Critical brain hypothesis
In neuroscience, the critical brain hypothesis states that certain biological neuronal networks work near phase transitions. 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. 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. In criticality, the brain capacity for information processing is enhanced, so subcritical, critical and slightly supercritical branching process of thoughts could describe how human and animal minds function.
Discussion on the brain's criticality have been done since 1950, with the paper on the imitation game for a Turing test. 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. Simultaneously Stassinopoulos and Bak proposed a simple neural network model working at criticality which was expanded latter by Chialvo and Bak. In 2003, the hypothesis found experimental support by Beggs and Plenz. The critical brain hypothesis is not a consensus among the scientific community.
- Ludmila Brochini, Ariadne de Andrade Costa, Miguel Abadi, Antônio C. Roque, Jorge Stolfi, Osame Kinouchi. Phase transitions and self-organized criticality in networks of stochastic spiking neurons. Available at:https://arxiv.org/pdf/1606.06391v1.pdf
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- Beggs, J. M. The criticality hypothesis: how local cortical networks might optimize information processing. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences 366, 329–343 (2008).
- Shew, W. L., Yang, H., Petermann, T., Roy, R. & Plenz, D. Neuronal avalanches imply maximum dynamic range in cortical networks at criticality. The Journal of Neuroscience 29, 15595–15600 (2009).
- Turing, A. M. Computing machinery and intelligence. Mind 59, 433–460 (1950).
- Herz, A. V. & Hopfield, J. J. Earthquake cycles and neural reverberations: collective oscillations in systems with pulse-coupled threshold elements. Physical Review Letters 75, 1222 (1995).
- 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.
- 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.
- Beggs, J. M. & Plenz, D. Neuronal avalanches in neocortical circuits. The Journal of Neuroscience 23, 11167–11177 (2003).