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Ordering Dynamics in Neuron Activity Pattern Model: An Insight to Brain Functionality

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  • Jasleen Gundh
  • Awaneesh Singh
  • R K Brojen Singh

Abstract

We study the domain ordering kinetics in d = 2 ferromagnets which corresponds to populated neuron activities with both long-ranged interactions, V(r) ∼ r−n and short-ranged interactions. We present the results from comprehensive Monte Carlo (MC) simulations for the nonconserved Ising model with n ≥ 2, interaction range considering near and far neighbors. Our model results could represent the long-ranged neuron kinetics (n ≤ 4) in consistent with the same dynamical behaviour of short-ranged case (n ≥ 4) at far below and near criticality. We found that emergence of fast and slow kinetics of long and short ranged case could imitate the formation of connections among near and distant neurons. The calculated characteristic length scale in long-ranged interaction is found to be n independent (L(t) ∼ t1/(n−2)), whereas short-ranged interaction follows L(t) ∼ t1/2 law and approximately preserve universality in domain kinetics. Further, we did the comparative study of phase ordering near the critical temperature which follows different behaviours of domain ordering near and far critical temperature but follows universal scaling law.

Suggested Citation

  • Jasleen Gundh & Awaneesh Singh & R K Brojen Singh, 2015. "Ordering Dynamics in Neuron Activity Pattern Model: An Insight to Brain Functionality," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-16, October.
  • Handle: RePEc:plo:pone00:0141463
    DOI: 10.1371/journal.pone.0141463
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    References listed on IDEAS

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    1. Daniele Marinazzo & Mario Pellicoro & Guorong Wu & Leonardo Angelini & Jesús M Cortés & Sebastiano Stramaglia, 2014. "Information Transfer and Criticality in the Ising Model on the Human Connectome," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-7, April.
    2. Elad Schneidman & Michael J. Berry & Ronen Segev & William Bialek, 2006. "Weak pairwise correlations imply strongly correlated network states in a neural population," Nature, Nature, vol. 440(7087), pages 1007-1012, April.
    3. R. Chialvo, Dante, 2004. "Critical brain networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 340(4), pages 756-765.
    4. Christian Meisel & Alexander Storch & Susanne Hallmeyer-Elgner & Ed Bullmore & Thilo Gross, 2012. "Failure of Adaptive Self-Organized Criticality during Epileptic Seizure Attacks," PLOS Computational Biology, Public Library of Science, vol. 8(1), pages 1-8, January.
    5. Min Fu & Xinzhu Yu & Ju Lu & Yi Zuo, 2012. "Repetitive motor learning induces coordinated formation of clustered dendritic spines in vivo," Nature, Nature, vol. 483(7387), pages 92-95, March.
    6. Daniele Marinazzo & Guorong Wu & Mario Pellicoro & Leonardo Angelini & Sebastiano Stramaglia, 2012. "Information Flow in Networks and the Law of Diminishing Marginal Returns: Evidence from Modeling and Human Electroencephalographic Recordings," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-9, September.
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