Valencia Urbina, Carlos E. (2024) Comportamientos emergentes en la dinámica neuronal de C. elegans / Emerging behaviors in the neurony dynamics of C. elegans. Tesis Doctoral en Física, Universidad Nacional de Cuyo, Instituto Balseiro.
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Resumen en español
Esta tesis investiga la relación entre la dinámica neuronal y las acciones emergentes en sistemas biológicos y robóticos. El enfoque principal esta inspirado en el sistema nervioso del nematodo Caenorhabditis elegans. Para esto utilizamos un robot controlado por una simulación numérica de la red neuronal de este gusano. El robot interactúa con su entorno a través de sensores y neuronas motoras, lo que le permite realizar acciones emergentes, como evitar colisiones en entornos complejos. Los resultados de esta tesis muestran que la estructura subyacente de la red neuronal desempeña un papel fundamental en los comportamientos observados en seres vivos. En particular, se ha identificado una serie de características relevantes de la dinámica neuronal asociadas con las acciones emergentes del robot. Estas características se observan también en gusanos biológicos, lo que sugiere que son universales en sistemas nerviosos complejos. Además de explorar la dinámica neuronal y las acciones emergentes del robot, esta tesis también aborda la hipótesis de la existencia de criticidad neuronal en el nematodo C. elegans. Se valida esta hipótesis a través de dos enfoques interconectados: primero, el análisis de la firma de criticidad en datos experimentales; y segundo, el desarrollo de un modelo computacional basado en una red neuronal con dinámica de Greenberg–Hastings y conexiones que siguen el conectoma del C. elegans. En conclusión, esta tesis contribuye al entendimiento de la relación entre la estructura neuronal y el comportamiento emergente en sistemas biológicos y robóticos. Los hallazgos de esta investigación tienen importantes implicaciones para la comprensión de los sistemas nerviosos y su aplicación en la neurociencia computacional.
Resumen en inglés
This thesis investigates the relationship between neuronal dynamics and emergent actions in biological and robotic systems. The main focus is inspired by the nervous system of the nematode Caenorhabditis elegans. For this, we use a robot controlled by a numerical simulation of the neural network of this worm. The robot interacts with its environment through sensors and motor neurons, which allows it to perform emergent actions, such as avoiding collisions in complex environments. The results of this thesis show that the underlying structure of the neural network plays a fundamental role in the behaviors observed in living beings. In particular, a number of relevant characteristics of neuronal dynamics associated with the emergent actions of the robot have been identified. These characteristics are also observed in biological worms, suggesting that they are universal in complex nervous systems. In addition to exploring neuronal dynamics and the emergent actions of the robot, this thesis also addresses the hypothesis of the existence of neuronal criticality in the nematode C. elegans. This hypothesis is validated through two interconnected approaches: first, the analysis of the signature of criticality in experimental data; and second, the development of a computational model based on a neural network with Greenberg–Hastings dynamics and connections that follow the connectome of C. elegans. In conclusion, this thesis contributes to the understanding of the relationship between neuronal structure and emergent behavior in biological and robotic systems. The findings of this research have important implications for the understanding of nervous systems and their application in computational neuroscience.
| Tipo de objeto: | Tesis (Tesis Doctoral en Física) |
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| Palabras Clave: | Robots; Automatas; Nematodes; Nematodos; [Neuronal dynamics; Dinámica neuronal; Emergent actions; Acciones emergentes; Connectome; Conectoma; Neuronal criticality; Criticidad neuronal] |
| Referencias: | 1] Baluška, F., Levin, M. On Having No Head: Cognition throughout Biological Systems. Frontiers in Psychology, 7, 2016. URL https://www.frontiersin.org/articles/10.3389/fpsyg.2016.00902. 7 [2] Heuvel, M. P. v. d., Bullmore, E. T., Sporns, O. Comparative Connectomics. Trends in Cognitive Sciences, 20 (5), 345–361, mayo 2016. URL https://www.cell.com/trends/cognitive-sciences/abstract/S1364-6613(16)00048-6. 8, 21, 98 [3] Varshney, L. R., Chen, B. L., Paniagua, E., Hall, D. H., Chklovskii, D. B. Structural Properties of the Caenorhabditis elegans Neuronal Network. PLOS Computational Biology, 7 (2), e1001066, feb. 2011. URL https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1001066. 8, 86 [4] Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U. Network Motifs: Simple Building Blocks of Complex Networks. Science, oct. 2002. URL https://www.science.org/doi/10.1126/science.298.5594.824. 8, 70 [5] Azulay, A., Itskovits, E., Zaslaver, A. The C. elegans Connectome Consists of Homogenous Circuits with Defined Functional Roles. PLOS Computational Biology, 12 (9), e1005021, sep. 2016. URL https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005021. 8, 72 [6] Bono, M. d., Villu Maricq, A. Neuronal Substrates of Complex Behaviors in C. Elegans. Annual Review of Neuroscience, 28 (1), 451–501, 2005. URL https://doi.org/10.1146/annurev.neuro.27.070203.144259. 8, 15, 16 [7] Branson, K., Freeman, J. Imaging the Neural Basis of Locomotion. Cell, 163 (3), 541–542, oct. 2015. URL https://www.sciencedirect.com/science/article/pii/S0092867415013288. 9, 27, 28 [8] Bargmann, C. I., Marder, E. From the connectome to brain function. Nature Methods, 10 (6), 483–490, jun. 2013. URL https://www.nature.com/articles/nmeth.2451. 9 [9] Kato, S., Kaplan, H. S., Schrödel, T., Skora, S., Lindsay, T. H., Yemini, E., et al. Global Brain Dynamics Embed the Motor Command Sequence of Caenorhabditis elegans. Cell, 163 (3), 656–669, oct. 2015. URL https://www.cell.com/cell/ abstract/S0092-8674(15)01196-4. 9, 26, 33, 39, 45, 49, 57, 70, 83, 84, 87, 90, 96, 98, 118, 122, 149, 166, 200, 213, 221 [10] Brambilla, M., Ferrante, E., Birattari, M., Dorigo, M. Swarm robotics: a review from the swarm engineering perspective. Swarm Intelligence, 7 (1), 1–41, mar. 2013. URL https://doi.org/10.1007/s11721-012-0075-2. 10 [11] Yue, Y., Li, S., Shen, P., Park, Y. Caenorhabditis elegans as a model for obesity research. Current Research in Food Science, 4, 692–697, oct. 2021. URL https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8501670/. 11, 12 [12] Toyoshima, Y., Sato, H., Nagata, D., Kanamori, M., Jang, M. S., Kuze, K., et al. Deducing ensemble dynamics and information flow from the whole-brain imaging data, nov. 2022. URL https://www.biorxiv.org/content/10.1101/2022.11.18.517011v1. 13, 213 [13] Fang-Yen, C., Alkema, M. J., Samuel, A. D. T. Illuminating neural circuits and behaviour in Caenorhabditis elegans with optogenetics. Philosophical Transactions of the Royal Society B: Biological Sciences, 370 (1677), 20140212, sep. 2015. URL https://royalsocietypublishing.org/doi/10.1098/rstb.2014.0212, publisher: Royal Society. 