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Emergence of biological complexity: Criticality, renewal and memory

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  • Grigolini, Paolo

Abstract

The key purpose of this article is to establish a connection between two emerging fields of research in theoretical biology. The former focuses on the concept of criticality borrowed from physics that is expected to be extensible to biology through a robust theoretical approach that although not yet available shall eventually shed light into the origin of cognition. The latter, largely based on the tracking of single molecules diffusing in biological cells, is bringing to the general attention the need to go beyond the ergodic assumption currently done in the traditional statistical physics. We show that replacing critical slowing down with temporal complexity explains why biological systems at criticality are resilient and why long-range correlations are compatible with the free-will condition necessary for the emergence of cognition. Temporal complexity generates ergodicity breakdown and requires new forms of response of complex systems to external stimuli. We concisely illustrate these new forms of information transport and we also address the challenging issue of combining temporal complexity with coherence and renewal with infinite memory.

Suggested Citation

  • Grigolini, Paolo, 2015. "Emergence of biological complexity: Criticality, renewal and memory," Chaos, Solitons & Fractals, Elsevier, vol. 81(PB), pages 575-588.
  • Handle: RePEc:eee:chsofr:v:81:y:2015:i:pb:p:575-588
    DOI: 10.1016/j.chaos.2015.07.025
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    References listed on IDEAS

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    1. Luković, Mirko & Vanni, Fabio & Svenkeson, Adam & Grigolini, Paolo, 2014. "Transmission of information at criticality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 430-438.
    2. Pensri Pramukkul & Adam Svenkeson & Paolo Grigolini & Mauro Bologna & Bruce West, 2013. "Complexity and the Fractional Calculus," Advances in Mathematical Physics, Hindawi, vol. 2013, pages 1-7, April.
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    Cited by:

    1. Raptis, Theophanes E., 2017. "“Viral” Turing Machines, computation from noise and combinatorial hierarchies," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 734-740.
    2. Carbone, Anna & Jensen, Meiko & Sato, Aki-Hiro, 2016. "Challenges in data science: a complex systems perspective," Chaos, Solitons & Fractals, Elsevier, vol. 90(C), pages 1-7.
    3. Scharf, Yael, 2017. "A chaotic outlook on biological systems," Chaos, Solitons & Fractals, Elsevier, vol. 95(C), pages 42-47.
    4. Aghababaei, Sajedeh & Balaraman, Sundarambal & Rajagopal, Karthikeyan & Parastesh, Fatemeh & Panahi, Shirin & Jafari, Sajad, 2021. "Effects of autapse on the chimera state in a Hindmarsh-Rose neuronal network," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    5. Tjeerd V olde Scheper, 2022. "Controlled bio-inspired self-organised criticality," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-19, January.
    6. Pease, April & Mahmoodi, Korosh & West, Bruce J., 2018. "Complexity measures of music," Chaos, Solitons & Fractals, Elsevier, vol. 108(C), pages 82-86.

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