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Usuário(a):MGromov/Critical brain hypothesis

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In neuroscience, the critical brain hypothesis states that certain biological neuronal networks work near phase transitions.[1][2][3]. In experiments, some neurons have shown an activity avalanche, with sizes that follow a power law distribution. This suggest they operate close to a critical point.[4] According to this hypothesis, the brain would transition between two phases, one in which activity will rapidly reduce and die, and another where activity will buid up and amplify over time.[4] In criticality, the brain capacity for information processing is enhanced,[4][5][6][7] so subcritical, critical and slightly supercritical branching process of thoughts could describe how human and animal minds function.[1]

History

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Discussion on the brain's criticality have been done since 1950, with the paper on the imitation game for a Turing test.[8] 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.[9] In 2003, the hypothesis found experimental support by Beggs and Plenz[10] In spite of that, the critical brain hypothesis is not yet a consensus among the scientific community.[1][4]

Referências

  1. ↑ a b c 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:http://arxiv.org/pdf/1606.06391v1.pdf
  2. ↑ Chialvo, D. R. Emergent complex neural dynamics. Nature physics 6, 744–750 (2010).
  3. ↑ 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)
  4. ↑ a b c d Beggs, John M., Timme, Nicholas. Being critical of criticality in the brain. Frontiers in Physiology, 07, June 2012
  5. ↑ Kinouchi, O. & Copelli, M. Optimal dynamical range of excitable networks at criticality. Nature physics 2, 348–351 (2006).
  6. ↑ Beggs, J. M. The criticality hypothesis: how local cortical networks might optimize information process- ing. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences 366, 329–343 (2008).
  7. ↑ 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).
  8. ↑ Turing, A. M. Computing machinery and intelligence. Mind 59, 433–460 (1950).
  9. ↑ 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).
  10. ↑ Beggs, J. M. & Plenz, D. Neuronal avalanches in neocortical circuits. The Journal of neuroscience 23, 11167–11177 (2003).



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