Hypothesis that states certain biological neuronal networks work near phase transitions
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.
^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.
^Hesse, Janina; Gross, Thilo (2014-09-23). "Self-organized criticality as a fundamental property of neural systems". Frontiers in Systems Neuroscience. 8: 166. doi:10.3389/fnsys.2014.00166. ISSN 1662-5137. PMC 4171833. PMID 25294989.
^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.
^ abcBeggs, 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.
^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.
^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.
^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.
^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.
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