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      Memory inception and preservation in slime moulds: the quest for a common mechanism

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          Abstract

          Learning and memory are indisputably key features of animal success. Using information about past experiences is critical for optimal decision-making in a fluctuating environment. Those abilities are usually believed to be limited to organisms with a nervous system, precluding their existence in non-neural organisms. However, recent studies showed that the slime mould Physarum polycephalum , despite being unicellular, displays habituation, a simple form of learning. In this paper, we studied the possible substrate of both short- and long-term habituation in slime moulds. We habituated slime moulds to sodium, a known repellent, using a 6 day training and turned them into a dormant state named sclerotia. Those slime moulds were then revived and tested for habituation. We showed that information acquired during the training was preserved through the dormant stage as slime moulds still showed habituation after a one-month dormancy period. Chemical analyses indicated a continuous uptake of sodium during the process of habituation and showed that sodium was retained throughout the dormant stage. Lastly, we showed that memory inception via constrained absorption of sodium for 2 h elicited habituation. Our results suggest that slime moulds absorbed the repellent and used it as a ‘circulating memory’. This article is part of the theme issue ‘Liquid brains, solid brains: How distributed cognitive architectures process information’.

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          Most cited references36

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          Rules for biologically inspired adaptive network design.

          Transport networks are ubiquitous in both social and biological systems. Robust network performance involves a complex trade-off involving cost, transport efficiency, and fault tolerance. Biological networks have been honed by many cycles of evolutionary selection pressure and are likely to yield reasonable solutions to such combinatorial optimization problems. Furthermore, they develop without centralized control and may represent a readily scalable solution for growing networks in general. We show that the slime mold Physarum polycephalum forms networks with comparable efficiency, fault tolerance, and cost to those of real-world infrastructure networks--in this case, the Tokyo rail system. The core mechanisms needed for adaptive network formation can be captured in a biologically inspired mathematical model that may be useful to guide network construction in other domains.
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            Plant ABC proteins--a unified nomenclature and updated inventory.

            The ABC superfamily comprises both membrane-bound transporters and soluble proteins involved in a broad range of processes, many of which are of considerable agricultural, biotechnological and medical potential. Completion of the Arabidopsis and rice genome sequences has revealed a particularly large and diverse complement of plant ABC proteins in comparison with other organisms. Forward and reverse genetics, together with heterologous expression, have uncovered many novel roles for plant ABC proteins, but this progress has been accompanied by a confusing proliferation of names for plant ABC genes and their products. A consolidated nomenclature will provide much-needed clarity and a framework for future research.
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              The immune system, adaptation, and machine learning

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                Author and article information

                Journal
                Philosophical Transactions of the Royal Society B: Biological Sciences
                Phil. Trans. R. Soc. B
                The Royal Society
                0962-8436
                1471-2970
                June 10 2019
                June 10 2019
                : 374
                : 1774
                : 20180368
                Affiliations
                [1 ]Research Centre on Animal Cognition (CRCA), Centre for Integrative Biology (CBI), Toulouse University, CNRS, UPS, Toulouse 31062, France
                Article
                10.1098/rstb.2018.0368
                6553583
                31006372
                15b1e109-8df8-40b3-8d13-27a0004c7752
                © 2019
                History

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