Critical brain hypothesis explained

In neuroscience, the critical brain hypothesis states that certain biological neuronal networks work near phase transitions.[1] [2] [3] Experimental recordings from large groups of neurons have shown bursts of activity, so-called neuronal avalanches, with sizes that follow a power law distribution. These results, and subsequent replication on a number of settings, led to the hypothesis that the collective dynamics of large neuronal networks in the brain operates close to the critical point of a phase transition.[4] [5] According to this hypothesis, the activity of the brain would be continuously transitioning between two phases, one in which activity will rapidly reduce and die, and another where activity will build up and amplify over time.[4] In criticality, the brain capacity for information processing is enhanced,[6] [7] [8] so subcritical, critical and slightly supercritical branching process of thoughts could describe how human and animal minds function.

History

Discussion on the brain's criticality have been done since 1950, with the paper on the imitation game for a Turing test.[9] In 1995, Herz and Hopfield noted that self-organized criticality (SOC) models for earthquakes were mathematically equivalent to networks of integrate-and-fire neurons, and speculated that perhaps SOC would occur in the brain.[10] Simultaneously Stassinopoulos and Bak proposed a simple neural network model working at criticality[11] which was expanded later by Chialvo and Bak.[12] In 2003, the hypothesis found experimental support by Beggs and Plenz.[13] The critical brain hypothesis is not a consensus among the scientific community.[4] However, there exists more and more support for the hypothesis as more experimenters take to verifying the claims that it makes, particularly in vivo in rats with chronic electrophysiological recordings[14] and mice with high-density electrophysiological recordings.[15]

Notes and References

  1. Chialvo . D. R. . 2010 . Emergent complex neural dynamics . Nature Physics . 6 . 10 . 744–750 . 10.1038/nphys1803 . 2010NatPh...6..744C . 1010.2530 . 17584864 .
  2. Hesse . Janina . Gross . Thilo . Self-organized criticality as a fundamental property of neural systems . Frontiers in Systems Neuroscience . 8 . 2014-09-23 . 1662-5137 . 25294989 . 4171833 . 10.3389/fnsys.2014.00166 . free . 166.
  3. Chialvo. D. R.. Bak. P.. 1999-06-01. Learning from mistakes. Neuroscience. 90. 4. 1137–1148. 10.1016/S0306-4522(98)00472-2. adap-org/9707006. 10338284. 1304836 .
  4. 10.3389/fphys.2012.00163 . free . Being Critical of Criticality in the Brain . 2012 . Beggs . John M. . Timme . Nicholas . Frontiers in Physiology . 3 . 163 . 22701101 . 3369250 .
  5. di Santo . Serena . Villegas . Pablo . Burioni . Raffaella . Muñoz . Miguel A. . Landau–Ginzburg theory of cortex dynamics: Scale-free avalanches emerge at the edge of synchronization . Proceedings of the National Academy of Sciences . 13 February 2018 . 115 . 7 . E1356–E1365 . 10.1073/pnas.1712989115. 29378970 . 5816155 . 1801.10356 . 2018PNAS..115E1356D . free .
  6. Kinouchi . O. . Copelli . M. . 2006 . Optimal dynamical range of excitable networks at criticality . Nature Physics . 2 . 5 . 348–351 . 10.1038/nphys289 . q-bio/0601037 . 2006NatPh...2..348K . 9650581 .
  7. 10.1098/rsta.2007.2092 . The criticality hypothesis: How local cortical networks might optimize information processing . 2008 . Beggs . John M. . Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences . 366 . 1864 . 329–343 . 17673410 . 2008RSPTA.366..329B . 9790287 .
  8. Shew . W. L. . Yang . H. . Petermann . T. . Roy . R. . Plenz . D. . 2009 . Neuronal avalanches imply maximum dynamic range in cortical networks at criticality . The Journal of Neuroscience . 29 . 49 . 15595–15600 . 3862241 . 10.1523/jneurosci.3864-09.2009 . 20007483 . free .
  9. Turing . A. M. . 1950 . Computing machinery and intelligence . Mind . 59 . 236 . 433–460 . 10.1093/mind/lix.236.433 .
  10. Herz . A. V. . Hopfield . J. J. . 1995 . Earthquake cycles and neural reverberations: collective oscillations in systems with pulse-coupled threshold elements . Physical Review Letters . 75 . 6 . 1222–1225 . 10.1103/physrevlett.75.1222 . 10060236 . 1995PhRvL..75.1222H .
  11. Stassinopoulos. Dimitris. Bak. Per. 1995-05-01. Democratic reinforcement: A principle for brain function. Physical Review E. 51. 5. 5033–5039. 10.1103/PhysRevE.51.5033. 9963215 . 1995PhRvE..51.5033S.
  12. Chialvo. D.R.. Bak. P.. Learning from mistakes. Neuroscience. 90. 4. 1137–1148. 10.1016/s0306-4522(98)00472-2. adap-org/9707006. 1999. 10338284. 1304836 .
  13. Beggs . J. M. . Plenz . D. . 2003 . Neuronal avalanches in neocortical circuits . The Journal of Neuroscience . 23 . 35 . 11167–11177 . 10.1523/jneurosci.23-35-11167.2003 . free . 14657176 . 6741045 .
  14. Ma . Zhengyu . Turrigiano . Gina G. . Wessel . Ralf . Hengen . Keith B. . Cortical Circuit Dynamics Are Homeostatically Tuned to Criticality In Vivo . Neuron . Elsevier BV . 104 . 4 . 2019 . 0896-6273 . 10.1016/j.neuron.2019.08.031 . 655–664.e4 . 31601510. 6934140 .
  15. Smith . Wesley C. . In vivo Quantification of Neural Criticality and Complexity in Mouse Cortex and Striatum in a Model of Cocaine Abstinence . 2022-08-03 . 10.1101/2022.08.02.501652 .

This article is licensed under the GNU Free Documentation License. It uses material from the Wikipedia article "Critical brain hypothesis".

Except where otherwise indicated, Everything.Explained.Today is © Copyright 2009-2024, A B Cryer, All Rights Reserved. Cookie policy.