Critical brain hypothesis

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,[4][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]

References
  1. Chialvo, D. R. (2010). "Emergent complex neural dynamics". Nature Physics. 6 (10): 744–750. arXiv:1010.2530. Bibcode:2010NatPh...6..744C. doi:10.1038/nphys1803. S2CID 17584864.
  2. Hesse, J. & Gross, T. Self-organized criticality as a fundamental property of neural systems. Criticality as a signature of healthy neural systems: multi-scale experimental and computational studies (2015)
  3. Chialvo, D. R.; Bak, P. (1999-06-01). "Learning from mistakes". Neuroscience. 90 (4): 1137–1148. arXiv:adap-org/9707006. doi:10.1016/S0306-4522(98)00472-2. PMID 10338284. S2CID 1304836.
  4. Beggs, John M.; Timme, Nicholas (2012). "Being Critical of Criticality in the Brain". Frontiers in Physiology. 3: 163. doi:10.3389/fphys.2012.00163. PMC 3369250. PMID 22701101.
  5. di Santo, Serena; Villegas, Pablo; Burioni, Raffaella; Muñoz, Miguel A. (13 February 2018). "Landau–Ginzburg theory of cortex dynamics: Scale-free avalanches emerge at the edge of synchronization". Proceedings of the National Academy of Sciences. 115 (7): E1356–E1365. arXiv:1801.10356. Bibcode:2018PNAS..115E1356D. doi:10.1073/pnas.1712989115. PMC 5816155. PMID 29378970.
  6. Kinouchi, O.; Copelli, M. (2006). "Optimal dynamical range of excitable networks at criticality". Nature Physics. 2 (5): 348–351. arXiv:q-bio/0601037. Bibcode:2006NatPh...2..348K. doi:10.1038/nphys289. S2CID 9650581.
  7. Beggs, John M. (2008). "The criticality hypothesis: How local cortical networks might optimize information processing". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 366 (1864): 329–343. Bibcode:2008RSPTA.366..329B. doi:10.1098/rsta.2007.2092. PMID 17673410. S2CID 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. doi:10.1523/jneurosci.3864-09.2009. PMC 3862241. PMID 20007483.
  9. Turing, A. M. (1950). "Computing machinery and intelligence". Mind. 59 (236): 433–460. doi: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. Bibcode:1995PhRvL..75.1222H. doi:10.1103/physrevlett.75.1222. PMID 10060236.
  11. Stassinopoulos, Dimitris; Bak, Per (1995-05-01). "Democratic reinforcement: A principle for brain function". Physical Review E. 51 (5): 5033–5039. Bibcode:1995PhRvE..51.5033S. doi:10.1103/PhysRevE.51.5033. PMID 9963215.
  12. Chialvo, D.R.; Bak, P. (1999). "Learning from mistakes". Neuroscience. 90 (4): 1137–1148. arXiv:adap-org/9707006. doi:10.1016/s0306-4522(98)00472-2. PMID 10338284. S2CID 1304836.
  13. Beggs, J. M.; Plenz, D. (2003). "Neuronal avalanches in neocortical circuits". The Journal of Neuroscience. 23 (35): 11167–11177. doi:10.1523/jneurosci.23-35-11167.2003. PMC 6741045. PMID 14657176.
  14. Neuron 2019 Nov 20;104(4):655-664.e4. doi: 10.1016/j.neuron.2019.08.031. Epub 2019 Oct 7. Cortical Circuit Dynamics Are Homeostatically Tuned to Criticality In Vivo Zhengyu Ma, Gina G Turrigiano, Ralf Wessel, & Keith B Hengen https://pubmed.ncbi.nlm.nih.gov/31601510/
  15. In vivo Quantification of Neural Criticality and Complexity in Mouse Cortex and Striatum in a Model of Cocaine Abstinence. Wesley C. Smith https://www.biorxiv.org/content/10.1101/2022.08.02.501652v2
This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.