13, 14 [14] Cook, S. J., Jarrell, T. A., Brittin, C. A., Wang, Y., Bloniarz, A. E., Yakovlev, M. A., et al. Whole-animal connectomes of both Caenorhabditis elegans sexes. Nature, 571 (7763), 63–71, jul. 2019. URL https://www.nature.com/articles/s41586-019-1352-7. 14, 22, 24, 25, 34, 45, 50, 118, 150, 167, 221 [15] Bargmann, C. Chemosensation in C. elegans. WormBook, 2006. URL http://www.wormbook.org/chapters/www_chemosensation/chemosensation.html.18, 35 [16] Ward, S. Chemotaxis by the Nematode Caenorhabditis elegans: Identification of Attractants and Analysis of the Response by Use of Mutants. Proceedings of the National Academy of Sciences, 70 (3), 817–821, mar. 1973. URL https: //www.pnas.org/doi/10.1073/pnas.70.3.817. 18 [17] Portman, D. S. Neural networks mapped in both sexes of the worm. Nature, 571 (7763), 40–42, jul. 2019. URL https://www.nature.com/articles/d41586-019-02006-8. 20, 22, 23 [18] White, J. G., Southgate, E., Thomson, J. N., Brenner, S. The structure of the nervous system of the nematode Caenorhabditis elegans. Philosophical Transactions of the Royal Society of London. B, Biological Sciences, 314 (1165), 1–340, ene. 1997. URL https://royalsocietypublishing.org/doi/10.1098/rstb. 1986.0056. 20, 22 [19] Schröter, M., Paulsen, O., Bullmore, E. T. Micro-connectomics: probing the organization of neuronal networks at the cellular scale. Nature Reviews Neuroscience, 18 (3), 131–146, mar. 2017. URL https://www.nature.com/articles/nrn.2016.182. 21 [20] Flavell, S. W., Gordus, A. Dynamic functional connectivity in the static connectome of Caenorhabditis elegans. Current Opinion in Neurobiology, 73,102515, abr. 2022. URL https://www.sciencedirect.com/science/article/pii/S0959438821001549. 21, 46, 73, 267 [21] Ryan, K., Lu, Z., Meinertzhagen, I. A. The CNS connectome of a tadpole larva of Ciona intestinalis (L.) highlights sidedness in the brain of a chordate sibling. eLife, 5, e16962, dic. 2016. URL https://doi.org/10.7554/eLife.16962, publisher: eLife Sciences Publications, Ltd. 21 [22] Verasztó, C., Jasek, S., Gühmann, M., Shahidi, R., Ueda, N., Beard, J. D., et al. Whole-animal connectome and cell-type complement of the threesegmented Platynereis dumerilii larva, ago. 2020. URL https://www.biorxiv.org/content/10.1101/2020.08.21.260984v2. 21 [23] Scheffer, L. K., Xu, C. S., Januszewski, M., Lu, Z., Takemura, S.-y., Hayworth, K. J., et al. A connectome and analysis of the adult Drosophila central brain. eLife, 9, e57443, sep. 2020. URL https://doi.org/10.7554/eLife.57443. 21,99 [24] Winding, M., Pedigo, B. D., Barnes, C. L., Patsolic, H. G., Park, Y., Kazimiers, T., et al. The connectome of an insect brain. Science, 379 (6636), eadd9330, mar. 2023. URL https://www.science.org/doi/10.1126/science.add9330. 21 [25] Kaplan, H. S., Salazar Thula, O., Khoss, N., Zimmer, M. Nested Neuronal Dynamics Orchestrate a Behavioral Hierarchy across Timescales. Neuron, 105 (3), 562–576.e9, feb. 2020. URL https://www.sciencedirect.com/ science/article/pii/S0896627319309328. 26, 29, 49, 96, 97, 98, 118, 166, 185, 197, 200, 205, 221 [26] Yemini, E., Lin, A., Nejatbakhsh, A., Varol, E., Sun, R., Mena, G. E., et al. NeuroPAL: A Multicolor Atlas for Whole-Brain Neuronal Identification in C. elegans. Cell, 184 (1), 272–288.e11, ene. 2021. URL https://www.sciencedirect.com/ science/article/pii/S0092867420316822. 26, 31, 118, 200, 220, 221 [27] Randi, F., Leifer, A. M. Measuring and modeling whole-brain neural dynamics in Caenorhabditis elegans. Current Opinion in Neurobiology, 65, 167– 175, dic. 2020. URL https://www.sciencedirect.com/science/article/pii/ S0959438820301689. 27, 47, 48 [28] Hollon, N. G., Jin, X. Neural Implementation of Behavioral Hierarchy. Neuron, 105 (3), 402–404, feb. 2020. URL https://www.cell.com/neuron/abstract/S0896-6273(20)30006-4. 29, 30 [29] Santiago, E. R., Shelar, A., Christie, N. T., Lewis-Hayre, M. R., Koelle, M. R. Using NeuroPAL Multicolor Fluorescence Labeling to Identify Neurons in C. elegans. Current Protocols, 2 (12), e610, 2022. URL https://onlinelibrary.wiley.com/doi/abs/10.1002/cpz1.610. 31 [30] Gray, J. M., Hill, J. J., Bargmann, C. I. A circuit for navigation in Caenorhabditis elegans. Proceedings of the National Academy of Sciences, 102 (9), 3184–3191, mar. 2005. URL https://www.pnas.org/doi/10.1073/pnas.0409009101. 32 [31] Kaplan, H. S., Nichols, A. L., Zimmer, M. Sensorimotor integration in Caenorhabditis elegans: a reappraisal towards dynamic and distributed computations. Philosophical Transactions of the Royal Society B: Biological Sciences, 373 (1758), 20170371, sep. 2018. URL https://royalsocietypublishing.org/doi/10.1098/rstb.2017.0371. 32, 33, 39, 42 [32] Elsayed, G. F., Cunningham, J. P. Structure in neural population recordings: an expected byproduct of simpler phenomena? Nature Neuroscience, 20 (9), 1310–1318, sep. 2017. URL https://www.nature.com/articles/nn.4617. 33 [33] Norman-Tenazas, R., Matelsky, J., Katyal, K., Johnson, E., Gray-Roncal, W. Worminator: A platform to enable bio-inspired (C. elegans) robotics. En: 2018 Conference on Cognitive Computational Neuroscience. Cognitive Computational Neuroscience, 2018. URL https://ccneuro.org/2018/Papers/ViewPapers.asp?PaperNum=1149. 34 [34] Sarma, G. P., Lee, C. W., Portegys, T., Ghayoomie, V., Jacobs, T., Alicea, B., et al. OpenWorm: overview and recent advances in integrative biological simulation of Caenorhabditis elegans. Philosophical Transactions of the Royal Society B: Biological Sciences, 373 (1758), 20170382, sep. 2018. URL https://royalsocietypublishing.org/doi/full/10.1098/rstb.2017.0382. 35 [35] Lechner, M., Grosu, R., Hasani, R. M. Worm-level Control through Search-based Reinforcement Learning, nov. 2017. URL http://arxiv.org/abs/1711.03467.35 [36] Morse, T. M., Lockery, S. R., Ferrée, T. C. Robust Spatial Navigation in a Robot Inspired by Chemotaxis in Caenorhabditis elegans. Adaptive Behavior, 6 (3-4), 393–410, ene. 1998. URL https://doi.org/10.1177/105971239800600303. 35, 263 [37] Busbice, T. The robotic worm. BioCoder, 2015. 35, 36 [38] Frederick, S. W., Meyer, A. B., Mochon, D. Characterizing perceptions of energy consumption. Proceedings of the National Academy of Sciences, 108 (8), E23–E23, feb. 2011. URL https://www.pnas.org/doi/full/10.1073/pnas.1014806108. 37 [39] Uzel, K., Kato, S., Zimmer, M. A set of hub neurons and non-local connectivity features support global brain dynamics in C. elegans. Current Biology, 32 (16), 3443–3459.e8, ago. 2022. URL https://www.sciencedirect.com/science/article/pii/S0960982222010016. 38 [40] Der, R., Martius, G. The Playful Machine, tomo 15 de Cognitive Systems Monographs. Berlin, Heidelberg: Springer, 2012. URL http://link.springer.com/10.1007/978-3-642-20253-7. 38 [41] Brembs, B. Towards a scientific concept of free will as a biological trait: spontaneous actions and decision-making in invertebrates. Proceedings of the Royal Society B: Biological Sciences, 278 (1707), 930–939, dic. 2010. URL https://royalsocietypublishing.org/doi/10.1098/rspb.2010.2325. 38 [42] Webb, B. Robots in invertebrate neuroscience. Nature, 417 (6886), 359–363, mayo 2002. URL https://www.nature.com/articles/417359a. 38 [43] Floreano, D., Ijspeert, A. J., Schaal, S. Robotics and Neuroscience. Current Biology, 24 (18), R910–R920, sep. 2014. URL https://www.cell.com/current-biology/abstract/S0960-9822(14)00921-X, publisher: Elsevier. 