Plant behaviour in response to the environment: information processing in the solid state

Duran-Nebreda, Salva; Bassel, George W.

doi:10.1098/rstb.2018.0370

Cited by 23 publications

(25 citation statements)

References 71 publications

(96 reference statements)

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“…The solid, aneural region of cognitive space is shared with other groups of living organisms with different organizations, life styles, and life cycles. Plants, in particular, define a limiting case [28,29]. The cognitive potential of plants was recognized as early as Darwin in a monograph [30], where he pointed to the interesting responses displayed by plants to external signals and environmental cues.…”

Section: Liquid or Solid Neurons Or No Neuronsmentioning

confidence: 99%

“…The cognitive potential of plants was recognized as early as Darwin in a monograph [30], where he pointed to the interesting responses displayed by plants to external signals and environmental cues. Plants exhibit responses that suggest interesting computational abilities [28], and the concept of 'plant intelligence' [31] has also been developed (with some degree of controversy) in recent decades. Communication at multiple scales, in particular, has Here there are no neural-like elements and yet in many ways these systems solve complex problems, exhibit learning and memory, and make decisions in response to enivornmental conditions.…”

Section: Liquid or Solid Neurons Or No Neuronsmentioning

confidence: 99%

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Liquid brains, solid brains

Solé

Moses

Forrest

2019

Phil. Trans. R. Soc. B

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Cognitive networks have evolved a broad range of solutions to the problem of gathering, storing and responding to information. Some of these networks are describable as static sets of neurons linked in an adaptive web of connections. These are ‘solid’ networks, with a well-defined and physically persistent architecture. Other systems are formed by sets of agents that exchange, store and process information but without persistent connections or move relative to each other in physical space. We refer to these networks that lack stable connections and static elements as ‘liquid’ brains, a category that includes ant and termite colonies, immune systems and some microbiomes and slime moulds. What are the key differences between solid and liquid brains, particularly in their cognitive potential, ability to solve particular problems and environments, and information-processing strategies? To answer this question requires a new, integrative framework. This article is part of the theme issue ‘Liquid brains, solid brains: How distributed cognitive architectures process information’.

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Section: Liquid or Solid Neurons Or No Neuronsmentioning

confidence: 99%

Section: Liquid or Solid Neurons Or No Neuronsmentioning

confidence: 99%

Liquid brains, solid brains

Solé

Moses

Forrest

2019

Phil. Trans. R. Soc. B

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“…Node centrality computation informs the algorithm about which nodes have a greater impact in the relaying of information within the network (see Methods). The physical limitation of information transfer and distances in a tissue is related to the feasibility of coordinating developmental transitions as well as coordinating responses to environmental changes or biological damage [6,7]. Node fusion reduces distances in the network and facilitates information transfer by creating shortcuts.…”

Section: A General Model To Simulate Vascular Development In Cell Conmentioning

confidence: 99%

“…However, increased size incurs additional challenges to the functioning of organs and organisms [5], such as the effective distribution of resources across the multicellular entity as well as coordination of biological action via information transfer [6]. These challenges include responses to changing environments and the timing of developmental transitions [7,8].…”

Section: Introductionmentioning

confidence: 99%

Efficient vasculature investment in tissues can be determined without global information

Duran-Nebreda

Johnston

Bassel

2020

J. R. Soc. Interface.

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Cells are the fundamental building blocks of organs and tissues. Information and mass flow through cellular contacts in these structures is vital for the orchestration of organ function. Constraints imposed by packing and cell immobility limit intercellular communication, particularly as organs and organisms scale up to greater sizes. In order to transcend transport limitations, delivery systems including vascular and respiratory systems evolved to facilitate the movement of matter and information. The construction of these delivery systems has an associated cost, as vascular elements do not perform the metabolic functions of the organs they are part of. This study investigates a fundamental trade-off in vascularization in multicellular tissues: the reduction of path lengths for communication versus the cost associated with producing vasculature. Biologically realistic generative models, using multicellular templates of different dimensionalities, revealed a limited advantage to the vascularization of two-dimensional tissues. Strikingly, scale-free improvements in transport efficiency can be achieved even in the absence of global knowledge of tissue organization. A point of diminishing returns in the investment of additional vascular tissue to the increased reduction of path length in 2.5- and three-dimensional tissues was identified. Applying this theory to experimentally determined biological tissue structures, we show the possibility of a co-dependency between the method used to limit path length and the organization of cells it acts upon. These results provide insight as to why tissues are or are not vascularized in nature, the robustness of developmental generative mechanisms and the extent to which vasculature is advantageous in the support of organ function.

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“…Cognition is possible in organisms without neurons [ 39 , 40 , 41 , 42 , 43 ]; however, nerve cells are the cornerstone of cognitive complexity. Within the previous framework we wonder how to elaborate a biologically grounded statistical physics of neural circuits ( Figure 1 c).…”

Section: Introductionmentioning

confidence: 99%

Fate of Duplicated Neural Structures

Seoane

2020

Entropy

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Statistical physics determines the abundance of different arrangements of matter depending on cost-benefit balances. Its formalism and phenomenology percolate throughout biological processes and set limits to effective computation. Under specific conditions, self-replicating and computationally complex patterns become favored, yielding life, cognition, and Darwinian evolution. Neurons and neural circuits sit at a crossroads between statistical physics, computation, and (through their role in cognition) natural selection. Can we establish a statistical physics of neural circuits? Such theory would tell what kinds of brains to expect under set energetic, evolutionary, and computational conditions. With this big picture in mind, we focus on the fate of duplicated neural circuits. We look at examples from central nervous systems, with stress on computational thresholds that might prompt this redundancy. We also study a naive cost-benefit balance for duplicated circuits implementing complex phenotypes. From this, we derive phase diagrams and (phase-like) transitions between single and duplicated circuits, which constrain evolutionary paths to complex cognition. Back to the big picture, similar phase diagrams and transitions might constrain I/O and internal connectivity patterns of neural circuits at large. The formalism of statistical physics seems to be a natural framework for this worthy line of research.

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Plant behaviour in response to the environment: information processing in the solid state

Cited by 23 publications

References 71 publications

Liquid brains, solid brains

Liquid brains, solid brains

Efficient vasculature investment in tissues can be determined without global information

Fate of Duplicated Neural Structures

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