IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v587y2022ics0378437121007767.html
   My bibliography  Save this article

Critical behavior at the onset of synchronization in a neuronal model

Author

Listed:
  • Safaeesirat, Amin
  • Moghimi-Araghi, Saman

Abstract

It has been observed experimentally that the neural tissues generate highly variable and scale-free distributed outbursts of activity both in vivo and in vitro. Understanding whether these heterogeneous patterns of activity come from operation of the brain at the edge of a phase transition is an interesting possibility. Therefore, constructing a simple model that exhibits such behavior is of great interest. Additionally, the presence of both critical behavior and oscillatory patterns in brain dynamics is a very interesting phenomenon: Oscillatory patterns define a temporal scale, while criticality imposes scale-free characteristics. In this paper, we consider a model for a neuronal population where each neuron is modeled by an over-damped rotator. We find that there are some regions in the space of external parameters where the system shows synchronization. Interestingly, just at the transition point, the avalanche statistics show power-law behavior. Also, in the case of small systems, the (partial) synchronization and power-law behavior can occur simultaneously.

Suggested Citation

  • Safaeesirat, Amin & Moghimi-Araghi, Saman, 2022. "Critical behavior at the onset of synchronization in a neuronal model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    • Handle: RePEc:eee:phsmap:v:587:y:2022:i:c:s0378437121007767
    DOI: 10.1016/j.physa.2021.126503
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437121007767
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2021.126503?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Shree Hari Gautam & Thanh T Hoang & Kylie McClanahan & Stephen K Grady & Woodrow L Shew, 2015. "Maximizing Sensory Dynamic Range by Tuning the Cortical State to Criticality," PLOS Computational Biology, Public Library of Science, vol. 11(12), pages 1-15, December.
    2. Bruno Del Papa & Viola Priesemann & Jochen Triesch, 2017. "Criticality meets learning: Criticality signatures in a self-organizing recurrent neural network," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-21, May.
    3. K. Christensen & N. Farid & G. Pruessner & M. Stapleton, 2008. "On the scaling of probability density functions with apparent power-law exponents less than unity," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 62(3), pages 331-336, April.
    4. Marc Benayoun & Jack D Cowan & Wim van Drongelen & Edward Wallace, 2010. "Avalanches in a Stochastic Model of Spiking Neurons," PLOS Computational Biology, Public Library of Science, vol. 6(7), pages 1-13, July.
    5. Paolo Moretti & Miguel A. Muñoz, 2013. "Griffiths phases and the stretching of criticality in brain networks," Nature Communications, Nature, vol. 4(1), pages 1-10, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Forough Habibollahi & Brett J. Kagan & Anthony N. Burkitt & Chris French, 2023. "Critical dynamics arise during structured information presentation within embodied in vitro neuronal networks," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    2. Thomas F Varley & Olaf Sporns & Aina Puce & John Beggs, 2020. "Differential effects of propofol and ketamine on critical brain dynamics," PLOS Computational Biology, Public Library of Science, vol. 16(12), pages 1-29, December.
    3. Mikail Rubinov & Olaf Sporns & Jean-Philippe Thivierge & Michael Breakspear, 2011. "Neurobiologically Realistic Determinants of Self-Organized Criticality in Networks of Spiking Neurons," PLOS Computational Biology, Public Library of Science, vol. 7(6), pages 1-14, June.
    4. Marco Fuscà & Felix Siebenhühner & Sheng H. Wang & Vladislav Myrov & Gabriele Arnulfo & Lino Nobili & J. Matias Palva & Satu Palva, 2023. "Brain criticality predicts individual levels of inter-areal synchronization in human electrophysiological data," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    5. Arnaud Delorme & Jason Palmer & Julie Onton & Robert Oostenveld & Scott Makeig, 2012. "Independent EEG Sources Are Dipolar," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-14, February.
    6. Gerrit Großmann & Luca Bortolussi & Verena Wolf, 2020. "Efficient simulation of non-Markovian dynamics on complex networks," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-18, October.
    7. Garrett Jenkinson & John Goutsias, 2014. "Intrinsic Noise Induces Critical Behavior in Leaky Markovian Networks Leading to Avalanching," PLOS Computational Biology, Public Library of Science, vol. 10(1), pages 1-15, January.
    8. Andrea K Barreiro & Shree Hari Gautam & Woodrow L Shew & Cheng Ly, 2017. "A theoretical framework for analyzing coupled neuronal networks: Application to the olfactory system," PLOS Computational Biology, Public Library of Science, vol. 13(10), pages 1-37, October.
    9. Guido Gigante & Gustavo Deco & Shimon Marom & Paolo Del Giudice, 2015. "Network Events on Multiple Space and Time Scales in Cultured Neural Networks and in a Stochastic Rate Model," PLOS Computational Biology, Public Library of Science, vol. 11(11), pages 1-23, November.
    10. Wio, H.S. & Deza, J.I. & Sánchez, A.D. & García-García, R. & Gallego, R. & Revelli, J.A. & Deza, R.R., 2022. "The nonequilibrium potential today: A short review," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    11. Choi, Jaesung & Kim, Pilwon, 2020. "Reservoir computing based on quenched chaos," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    12. Martinez-Saito, Mario, 2022. "Discrete scaling and criticality in a chain of adaptive excitable integrators," Chaos, Solitons & Fractals, Elsevier, vol. 163(C).
    13. Bruno Del Papa & Viola Priesemann & Jochen Triesch, 2017. "Criticality meets learning: Criticality signatures in a self-organizing recurrent neural network," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-21, May.
    14. Paraskevov, A.V. & Minkin, A.S., 2022. "Damped oscillations of the probability of random events followed by absolute refractory period: exact analytical results," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    15. Konstantinos Sgantzos & Ian Grigg & Mohamed Al Hemairy, 2022. "Multiple Neighborhood Cellular Automata as a Mechanism for Creating an AGI on a Blockchain," JRFM, MDPI, vol. 15(8), pages 1-24, August.
    16. Corral, Álvaro, 2015. "Scaling in the timing of extreme events," Chaos, Solitons & Fractals, Elsevier, vol. 74(C), pages 99-112.
    17. Menesse, Gustavo & Marin, Bóris & Girardi-Schappo, Mauricio & Kinouchi, Osame, 2022. "Homeostatic criticality in neuronal networks," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    18. Brandon R. Munn & Eli J. Müller & Vicente Medel & Sharon L. Naismith & Joseph T. Lizier & Robert D. Sanders & James M. Shine, 2023. "Neuronal connected burst cascades bridge macroscale adaptive signatures across arousal states," Nature Communications, Nature, vol. 14(1), pages 1-17, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:587:y:2022:i:c:s0378437121007767. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.