38 [44] Pfeifer, R., Lungarella, M., Iida, F. Self-Organization, Embodiment, and Biologically Inspired Robotics. Science, 318 (5853), 1088–1093, nov. 2007. URL https://www.science.org/doi/10.1126/science.1145803, publisher: American Association for the Advancement of Science. 38 [45] Busbice, T. Extending the C. Elegans Connectome to Robotics. Original Research, mayo 2014. URL http://www.connectomeengine.com/download/Papers/Connectome.pdf. 40, 51 [46] Gleeson, P., Cantarelli, M., Marin, B., Quintana, A., Earnshaw, M., Sadeh, S., et al. Open Source Brain: A Collaborative Resource for Visualizing, Analyzing, Simulating, and Developing Standardized Models of Neurons and Circuits. Neuron, 103 (3), 395–411.e5, ago. 2019. URL https://linkinghub.elsevier.com/retrieve/pii/S0896627319304441. 47 [47] Gleeson, P., Lung, D., Grosu, R., Hasani, R., Larson, S. D. c302: a multiscale framework for modelling the nervous system of Caenorhabditis elegans. Philosophical Transactions of the Royal Society B: Biological Sciences, 373 (1758), 20170379, oct. 2018. URL https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6158223/. 47 [48] Kuramochi, M., Doi, M. A Computational Model Based on Multi-Regional Calcium Imaging Represents the Spatio-Temporal Dynamics in a Caenorhabditis elegans Sensory Neuron. PLOS ONE, 12 (1), e0168415, ene. 2017. URL https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0168415, publisher: Public Library of Science. 47 [49] Boyle, J. H., Cohen, N. Caenorhabditis elegans body wall muscles are simple actuators. Biosystems, 94 (1), 170–181, oct. 2008. URL https://www. sciencedirect.com/science/article/pii/S0303264708001408. 47 [50] Roberts, W. M., Augustine, S. B., Lawton, K. J., Lindsay, T. H., Thiele, T. R., Izquierdo, E. J., et al. A stochastic neuronal model predicts random search behaviors at multiple spatial scales in C. elegans. eLife, 5, e12572. URL https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4798983/. 47 [51] Izquierdo, E. J., Beer, R. D. An Integrated Neuromechanical Model of Steering in C. elegans. págs. 199–206. MIT Press, 2015. URL https://dx.doi.org/10.1162/978-0-262-33027-5-ch040. 47 [52] Palyanov, A., Khayrulin, S., Larson, S. D., Dibert, A. Towards a virtual C. elegans: A framework for simulation and visualization of the neuromuscular system in a 3D physical environment. In Silico Biology, 11 (3-4), 137–147, ene. 2012. URL https://content.iospress.com/articles/in-silico-biology/isb0445. 47 [53] Kunert, J. M., Proctor, J. L., Brunton, S. L., Kutz, J. N. Spatiotemporal Feedback and Network Structure Drive and Encode Caenorhabdi tis elegans Locomotion. PLOS Computational Biology, 13 (1), e1005303,ene. 2017. URL https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005303, publisher: Public Library of Science. 48 [54] Morrison, M., Fieseler, C., Kutz, J. N. Nonlinear Control in the Nematode C. elegans. Frontiers in Computational Neuroscience, 14, 2021. URL https://www.frontiersin.org/articles/10.3389/fncom.2020.616639. 48 [55] Linderman, S., Nichols, A., Blei, D., Zimmer, M., Paninski, L. Hierarchical recurrent state space models reveal discrete and continuous dynamics of neural activity in C. elegans, abr. 2019. URL https://www.biorxiv.org/content/10.1101/621540v1. 48 [56] Costa, A. C., Ahamed, T., Stephens, G. J. Adaptive, locally linear models of complex dynamics. Proceedings of the National Academy of Sciences of the United States of America, 116 (5), 1501–1510, ene. 2019. URL https://www.ncbi.nlm. nih.gov/pmc/articles/PMC6358715/. 48 [57] Fieseler, C., Zimmer, M., Kutz, J. N. Unsupervised learning of control signals and their encodings in Caenorhabditis elegans whole-brain recordings. Journal of The Royal Society Interface, 17 (173), 20200459, dic. 2020. URL https://royalsocietypublishing.org/doi/10.1098/rsif.2020.0459, publisher: Royal Society. 48 [58] Aguilera, M., Alquézar, C., Izquierdo, E. J. Signatures of criticality in a maximum entropy model of the C. elegans brain during free behaviour. págs. 29–35. MIT Press, 2017. URL https://dx.doi.org/10.1162/isal_a_010. 48 [59] Tutorial: A Raspberry Pi robot with a worm brain. URL https://www.dexterindustries.com/GoPiGo/projects/python-examples-for-the-raspberry-pi/simulate-a-worm-brain/. 51 [60] GoPiGo C. elegans Connectome Code, sep. 2023. URL https://github.com/Connectome/GoPiGo, original-date: 2015-02-09T23:25:43Z. 51 [61] Foundation, R. P. Teach, learn, and make with the Raspberry Pi Foundation, sep. 2023. URL https://www.raspberrypi.org/. 51 [62] Dexter Industries. URL https://www.dexterindustries.com/. 53 [63] Principal Component Analysis. Springer Series in Statistics. Springer-Verlag, 2002. URL http://link.springer.com/10.1007/b98835.59 [64] Chalfie, M., Hart, A. C., Rankin, C. H., Goodman, M. B. Assaying mechanosensation. En: WormBook: The Online Review of C. elegans Biology [Internet]. WormBook, 2018. URL https://www.ncbi.nlm.nih.gov/books/NBK235860/.74 [65] Chalfie, M., Sulston, J. E., White, J. G., Southgate, E., Thomson, J. N., Brenner, S. The neural circuit for touch sensitivity in Caenorhabditis elegans. Journal of Neuroscience, 5 (4), 956–964, abr. 1985. URL https://www.jneurosci.org/content/5/4/956. 74 [66] Manrubia, S. C., Mikhailov, A. S., Zanette, D. Emergence of Dynamical Order: Synchronization Phenomena in Complex Systems. World Scientific, 2004. 89 [67] Jensen, O., Colgin, L. L. Cross-frequency coupling between neuronal oscillations. Trends in Cognitive Sciences, 11 (7), jul. 2007. URL https://www. sciencedirect.com/science/article/pii/S1364661307001271. 94 [68] Muñoz, M. A. Colloquium: Criticality and dynamical scaling in living systems. Reviews of Modern Physics, 90 (3), 031001, Jul 2018. URL https://link.aps.org/doi/10.1103/RevModPhys.90.031001. 106 [69] Hidalgo, J., Grilli, J., Suweis, S., Muñoz, M. A., Banavar, J. R., Maritan, A. Information-based fitness and the emergence of criticality in living systems. Proceedings of the National Academy of Sciences, 111 (28), 10095–10100, jul. 2014. URL https://www.pnas.org/doi/abs/10.1073/pnas.1319166111. [70] Kauffman, S. A. The Origins of Order: Self-Organization and Selection in Evolution. New York: Oxford University Press, 1993. URL https://books.google.com.ar/books?id=lZcSpRJz0dgC. [71] Bak, P. How Nature Works. Springer, 1996. URL http://link.springer.com/10.1007/978-1-4757-5426-1. 109 [72] Chialvo, D. R., Balenzuela, P., Fraiman, D. The Brain: What is Critical About It? AIP Conference Proceedings, 1028 (1), 28–45, jul. 2008. URL https://aip. scitation.org/doi/abs/10.1063/1.2965095. [73] Chialvo, D. R. Emergent complex neural dynamics. Nature Physics, 6 (10), 744–750, oct. 2010. URL https://www.nature.com/articles/nphys1803. [74] Plenz, D. The Critical Brain. Physics, 6, 47, abr. 2013. URL https://physics. aps.org/articles/v6/47. [75] Niebur, E., Plenz, D., Schuster, H. Criticality in Neural Systems. Weinheim: Wiley, 2014. [76] Shew, W. L., Plenz, D. The Functional Benefits of Criticality in the Cortex. The Neuroscientist, 19 (1), 88–100, feb. 2013. URL https://doi.org/10.1177/1073858412445487. [77] Cocchi, L., Gollo, L. L., Zalesky, A., Breakspear, M. Criticality in the brain: A synthesis of neurobiology, models and cognition. Progress in Neurobiology, 158, 132–152, nov. 2017. URL https://www.sciencedirect.com/science/ article/pii/S0301008216301630. [78] Zimmern, V. Why Brain Criticality Is Clinically Relevant: A Scoping Review. Frontiers in Neural Circuits, 14, 54, ago. 2020. URL https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7479292/. 