Linking single-cell decisions to collective behaviours in social bacteria

Dinet, Céline; Michelot, Alphée; Herrou, Julien; Mignot, Tâm

doi:10.1098/rstb.2019.0755

Cited by 12 publications

(9 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fundamental to these behaviours is a kind of collective motility called ‘swarming’ (S-motility), which involves periodic reversals in the direction of individual cells, whereby the leading pole of each cell becomes its lagging pole and vice versa , facilitated by the rhythmic intracellular migration of the protein complex driving the reversals. In their review, members of Tâm Mignot's [62] laboratory (CNRS-Aix-Marseille University, FR) describe in detail several swarming-enabled social transitions in M. xanthus , linking single-cell decisions to collective behaviours in this remarkable microbe. All of these multicellular behaviours require environmental sensing, signal integration, memory and decision-making, as well as solving problems faced by all flocking, shoaling and swarming animals, such as how to avoid interfering with another individual's progress [63].…”

Section: The Structure Of the Two Issuesmentioning

confidence: 99%

Reframing cognition: getting down to biological basics

Lyon

Keijzer

Arendt

et al. 2021

Phil. Trans. R. Soc. B

120

119

View full text Add to dashboard Cite

The premise of this two-part theme issue is simple: the cognitive sciences should join the rest of the life sciences in how they approach the quarry within their research domain. Specifically, understanding how organisms on the lower branches of the phylogenetic tree become familiar with, value and exploit elements of an ecological niche while avoiding harm can be expected to aid understanding of how organisms that evolved later (including Homo sapiens ) do the same or similar things. We call this approach basal cognition. In this introductory essay, we explain what the approach involves. Because no definition of cognition exists that reflects its biological basis, we advance a working definition that can be operationalized; introduce a behaviour-generating toolkit of capacities that comprise the function (e.g. sensing/perception, memory, valence, learning, decision making, communication), each element of which can be studied relatively independently; and identify a (necessarily incomplete) suite of common biophysical mechanisms found throughout the domains of life involved in implementing the toolkit. The articles in this collection illuminate different aspects of basal cognition across different forms of biological organization, from prokaryotes and single-celled eukaryotes—the focus of Part 1—to plants and finally to animals, without and with nervous systems, the focus of Part 2. By showcasing work in diverse, currently disconnected fields, we hope to sketch the outline of a new multidisciplinary approach for comprehending cognition, arguably the most fascinating and hard-to-fathom evolved function on this planet. Doing so has the potential to shed light on problems in a wide variety of research domains, including microbiology, immunology, zoology, biophysics, botany, developmental biology, neurobiology/science, regenerative medicine, computational biology, artificial life and synthetic bioengineering. This article is part of the theme issue ‘Basal cognition: conceptual tools and the view from the single cell’.

show abstract

Section: The Structure Of the Two Issuesmentioning

confidence: 99%

Reframing cognition: getting down to biological basics

Lyon

Keijzer

Arendt

et al. 2021

Phil. Trans. R. Soc. B

120

119

View full text Add to dashboard Cite

show abstract

“…The truly innovative move in the literature on basal cognition (that is, cognition as situated in more “primitive” organisms and cellular activities), then, is the explicit recognition of the cognitive (or proto-cognitive; Godfrey-Smith, 2016a , b , 2017 ) nature of the activities identified above. Indeed, examples of memory in social bacteria (Dinet et al, 2021 ), learning in unicellulars and protists (Gershman et al, 2021 ), decision-making in acellular and cellular slime moulds (Arias Del Angel et al, 2020 ; Smith-Ferguson and Beekman, 2020 ; Boussard et al, 2021 ) have all been identified in non-neural organisms, and it is known that constituent cells in metazoan swarms (i.e., multicellular animals) actively and adaptively manage their morphology, behaviour, and physiology as needed for survival. Again, this is cognition within and throughout biological bodies and therefore is suggestive of a more thoroughly embodied cognition insofar as higher-order organized wholes are dependent on constituent units and parts maintaining, in certain crucial respects, cognitive capacities of far more ancient origins.…”

Section: How Fine-grained Functional Details Matter To Cognitionmentioning

confidence: 99%

Mind the matter: Active matter, soft robotics, and the making of bio-inspired artificial intelligence

2022

View full text Add to dashboard Cite

Philosophical and theoretical debates on the multiple realisability of the cognitive have historically influenced discussions of the possible systems capable of instantiating complex functions like memory, learning, goal-directedness, and decision-making. These debates have had the corollary of undermining, if not altogether neglecting, the materiality and corporeality of cognition—treating material, living processes as “hardware” problems that can be abstracted out and, in principle, implemented in a variety of materials—in particular on digital computers and in the form of state-of-the-art neural networks. In sum, the matter in se has been taken not to matter for cognition. However, in this paper, we argue that the materiality of cognition—and the living, self-organizing processes that it enables—requires a more detailed assessment when understanding the nature of cognition and recreating it in the field of embodied robotics. Or, in slogan form, that the matter matters for cognitive form and function. We pull from the fields of Active Matter Physics, Soft Robotics, and Basal Cognition literature to suggest that the imbrication between material and cognitive processes is closer than standard accounts of multiple realisability suggest. In light of this, we propose upgrading the notion of multiple realisability from the standard version—what we call 1.0—to a more nuanced conception 2.0 to better reflect the recent empirical advancements, while at the same time averting many of the problems that have been raised for it. These fields are actively reshaping the terrain in which we understand materiality and how it enables, mediates, and constrains cognition. We propose that taking the materiality of our embodied, precarious nature seriously furnishes an important research avenue for the development of embodied robots that autonomously value, engage, and interact with the environment in a goal-directed manner, in response to existential needs of survival, persistence, and, ultimately, reproduction. Thus, we argue that by placing further emphasis on the soft, active, and plastic nature of the materials that constitute cognitive embodiment, we can move further in the direction of autonomous embodied robots and Artificial Intelligence.

show abstract

“…Collective behaviour is one of the most enigmatic phenomena of contemporary times [1]. It is a genuinely omnipresent phenomenon-from the flocking of birds and the growth of bacterial colonies [2,3] to the stock market trading and hierarchy of school classes. We intuitively feel that something should be familiar to all these phenomena because the observed patterns of agents' behaviour, such as leader, follower, mediator, or crowd, sound applicable to all these systems.…”

Section: Introductionmentioning

confidence: 99%

Quantification of collective behaviour via causality analysis

Lonhus

Rychtáriková

Štys

2023

Complex Intell. Syst.

View full text Add to dashboard Cite

Terms such as leader, mediator, and follower sound equal in the description of a pack of wolves, a street protest crowd, or a business team and have very similar meanings. This indicates the presence of some general law or structure that governs collective behaviour. To reveal this, we selected the most common parameter for most levels of the organisation—motion. A causality analysis of distance correlations was performed to obtain follow-up networks that show who follows whom and how strongly. These networks characterise an observed system in general and work as an automation bridge between the biological experiment and the broad field of network analysis. The proposed method was tested on 3D image data from a controlled experiment on a 6-member school of aquarium fish of Tiger Barb. The network patterns can be easily ethologically interpreted and agreed with expected behaviour.

show abstract

Linking single-cell decisions to collective behaviours in social bacteria

Cited by 12 publications

References 61 publications

Reframing cognition: getting down to biological basics

Reframing cognition: getting down to biological basics

Mind the matter: Active matter, soft robotics, and the making of bio-inspired artificial intelligence

Quantification of collective behaviour via causality analysis

Contact Info

Product

Resources

About