehas taught us many critical lessons that can be applied to higher organisms, including humans, which also helped to develop general biological principles.…”

Section: Modelling Stentor Roeseli's Avoidance Behaviour Using Machine Learning Approachesmentioning

confidence: 99%

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

Trinh

Wayland

Prabakaran

2019

J. R. Soc. Interface.

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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 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.

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Uni-cellular integration of complex spatial information in slime moulds and ciliates

Cited by 9 publications

References 48 publications

Adaptive behaviour and learning in slime moulds: the role of oscillations

Adaptive behaviour and learning in slime moulds: the role of oscillations

Stress signalling in acellular slime moulds and its detection by conspecifics

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

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