106, 110 [79] Heffern, E. F. W., Huelskamp, H., Bahar, S., Inglis, R. F. Phase transitions in biology: from bird flocks to population dynamics. Proceedings of the Royal Society B: Biological Sciences, 288 (1961), 20211111, oct. 2021. URL https://royalsocietypublishing.org/doi/10.1098/rspb.2021.1111. 107 [80] de Kruyff, B., Demel, R. A., dan Deenen, L. L. M. The effect of cholesterol and epicholesterol incorporation on the permeability and on the phase transition of intact Acholeplasma laidlawii cell membranes and derived liposomes. Biochimica et Biophysica Acta (BBA) - Biomembranes, 255 (1), 331–347, ene. 1972. URL https://www.sciencedirect.com/science/article/pii/0005273672900326. 107 [81] Adhikari, B. M., Prasad, A., Dhamala, M. Time-delay-induced phase-transition to synchrony in coupled bursting neurons. Chaos: An Interdisciplinary Journal of Nonlinear Science, 21 (2), 023116, jun. 2011. URL https://aip.scitation.org/doi/10.1063/1.3584822. 107 [82] Baumgarten, L., Bornholdt, S. Critical excitation-inhibition balance in dense neural networks. Physical Review E, 100 (1), 010301, jul. 2019. URL https://link.aps.org/doi/10.1103/PhysRevE.100.010301. 107 [83] Cavagna, A., Cimarelli, A., Giardina, I., Parisi, G., Santagati, R., Stefanini, F., et al. Scale-free correlations in starling flocks. Proceedings of the National Academy of Sciences, 107 (26), 11865–11870, jun. 2010. URL https: //www.pnas.org/doi/full/10.1073/pnas.1005766107. 107 [84] Bahar, S. Flocking, Swarming, and Communicating. En: S. Bahar (ed.) The Essential tension: Competition, Cooperation and Multilevel Selection in Evolution, The Frontiers Collection, págs. 127–152. Dordrecht: Springer Netherlands, 2018. URL https://doi.org/10.1007/978-94-024-1054-9_8. [85] Attanasi, A., Cavagna, A., Del Castello, L., Giardina, I., Melillo, S., Parisi, L., et al. Finite-Size Scaling as a Way to Probe Near-Criticality in Natural Swarms. Physical Review Letters, 113 (23), 238102, dic. 2014. URL https://link.aps.org/doi/10.1103/PhysRevLett.113.238102. 107 [86] Gurmessa, B. J., Bitten, N., Nguyen, D. T., Saleh, O. A., Ross, J. L., Das, M., et al. Triggered disassembly and reassembly of actin networks induces rigidity phase transitions. Soft matter, 15 (6), 1335–1344, feb. 2019. URL https://www. ncbi.nlm.nih.gov/pmc/articles/PMC6486790/. 107 [87] Hussain, S., Molloy, J. E., Khan, S. M. Spatiotemporal Dynamics of Actomyosin Networks. Biophysical Journal, 105 (6), 1456–1465, sep. 2013. URL https://www.cell.com/biophysj/abstract/S0006-3495(13)00872-2. 107 [88] Mensonides, F. I. C., Schuurmans, J. M., Teixeira de Mattos, M. J., Hellingwerf, K. J., Brul, S. The metabolic response of Saccharomyces cerevisiae to continuous heat stress. Molecular Biology Reports, 29 (1-2), 103–106, 2002. 107 [89] Ordway, S. W., King, D. M., Friend, D., Noto, C., Phu, S., Huelskamp, H., et al. Phase transition behaviour in yeast and bacterial populations under stress. Royal Society Open Science, 7 (7), 192211, jul. 2020. URL https: //royalsocietypublishing.org/doi/10.1098/rsos.192211. 107 [90] Pascual, I., Aguirre, J., Manrubia, S., Cuesta, J. A. Epistasis between cultural traits causes paradigm shifts in cultural evolution. Royal Society Open Science, 7 (2), 191813, feb. 2020. URL https://royalsocietypublishing.org/doi/10.1098/rsos.191813, publisher: Royal Society. 107 [91] Held, J., Lorimer, T., Pomati, F., Stoop, R., Albert, C. Second-order phase transition in phytoplankton trait dynamics. Chaos: An Interdisciplinary Journal of Nonlinear Science, 30 (5), 053109, mayo 2020. URL https://aip.scitation.org/doi/10.1063/1.5141755. 107 [92] Hein, S., Jacob, J. Population recovery of a common vole population (Microtus arvalis) after population collapse. Pest Management Science, 75 (4), 908–914, 2019. URL https://onlinelibrary.wiley.com/doi/abs/10.1002/ps.5211. 107 [93] Lauerburg, R. A. M., Diekmann, R., Blanz, B., Gee, K., Held, H., Kannen, A., et al. Socio-ecological vulnerability to tipping points: A review of empirical approaches and their use for marine management. Science of The Total Environment, 705, 135838, feb. 2020. URL https://www.sciencedirect.com/science/article/pii/S0048969719358334. [94] Heinze, C., Blenckner, T., Martins, H., Rusiecka, D., Döscher, R., Gehlen, M., et al. The quiet crossing of ocean tipping points. Proceedings of the National Academy of Sciences, 118 (9), e2008478118, mar. 2021. URL https://www. pnas.org/doi/full/10.1073/pnas.2008478118. 107 [95] Petridou, N. I., Corominas-Murtra, B., Heisenberg, C.-P., Hannezo, E. Rigidity percolation uncovers a structural basis for embryonic tissue phase transitions. Cell, 184 (7), 1914–1928.e19, abr. 2021. URL https://www.cell.com/cell/abstract/S0092-8674(21)00167-7. 107 [96] Garcia-Ojalvo, J., Elowitz, M. B., Strogatz, S. H. Modeling a synthetic multicellular clock: Repressilators coupled by quorum sensing. Proceedings of the National Academy of Sciences, 101 (30), 10955–10960, jul. 2004. URL https: //www.pnas.org/doi/full/10.1073/pnas.0307095101. 106 [97] Forgacs, G., Newman, S. A., Obukhov, S. P., Birk, D. E. Phase transition and morphogenesis in a model biological system. Physical Review Letters, 67 (17), 2399–2402, oct. 1991. URL https://link.aps.org/doi/10.1103/ PhysRevLett.67.2399. 106 [98] Newman, S. A., Forgacs, G., Hinner, B., Maier, C. W., Sackmann, E. Phase transformations in a model mesenchymal tissue. Physical Biology, 1 (2), 100, jun. 2004. URL https://dx.doi.org/10.1088/1478-3967/1/2/006. [99] Alvarado, J., Sheinman, M., Sharma, A., MacKintosh, F. C., Koenderink, G. H. Molecular motors robustly drive active gels to a critically connected state. Nature Physics, 9 (9), 591–597, sep. 2013. URL https://www.nature.com/articles/nphys2715. 106 [100] Magee Jr., W. S., Gibbs, J. H., Zimm, B. H. Theory of helix–coil transitions involving complementary poly- and oligo-nucleotides. I. The complete binding case. Biopolymers, 1 (2), 133–143, 1963. URL https://onlinelibrary.wiley. com/doi/abs/10.1002/bip.360010204. 106 [101] Li, Y. C., Retzloff, D. Phase transition as a mechanism of DNA opening for replication and transcription. Mathematical Biosciences, 203 (1), 137–147, sep. 2006. 106 [102] Kelso, J. A. Phase transitions and critical behavior in human bimanual coordination. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 246 (6), R1000–R1004, jun. 1984. URL https://journals. physiology.org/doi/abs/10.1152/ajpregu.1984.246.6.R1000. 106 [103] Freeman, W. J., Holmes, M. D. Metastability, instability, and state transition in neocortex. Neural Networks, 18 (5), 497–504, jul. 2005. URL https://www. sciencedirect.com/science/article/pii/S0893608005001085. [104] Rabinovich, M. I., Varona, P., Selverston, A. I., Abarbanel, H. D. I. Dynamical principles in neuroscience. Reviews of Modern Physics, 78 (4), 1213–1265, nov. 2006. URL https://link.aps.org/doi/10.1103/RevModPhys.78.1213. [105] Werner, G. Metastability, criticality and phase transitions in brain and its models. Bio Systems, 2007. [106] Adamatzky, A., Chen, G. Chaos, CNN, Memristors and Beyond: A Festschrift for Leon ChuaWith DVD-ROM, composed by Eleonora Bilotta. WORLD SCIENTIFIC, 2013. URL https://www.worldscientific.com/worldscibooks/10. 