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  Emergence of behaviour in a self-organized living matter network

Fleig, P., Kramar, M., Wilczek, M., & Alim, K. (2022). Emergence of behaviour in a self-organized living matter network. eLife, 11: e62863. doi:10.1101/2020.09.06.285080.

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2020.09.06.285080v1.full.pdf (Preprint), 5MB
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2020.09.06.285080v1.full.pdf
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Downloaded on September 21, 2020 from https://www.biorxiv.org/content/10.1101/2020.09.06.285080v1.full.pdf
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Fleig, Philipp1, Author           
Kramar, Mirna1, Author           
Wilczek, Michael2, Author           
Alim, Karen1, Author           
Affiliations:
1Max Planck Research Group Biological Physics and Morphogenesis, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2266692              
2Max Planck Research Group Theory of Turbulent Flows, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2266693              

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 Abstract: What is the origin of behaviour? Although typically associated with a nervous system,
simple organisms also show complex behaviours. Among them, the slime mold Physarum polycephalum,
a giant single cell, is ideally suited to study emergence of behaviour. Here, we show how locomotion
and morphological adaptation behaviour emerge from self-organized
patterns of rhythmic
contractions of the actomyosin lining of the tubes making up the network-shaped
organism. We
quantify the spatio-temporal
contraction dynamics by decomposing experimentally recorded
contraction patterns into spatial contraction modes. Notably, we find a continuous spectrum of
modes, as opposed to a few dominant modes. Our data suggests that the continuous spectrum
of modes allows for dynamic transitions between a plethora of specific behaviours with transitions
marked by highly irregular contraction states. By mapping specific behaviours to states of active
contractions, we provide the basis to understand behaviour’s complexity as a function of biomechanical
dynamics.

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 Dates: 2020-09-082022-01-21
 Publication Status: Issued
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Title: eLife
Source Genre: Journal
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Publ. Info: Cambridge : eLife Sciences Publications
Pages: 25 Volume / Issue: 11 Sequence Number: e62863 Start / End Page: - Identifier: ISSN: 2050-084X
CoNE: https://pure.mpg.de/cone/journals/resource/2050-084X