Bio-communication of Plants

Witzany, Guenther

doi:10.1038/npre.2007.1429.1

Cited by 6 publications

(3 citation statements)

References 59 publications

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“…Biocommunication in ciliates [ 23 ] include intracellular signalling, chemotaxis as expression of communication, signals for vesicle trafficking, hormonal communication and pheromones. Plants communication processes are seen as primarily sign-mediated interactions and not simply an exchange of information [ 24 , 25 ]. Evidences of different kinds of chemical ‘words’ in plants are discussed in [ 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Language of fungi derived from their electrical spiking activity

Adamatzky

2022

R. Soc. open sci.

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Fungi exhibit oscillations of extracellular electrical potential recorded via differential electrodes inserted into a substrate colonized by mycelium or directly into sporocarps. We analysed electrical activity of ghost fungi ( Omphalotus nidiformis ), Enoki fungi ( Flammulina velutipes ), split gill fungi ( Schizophyllum commune ) and caterpillar fungi ( Cordyceps militaris ). The spiking characteristics are species specific: a spike duration varies from 1 to 21 h and an amplitude from 0.03 to 2.1 mV. We found that spikes are often clustered into trains. Assuming that spikes of electrical activity are used by fungi to communicate and process information in mycelium networks, we group spikes into words and provide a linguistic and information complexity analysis of the fungal spiking activity. We demonstrate that distributions of fungal word lengths match that of human languages. We also construct algorithmic and Liz-Zempel complexity hierarchies of fungal sentences and show that species S. commune generate the most complex sentences.

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Section: Introductionmentioning

confidence: 99%

Language of fungi derived from their electrical spiking activity

Adamatzky

2022

R. Soc. open sci.

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“…Начиная с бактерий и архей [58], все организмы без нервной системы способны познавать, то есть воспринимать окружающую их среду, обрабатывать информацию, обучаться, запоминать и взаимодействовать. Поскольку они являются природными организмами, способными обрабатывать информацию, некоторые из них, такие как слизевики, многоядерные или многоклеточные амебозои, использовались в качестве естественных вычислительных/ информационных процессоров, используемых для вычисления кратчайших путей.…”

Section: обучение в вычисляющей природеunclassified

Natural morphological computation as foundation of learning to learn in humans, other living organisms, and intelligent machines

Dodig-Crnković

2021

jour

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The emerging contemporary natural philosophy provides a common ground for the integrative view of the natural, the artificial, and the human-social knowledge and practices. Learning process is central for acquiring, maintaining, and managing knowledge, both theoretical and practical. This paper explores the relationships between the present advances in understanding of learning in the sciences of the artificial (deep learning, robotics), natural sciences (neuroscience, cognitive science, biology), and philosophy (philosophy of computing, philosophy of mind, natural philosophy). The question is, what at this stage of the development the inspiration from nature, specifically its computational models such as info-computation through morphological computing, can contribute to machine learning and artificial intelligence, and how much on the other hand models and experiments in machine learning and robotics can motivate, justify, and inform research in computational cognitive science, neurosciences, and computing nature. We propose that one contribution can be understanding of the mechanisms of ‘learning to learn’, as a step towards deep learning with symbolic layer of computation/information processing in a framework linking connectionism with symbolism. As all natural systems possessing intelligence are cognitive systems, we describe the evolutionary arguments for the necessity of learning to learn for a system to reach humanlevel intelligence through evolution and development. The paper thus presents a contribution to the epistemology of the contemporary philosophy of nature.

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“…As they are natural information processors, some such as slime mold, multinucleate, or multicellular Amoebozoa, has been used as natural computer/information processors to compute shortest paths. Even plants cognize, in spite of being often thought of as living systems without cognitive capacities [59]. Plants have been found to possess memory (in their bodily structures that change as a result of past events), the ability to learn (plasticity, ability to adapt through morphodynamics), and the capacity to anticipate and direct their behavior accordingly.…”

Section: Learning In the Computing Naturementioning

confidence: 99%

Natural Morphological Computation as Foundation of Learning to Learn in Humans, Other Living Organisms, and Intelligent Machines

Dodig-Crnković

2020

Philosophies

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The emerging contemporary natural philosophy provides a common ground for the integrative view of the natural, the artificial, and the human-social knowledge and practices. Learning process is central for acquiring, maintaining, and managing knowledge, both theoretical and practical. This paper explores the relationships between the present advances in understanding of learning in the sciences of the artificial (deep learning, robotics), natural sciences (neuroscience, cognitive science, biology), and philosophy (philosophy of computing, philosophy of mind, natural philosophy). The question is, what at this stage of the development the inspiration from nature, specifically its computational models such as info-computation through morphological computing, can contribute to machine learning and artificial intelligence, and how much on the other hand models and experiments in machine learning and robotics can motivate, justify, and inform research in computational cognitive science, neurosciences, and computing nature. We propose that one contribution can be understanding of the mechanisms of ‘learning to learn’, as a step towards deep learning with symbolic layer of computation/information processing in a framework linking connectionism with symbolism. As all natural systems possessing intelligence are cognitive systems, we describe the evolutionary arguments for the necessity of learning to learn for a system to reach human-level intelligence through evolution and development. The paper thus presents a contribution to the epistemology of the contemporary philosophy of nature.

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Bio-communication of Plants

Cited by 6 publications

References 59 publications

Language of fungi derived from their electrical spiking activity

Language of fungi derived from their electrical spiking activity

Natural morphological computation as foundation of learning to learn in humans, other living organisms, and intelligent machines

Natural Morphological Computation as Foundation of Learning to Learn in Humans, Other Living Organisms, and Intelligent Machines

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