1142/8590. [107] Haken, H., Haken, H. Principles of Brain Functioning, tomo 67 de Springer Series in Synergetics. Berlin, Heidelberg: Springer, 1996. URL http://link. springer.com/10.1007/978-3-642-79570-1. 106 [108] Tsuchiya, M., Giuliani, A., Hashimoto, M., Erenpreisa, J., Yoshikawa, K. Self- Organizing Global Gene Expression Regulated through Criticality: Mechanism of the Cell-Fate Change. PLOS ONE, 11 (12), e0167912, dic. 2016. URL https://journals.plos.org/plosone/article?id=10.1371/journal. pone.0167912. 106 [109] Larkin, J. W., Zhai, X., Kikuchi, K., Redford, S., Prindle, A., Liu, J., et al. Signal percolation within a bacterial community. Cell systems, 7 (2), 137–145.e3, ago. 2018. URL https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6214369/. 107 [110] Chen, Y., Ferrell, J. E. C. elegans colony formation as a condensation phenomenon. Nature Communications, 12 (1), 4947, ago. 2021. URL https://www.nature.com/articles/s41467-021-25244-9. 107 [111] Hesse, J., Gross, T. Self-organized criticality as a fundamental property of neural systems. Frontiers in Systems Neuroscience, 8, 2014. URL https://www.frontiersin.org/articles/10.3389/fnsys.2014.00166. 107, 111, 115, 116,117 [112] Legenstein, R., Maass, W. Edge of chaos and prediction of computational performance for neural circuit models. Neural Networks, 20 (3), 323–334, abr. 2007. URL https://www.sciencedirect.com/science/article/pii/S0893608007000433. 108 [113] Tagliazucchi, E. The signatures of conscious access and its phenomenology are consistent with large-scale brain communication at criticality. Consciousness and Cognition, 55, 136–147, oct. 2017. URL https://www.sciencedirect.com/science/article/pii/S1053810017300478. 108 [114] Shew, W. L., Yang, H., Petermann, T., Roy, R., Plenz, D. Neuronal Avalanches Imply Maximum Dynamic Range in Cortical Networks at Criticality. Journal of Neuroscience, 29 (49), 15595–15600, dic. 2009. URL https://www.jneurosci.org/content/29/49/15595. 108, 113 [115] Bertschinger, N., Natschläger, T. Real-Time Computation at the Edge of Chaos in Recurrent Neural Networks. Neural Computation, 16 (7), 1413–1436, jul. 2004. URL https://doi.org/10.1162/089976604323057443. 108 [116] Plenz, D., Thiagarajan, T. C. The organizing principles of neuronal avalanches: cell assemblies in the cortex? Trends in Neurosciences, 30 (3), 101–110, mar. 2007. URL https://www.cell.com/trends/neurosciences/abstract/S0166-2236(07)00019-7. 108 [117] Beggs, J. M. 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, ago. 2007. URL https://royalsocietypublishing.org/doi/10.1098/rsta.2007. 2092. [118] Haldeman, C., Beggs, J. M. Critical Branching Captures Activity in Living Neural Networks and Maximizes the Number of Metastable States. Physical Review Letters, 94 (5), 058101, feb. 2005. URL https://link.aps.org/doi/10.1103/PhysRevLett.94.058101. [119] Lombardi, F., Herrmann, H. J., Perrone-Capano, C., Plenz, D., de Arcangelis, L. Balance between Excitation and Inhibition Controls the Temporal Organization of Neuronal Avalanches. Physical Review Letters, 108 (22), 228703, mayo 2012. URL https://link.aps.org/doi/10.1103/PhysRevLett.108.228703. [120] Vázquez-Rodríguez, B., Avena-Koenigsberger, A., Sporns, O., Griffa, A., Hagmann, P., Larralde, H. Stochastic resonance at criticality in a network mo del of the human cortex. Scientific Reports, 7 (1), 13020, oct. 2017. URL https://www.nature.com/articles/s41598-017-13400-5. 108 [121] Kinouchi, O., Copelli, M. Optimal dynamical range of excitable networks at criticality. Nature Physics, 2 (5), 348–351, mayo 2006. URL https://www.nature.com/articles/nphys289. 108 [122] Liu, K. K. L., Bartsch, R. P., Lin, A., Mantegna, R. N., Ivanov, P. C. Plasticity of brain wave network interactions and evolution across physiologic states. Frontiers in Neural Circuits, 9, 2015. URL https://www.frontiersin.org/articles/ 10.3389/fncir.2015.00062. 108 [123] Schneidman, E., Berry, M. J., Segev, R., Bialek, W. Weak pairwise correlations imply strongly correlated network states in a neural population. Nature, 440 (7087), 1007–1012, abr. 2006. URL https://www.nature.com/articles/nature04701. 108 [124] Beggs, J. M., Plenz, D. Neuronal Avalanches in Neocortical Circuits. Journal of Neuroscience, 23 (35), 11167–11177, dic. 2003. URL https://www.jneurosci.org/content/23/35/11167. 108, 113, 114, 115 [125] Kauffman, S. A. Emergent properties in random complex automata. Physica D: Nonlinear Phenomena, 10, ene. 1984. URL https://www.sciencedirect.com/science/article/pii/0167278984902574. 108 [126] Derrida, B., Pomeau, Y. Random Networks of Automata: A Simple Annealed Approximation. Europhysics Letters, 1 (2), 45, ene. 1986. URL https://dx.doi.org/10.1209/0295-5075/1/2/001. 108 [127] Langton, C. G. Computation at the edge of chaos: Phase transitions and emergent computation. Physica D: Nonlinear Phenomena, 42 (1), 12–37, jun. 1990. URL https://www.sciencedirect.com/science/article/pii/016727899090064V. 108 [128] Carstens, N., Adejube, B., Strunskus, T., Faupel, F., Brown, S., Vahl, A. Brainlike critical dynamics and long-range temporal correlations in percolating networks of silver nanoparticles and functionality preservation after integration of insulating matrix. Nanoscale Advances, 4 (15), 3149–3160, jul. 2022. 108 [129] Haykin, S., Principe, J. C., Sejnowski, T. J., Mcwhirter, J. What Makes a Dynamical System Computationally Powerful? En: New Directions in Statistical Signal Processing: From Systems to Brains, págs. 127–154. MIT Press, 2007. URL https://ieeexplore.ieee.org/document/6282087. 108 [130] Fontenele, A. J., de Vasconcelos, N. A., Feliciano, T., Aguiar, L. A., Soares- Cunha, C., Coimbra, B., et al. Criticality between Cortical States. Physical Review Letters, 122 (20), 208101, mayo 2019. URL https://link.aps.org/ doi/10.1103/PhysRevLett.122.208101. 108 [131] Beggs, J. M., Plenz, D. Neuronal Avalanches Are Diverse and Precise Activity Patterns That Are Stable for Many Hours in Cortical Slice Cultures. Journal of Neuroscience, 24 (22), 5216–5229, 2004. URL https://www.jneurosci.org/ content/24/22/5216. 108 [132] Hahn, G., Petermann, T., Havenith, M. N., Yu, S., Singer, W., Plenz, D., et al. Neuronal Avalanches in Spontaneous Activity In Vivo. Journal of Neurophysiology, 04 (6), 3312–3322, dic. 2010. URL https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3007625/. 108 [133] Petermann, T., Thiagarajan, T. C., Lebedev, M. A., Nicolelis, M. A. L., Chialvo, D. R., Plenz, D. Spontaneous cortical activity in awake monkeys composed of neuronal avalanches. Proceedings of the National Academy of Sciences, 106 (37), 15921–15926, sep. 2009. URL https://www.pnas.org/doi/full/10.1073/pnas.0904089106. [134] Shriki, O., Alstott, J., Carver, F., Holroyd, T., Henson, R. N., Smith, M. L., et al. Neuronal Avalanches in the Resting MEG of the Human Brain. The Journal of Neuroscience, 33 (16), 7079–7090, abr. 2013. URL https://www.ncbi.nlm.nih. gov/pmc/articles/PMC3665287/. 108 [135] Tagliazucchi, E., Balenzuela, P., Fraiman, D., Chialvo, D. Criticality in Large- Scale Brain fMRI Dynamics Unveiled by a Novel Point Process Analysis. Frontiers in Physiology, 3, 2012. URL https://www.frontiersin.org/articles/10.3389/fphys.2012.00015. 108, 221 [136] Linkenkaer-Hansen, K., Nikouline, V. V., Palva, J. M., Ilmoniemi, R. J. Long- Range Temporal Correlations and Scaling Behavior in Human Brain Oscillations. Journal of Neuroscience, 21 (4), 1370–1377, feb. 2001. URL https: //www.jneurosci.org/content/21/4/1370. 108 [137] Expert, P., Lambiotte, R., Chialvo, D. R., Christensen, K., Jensen, H. J., Sharp, D. J., et al. Self-similar correlation function in brain resting-state functional magnetic resonance imaging. Journal of The Royal Society Interface, 8 (57), 472–479, sep. 2010. URL https://royalsocietypublishing.org/doi/10.1098/rsif.2010.0416. 108 [138] Fraiman, D., Chialvo, D. What kind of noise is brain noise: anomalous scaling behavior of the resting brain activity fluctuations. Frontiers in Physiology, 3, 2012. URL https://www.frontiersin.org/articles/10.3389/fphys.2012. 00307. 108 [139] Kitzbichler, M. G., Smith, M. L., Christensen, S. R., Bullmore, E. Broadband Criticality of Human Brain Network Synchronization. PLOS Computational Biology, 5 (3), e1000314, mar. 2009. URL https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000314. 108 [140] Van Orden, G. C., Holden, J. G., Turvey, M. T. Human Cognition and 1/f Scaling. Journal of Experimental Psychology: General, 134, 117–123, 2005. 108 [141] Shew, W. L., Clawson, W. P., Pobst, J., Karimipanah, Y., Wright, N. C., Wessel, R. Adaptation to sensory input tunes visual cortex to criticality. Nature Physics, 11 (8), 659–663, ago. 2015. URL https://www.nature.com/articles/nphys3370. 108 [142] Ribeiro, T. L., Jendrichovsky, P., Yu, S., Martin, D. A., Kanold, P., Chialvo, D. R., et al. Trial-by-Trial Variability in Cortical Responses Exhibits Scaling in Spatial Correlations Predicted From Critical Dynamics, mayo 2022. URL https://papers.ssrn.com/abstract=4102639. 108 [143] Haimovici, A., Tagliazucchi, E., Balenzuela, P., Chialvo, D. R. Brain Organization into Resting State Networks Emerges at Criticality on a Model of the Human Connectome. Physical Review Letters, 110 (17), 178101, abr. 2013. URL https //link.aps.org/doi/10.1103/PhysRevLett.110.178101. 108, 118, 147, 167, 234 [144] Moretti, P., Muñoz, M. A. Griffiths phases and the stretching of criticality in brain networks. Nature Communications, 4 (1), 2521, oct. 2013. URL https: //www.nature.com/articles/ncomms3521. 109 [145] Christensen, K., Donangelo, R., Koiller, B., Sneppen, K. Evolution of Random Networks. Physical Review Letters, 81 (11), 2380–2383, sep. 1998. URL https: //link.aps.org/doi/10.1103/PhysRevLett.81.2380. 109 [146] Bornholdt, S., Rohlf, T. Topological Evolution of Dynamical Networks: Global Criticality from Local Dynamics. Physical Review Letters, 84 (26), 6114–6117, jun. 2000. URL https://link.aps.org/doi/10.1103/PhysRevLett.84.6114. [147] Bornholdt, S., Röhl, T. Self-organized critical neural networks. Physical Review E, 67 (6), 066118, jun. 2003. URL https://link.aps.org/doi/10.1103/ PhysRevE.67.066118. 109 [148] Tadić, B., Melnik, R. Self-Organised Critical Dynamics as a Key to Fundamental Features of Complexity in Physical, Biological, and Social Networks. Dynamics, 1 (2), 181–197, dic. 2021. URL https://www.mdpi.com/2673-8716/1/2/11. 109 [149] Gross, T., Blasius, B. Adaptive coevolutionary networks: a review. Journal of The Royal Society Interface, 5 (20), 259–271, oct. 2007. URL https://royalsocietypublishing.org/doi/10.1098/rsif.2007.1229. 109 [150] Jensen, H. J. Self-Organized Criticality: Emergent Complex Behavior in Physical and Biological Systems. Cambridge University Press, 1998. 109 [151] Pruessner, G. Self-Organised Criticality: Theory, Models and Characterisation. Cambridge: Cambridge University Press, 2012. URL https://www.cambridge.org/core/books/selforganised-criticality/2AEAD437DD44A6ACBF83B57A45BF8339. 109 [152] Bonachela, J. A., Muñoz, M. A. Self-organization without conservation: true or just apparent scale-invariance? Journal of Statistical Mechanics: Theory and Experiment, 2009 (09), P09009, sep. 2009. URL https://dx.doi.org/10.1088/1742-5468/2009/09/P09009. 109 [153] Bonachela, J. A., Franciscis, S. d., Torres, J. J., Muñoz, M. A. Self-organization without conservation: are neuronal avalanches generically critical? Journal of Statistical Mechanics: Theory and Experiment, 2010 (02), P02015, feb. 2010. URL https://dx.doi.org/10.1088/1742-5468/2010/02/P02015. 113 [154] Buendía, V., di Santo, S., Bonachela, J. A., Muñoz, M. A. Feedback Mechanisms for Self-Organization to the Edge of a Phase Transition. Frontiers in Physics, 8, 2020. URL https://www.frontiersin.org/articles/10.3389/fphy.2020.00333. 109 [155] Palmieri, L., Jensen, H. J. The emergence of weak criticality in SOC systems. Europhysics Letters, 123 (2), 20002, ago. 2018. URL https://iopscience.iop.org/article/10.1209/0295-5075/123/20002/meta. 109 [156] Palmieri, L., Jensen, H. J. The Forest Fire Model: The Subtleties of Criticality and Scale Invariance. Frontiers in Physics, 8, 2020. URL https: //www.frontiersin.org/articles/10.3389/fphy.2020.00257. 109 [157] Kinouchi, O., Pazzini, R., Copelli, M. Mechanisms of Self-Organized Quasicriticality in Neuronal Network Models. Frontiers in Physics, 8, 2020. URL https://www.frontiersin.org/articles/10.3389/fphy.2020.583213. 109 [158] Zeraati, R., Priesemann, V., Levina, A. Self-Organization Toward Criticality by Synaptic Plasticity. Frontiers in Physics, 9, 2021. URL https://www.frontiersin.org/articles/10.3389/fphy.2021.619661. [159] Meisel, C., Gross, T. Adaptive self-organization in a realistic neural network model. Physical Review E, 80 (6), 061917, dic. 2009. URL https://link.aps.org/doi/10.1103/PhysRevE.80.061917. [160] Droste, F., Do, A.-L., Gross, T. Analytical investigation of self-organized criticality in neural networks. Journal of The Royal Society Interface, 10 (78), 20120558, ene. 2013. URL https://royalsocietypublishing.org/doi/10.1098/rsif.2012.0558. [161] Tetzlaff, C., Okujeni, S., Egert, U., Wörgötter, F., Butz, M. Self-Organized Criticality in Developing Neuronal Networks. PLOS Computational Biology, 6 (12),1–18, dic. 2010. URL https://doi.org/10.1371/journal.pcbi.1001013. [162] Meisel, C., Storch, A., Hallmeyer-Elgner, S., Bullmore, E., Gross, T. Failure of Adaptive Self-Organized Criticality during Epileptic Seizure Attacks. PLOS Computational Biology, 8 (1), e1002312, ene. 2012. URL https://journals. plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002312. [163] Rocha, R. P., Koçillari, L., Suweis, S., Corbetta, M., Maritan, A. Homeostatic plasticity and emergence of functional networks in a whole-brain model at criticality. Scientific Reports, 8 (1), 15682, oct. 2018. URL https: //www.nature.com/articles/s41598-018-33923-9. 50, 172, 173, 174 [164] Plenz, D., Ribeiro, T. L., Miller, S. R., Kells, P. A., Vakili, A., Capek, E. L. Self-Organized Criticality in the Brain. Frontiers in Physics, 9, 2021. URL https://www.frontiersin.org/articles/10.3389/fphy.2021.639389. [165] Levina, A., Herrmann, J. M., Geisel, T. Phase Transitions towards Criticality in a Neural System with Adaptive Interactions. Physical Review Letters, 102 (11), 118110, mar. 2009. URL https://link.aps.org/doi/10.1103/PhysRevLett. 102.118110. [166] Ma, Z., Turrigiano, G. G., Wessel, R., Hengen, K. B. Cortical Circuit Dynamics Are Homeostatically Tuned to Criticality In Vivo. Neuron, 104 (4), 655–664.e4, nov. 2019. URL https://www.cell.com/neuron/abstract/S0896-6273(19) 30737-8. 109, 115 [167] Heiney, K., Huse Ramstad, O., Fiskum, V., Christiansen, N., Sandvig, A., Nichele, S., et al. Criticality, Connectivity, and Neural Disorder: A Multifaceted Approach to Neural Computation. Frontiers in Computational Neuroscience, 15, 2021. URL https://www.frontiersin.org/articles/10.3389/fncom.2021.611183. 110, 114 [168] Osorio, I., Frei, M. G., Sornette, D., Milton, J., Lai, Y.-C. Epileptic seizures: Quakes of the brain? Physical Review E, 82 (2), 021919, ago. 2010. URL https: //link.aps.org/doi/10.1103/PhysRevE.82.021919. 110 [169] Witton, C., Sergeyev, S. V., Turitsyna, E. G., Furlong, P. L., Seri, S., Brookes, M., et al. Rogue bioelectrical waves in the brain: the Hurst exponent as a potential measure for presurgical mapping in epilepsy. Journal of Neural Engineering, 16 (5), 056019, ago. 2019. URL https://dx.doi.org/10.1088/1741-2552/ ab225e. [170] Kramer, M. A., Truccolo, W., Eden, U. T., Lepage, K. Q., Hochberg, L. R., Eskandar, E. N., et al. Human seizures self-terminate across spatial scales via a critical transition. Proceedings of the National Academy of Sciences of the United States of America, 109 (51), 21116–21121, dic. 2012. [171] Moosavi, S. A., Truccolo, W. Criticality in probabilistic models of spreading dynamics in brain networks: Epileptic seizures. PLOS Computational Biology, 19 (2), e1010852, feb. 2023. URL https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010852. [172] Meisel, C. Antiepileptic drugs induce subcritical dynamics in human cortical networks. Proceedings of the National Academy of Sciences, 117 (20), 11118–11125, mayo 2020. URL https://www.pnas.org/doi/full/10.1073/pnas.1911461117. 110 [173] Stam, C. J., Montez, T., Jones, B. F., Rombouts, S. A. R. B., van der Made, Y., Pijnenburg, Y. A. L., et al. Disturbed fluctuations of resting state EEG synchronization in Alzheimer’s disease. Clinical Neurophysiology, 116 (3), 708–715, mar. 2005. URL https://www.sciencedirect.com/science/article/pii/S1388245704003797. 110 [174] Montez, T., Poil, S.-S., Jones, B. F., Manshanden, I., Verbunt, J. P. A., van Dijk, B. W., et al. Altered temporal correlations in parietal alpha and prefrontal theta oscillations in early-stage Alzheimer disease. Proceedings of the National Academy of Sciences, 106 (5), 1614–1619, feb. 2009. URL https://www.pnas.org/doi/full/10.1073/pnas.0811699106. [175] Vyšata, O., Procházka, A., Mareš, J., Rusina, R., Pazdera, L., Vališ, M., et al. Change in the Characteristics of EEG Color Noise in Alzheimer’s Disease. Clinical EEG and Neuroscience, 45 (3), 147–151, jul. 2014. URL https://doi.org/10.1177/1550059413491558. 110 [176] Hohlefeld, F. U., Huebl, J., Huchzermeyer, C., Schneider, G.-H., Schönecker, T., Kühn, A. A., et al. Long-range temporal correlations in the subthalamic nucleus of patients with Parkinson’s disease. European Journal of Neuroscience, 36 (6), 2812–2821, 2012. URL https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1460-9568.2012.08198.x. 110 [177] Herrojo Ruiz, M., Hong, S. B., Hennig, H., Altenmüller, E., Kühn, A. A. Longrange correlation properties in timing of skilled piano performance: the influence of auditory feedback and deep brain stimulation. Frontiers in Psychology, 5, 1030, sep. 2014. URL https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4174744/. [178] West, T., Farmer, S., Berthouze, L., Jha, A., Beudel, M., Foltynie, T., et al. The Parkinsonian Subthalamic Network: Measures of Power, Linear, and Non-linear Synchronization and their Relationship to L-DOPA Treatment and OFF State Motor Severity. Frontiers in Human Neuroscience, 10, 517, oct. 2016. URL https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5078477/. 110 [179] Rocha, R. P., Koçillari, L., Suweis, S., De Filippo De Grazia, M., de Schotten, M. T., Zorzi, M., et al. Recovery of neural dynamics criticality in personalized whole-brain models of stroke. Nature Communications, 13 (1), 3683, jun. 2022. URL https://www.nature.com/articles/s41467-022-30892-6. 110, 213 [180] Alonso, L., Proekt, A., Schwartz, T., Pryor, K., Cecchi, G., Magnasco, M. Dynamical criticality during induction of anesthesia in human ECoG recordings. Frontiers in Neural Circuits, 8, 2014. URL https://www.frontiersin.org/articles/10.3389/fncir.2014.00020. 110 [181] Thiery, T., Lajnef, T., Combrisson, E., Dehgan, A., Rainville, P., Mashour, G. A., et al. Long-range temporal correlations in the brain distinguish conscious wakefulness from induced unconsciousness. NeuroImage, 179, 30–39, oct. 2018. URL https://www.sciencedirect.com/science/article/pii/S1053811918304956. [182] Liu, X., Ward, B. D., Binder, J. R., Li, S.-J., Hudetz, A. G. Scale-Free Functional Connectivity of the Brain Is Maintained in Anesthetized Healthy Participants but Not in Patients with Unresponsive Wakefulness Syndrome. PLOS ONE, 9 (3), e92182, mar. 2014. URL https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0092182. 110 [183] Bocaccio, H., Pallavicini, C., Castro, M. N., Sánchez, S. M., De Pino, G., Laufs, H., et al. The avalanche-like behaviour of large scale haemodynamic activity from wakefulness to deep sleep. Journal of The Royal Society Interface, 16 (158), 20190262, sep. 2019. URL https://royalsocietypublishing.org/doi/10.1098/rsif.2019.0262. 110 [184] Meisel, C., Bailey, K., Achermann, P., Plenz, D. Decline of long-range temporal correlations in the human brain during sustained wakefulness. Scientific Reports, 7 (1), 11825, sep. 2017. URL https://www.nature.com/articles/s41598-017-12140-w. 110 [185] Colombo, M. A., Wei, Y., Ramautar, J. R., Linkenkaer-Hansen, K., Tagliazucchi, E., Van Someren, E. J. W. More Severe Insomnia Complaints in People with Stronger Long-Range Temporal Correlations in Wake Resting-State EEG. Frontiers in Physiology, 7, 2016. URL https://www.frontiersin.org/articles/10.3389/fphys.2016.00576. 110 [186] Lo, C.-C., Bartsch, R. P., Ivanov, P. C. Asymmetry and basic pathways in sleep-stage transitions. Europhysics Letters, 102 (1), 10008, abr. 2013. URL https://dx.doi.org/10.1209/0295-5075/102/10008. 110 [187] Padilla, N., Saenger, V. M., van Hartevelt, T. J., Fernandes, H. M., Lennartsson, F., Andersson, J. L. R., et al. Breakdown of Whole-brain Dynamics in Pretermborn Children. Cerebral Cortex, 30 (3), 1159–1170, mar. 2020. URL https: //doi.org/10.1093/cercor/bhz156. 110 [188] Smit, D. J. A., Geus, E. J. C. d., Nieuwenhuijzen, M. E. v. d., Beijsterveldt, C. E. M. v., Baal, G. C. M. v., Mansvelder, H. D., et al. Scale-Free Modulation of Resting-State Neuronal Oscillations Reflects Prolonged Brain Maturation in Humans. Journal of Neuroscience, 31 (37), 13128–13136, sep. 2011. URL https: //www.jneurosci.org/content/31/37/13128. [189] Mareš, J., Vyšata, O., Procházka, A., Vališ, M. Age-dependent complex noise fluctuations in the brain. Physiological Measurement, 34 (10), 1269, sep. 2013. URL https://dx.doi.org/10.1088/0967-3334/34/10/1269. 110 [190] Ezaki, T., Fonseca dos Reis, E., Watanabe, T., Sakaki, M., Masuda, N. Closer to critical resting-state neural dynamics in individuals with higher fluid intelligence. Communications Biology, 3 (1), 1–9, feb. 2020. URL https://www.nature.com/ articles/s42003-020-0774-y. 110 [191] Jia, H., Li, Y., Yu, D. Attenuation of long-range temporal correlations of neuronal oscillations in young children with autism spectrum disorder. NeuroImage: Clini298cal, 20, 424–432, ene. 2018. URL https://www.sciencedirect.com/science/article/pii/S2213158218302535. [192] Ouyang, G., Hildebrandt, A., Schmitz, F., Herrmann, C. S. Decomposing alpha and 1/f brain activities reveals their differential associations with cognitive processing speed. NeuroImage, 205, 116304, ene. 2020. URL https://www.sciencedirect.com/science/article/pii/S105381191930895X. [193] Tinker, J., Velazquez, J. L. P. Power law scaling in synchronization of brain signals depends on cognitive load. Frontiers in Systems Neuroscience, 8, 73, mayo 2014. URL https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4013475/. [194] Dimitriadis, S. I., Laskaris, N. A., Simos, P. G., Micheloyannis, S., Fletcher, J. M., Rezaie, R., et al. Altered temporal correlations in resting-state connectivity fluctuations in children with reading difficulties detected via MEG. NeuroImage, 83, 307–317, dic. 2013. URL https://www.sciencedirect.com/science/article/pii/S1053811913006733. 110 [195] Zhigalov, A., Kaplan, A., Palva, J. M. Modulation of critical brain dynamics using closed-loop neurofeedback stimulation. Clinical Neurophysiology, 127 (8), 2882–2889, ago. 2016. URL https://www.sciencedirect.com/science/article/pii/S1388245716300578. 110 [196] Tagliazucchi, E., Carhart-Harris, R., Leech, R., Nutt, D., Chialvo, D. R. Enhanced repertoire of brain dynamical states during the psychedelic experience. Human Brain Mapping, 35 (11), 5442–5456, 2014. URL https://onlinelibrary. wiley.com/doi/abs/10.1002/hbm.22562. 110 [197] Gärtner, M., Irrmischer, M., Winnebeck, E., Fissler, M., Huntenburg, J. M., Schroeter, T. A., et al. Aberrant Long-Range Temporal Correlations in Depression Are Attenuated after Psychological Treatment. Frontiers in Human Neuroscience, 11, 340, jun. 2017. URL https://www.ncbi.nlm.nih.gov/pmc/ articles/PMC5488389/. 110 [198] Moran, J. K., Michail, G., Heinz, A., Keil, J., Senkowski, D. Long-Range Temporal Correlations in Resting State Beta Oscillations are Reduced in Schizophrenia. Frontiers in Psychiatry, 10, 2019. URL https://www.frontiersin.org/articles/10.3389/fpsyt.2019.00517. 110 [199] Tolkunov, D., Rubin, D., Mujica-Parodi, L. Power spectrum scale invariance quantifies limbic dysregulation in trait anxious adults using fMRI: Adapting methods optimized for characterizing autonomic dysregulation to neural dynamic time series. NeuroImage, 50 (1), 72–80, mar. 2010. URL https://www.sciencedirect.com/science/article/pii/S1053811909013093. 110 [200] Ros, T., J. Baars, B., Lanius, R. A., Vuilleumier, P. Tuning pathological brain oscillations with neurofeedback: a systems neuroscience framework. Frontiers in Human Neuroscience, 8, 1008, dic. 2014. URL https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4270171/. 110 [201] Dickman, R., Muñoz, M. A., Vespignani, A., Zapperi, S. Paths to self-organized criticality. Brazilian Journal of Physics, 30, 27–41, mar. 2000. URL http://www.scielo.br/j/bjp/a/zybV3FQw8gSGCcP6H7hcmGm/?lang=en. 111 [202] Touboul, J., Destexhe, A. Can Power-Law Scaling and Neuronal Avalanches Arise from Stochastic Dynamics? PLOS ONE, 5 (2), e8982, feb. 2010.URL https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0008982. 112 [203] Arnold, B. C. Pareto and Generalized Pareto Distributions. En: D. Chotikapanich (ed.) Modeling Income Distributions and Lorenz Curves, Economic Studies in Equality, Social Exclusion and Well-Being, págs. 119–145. New York, NY: Springer, 2008. URL https://doi.org/10.1007/978-0-387-72796-7_7. 112 [204] Newman, M. Power laws, Pareto distributions and Zipf’s law. Contemporary Physics, 46 (5), 323–351, sep. 2005. URL https://doi.org/10.1080/00107510500052444. 112 [205] Goldstein, M. L., Morris, S. A., Yen, G. G. Problems with fitting to the powerlaw distribution. The European Physical Journal B - Condensed Matter and Complex Systems, 41 (2), 255–258, sep. 2004. URL https://doi.org/10.1140/epjb/e2004-00316-5. 112 [206] Priesemann, V., Shriki, O. Can a time varying external drive give rise to apparent criticality in neural systems? PLOS Computational Biology, 14 (5), e1006081, mayo 2018. URL https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006081. 112 [207] Marković, D., Gros, C. Power laws and self-organized criticality in theory and nature. Physics Reports, 536 (2), 41–74, mar. 2014. URL https://www. sciencedirect.com/science/article/pii/S0370157313004298. 112, 113 [208] Sornette, D. Critical Phenomena in Natural Sciences. Springer Series in Synergetics. Berlin/Heidelberg: Springer-Verlag, 2006. URL http://link.springer. com/10.1007/3-540-33182-4. [209] Beggs, J. M., Timme, N. Being Critical of Criticality in the Brain. Frontiers in Physiology, 3, 163, jun. 2012. URL https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3369250/. 112 [210] Roberts, J. A., Boonstra, T. W., Breakspear, M. The heavy tail of the human brain. Current Opinion in Neurobiology, 31, 164–172, abr. 2015. URL https://www.sciencedirect.com/science/article/pii/S0959438814002141. 113 [211] Clauset, A., Shalizi, C. R., Newman, M. E. J. Power-Law Distributions in Empirical Data. SIAM Review, 51 (4), 661–703, nov. 2009. URL https: //epubs.siam.org/doi/10.1137/070710111. 113, 117, 192 [212] Klaus, A., Yu, S., Plenz, D. Statistical Analyses Support Power Law Distributions Found in Neuronal Avalanches. PLOS ONE, 6 (5), e19779, mayo 2011. URL https://journals.plos.org/plosone/article?id=10.1371/ journal.pone.0019779. 113 [213] Ribeiro, T. L., Ribeiro, S., Belchior, H., Caixeta, F., Copelli, M. Undersampled Critical Branching Processes on Small-World and Random Networks Fail to Reproduce the Statistics of Spike Avalanches. PLOS ONE, 9 (4), e94992, abr. 2014. URL https://journals.plos.org/plosone/article?id=10.1371/ journal.pone.0094992. 113 [214] Pasquale, V., Massobrio, P., Bologna, L. L., Chiappalone, M., Martinoia, S. Selforganization and neuronal avalanches in networks of dissociated cortical neurons. Neuroscience, 153 (4), jun. 2008. URL https://www.sciencedirect.com/science/article/pii/S0306452208004028. 114 [215] Kanders, K., Lorimer, T., Stoop, R. Avalanche and edge-of-chaos criticality do not necessarily co-occur in neural networks. Chaos: An Interdisciplinary Journal of Nonlinear Science, 27 (4), 047408, abr. 2017. URL https://aip.scitation.org/doi/10.1063/1.4978998. 115 [216] Harris, T. E. The Theory of Branching Processes. Springer Berlin Heidelberg,1963. 115 [217] Sethna, J. P., Dahmen, K. A., Myers, C. R. Crackling noise. Nature, 410 (6825),242–250, mar. 2001. URL https://www.nature.com/articles/35065675. 115 [218] Friedman, N., Ito, S., Brinkman, B. A. W., Shimono, M., DeVille, R. E. L., Dahmen, K. A., et al. Universal Critical Dynamics in High Resolution Neuronal Avalanche Data. Physical Review Letters, 108 (20), 208102, mayo 2012. URL https://link.aps.org/doi/10.1103/PhysRevLett.108.208102. 115 [219] Miller, S. R., Yu, S., Plenz, D. The scale-invariant, temporal profile of neuronal avalanches in relation to cortical γ–oscillations. Scientific Reports, 9 (1), 16403, nov. 2019. URL https://www.nature.com/articles/s41598-019-52326-y. 115 [220] Levina, A., Priesemann, V. Subsampling scaling. Nature Communications, 8 (1), 15140, mayo 2017. URL https://www.nature.com/articles/ncomms15140.116 [221] Wilting, J., Priesemann, V. Inferring collective dynamical states from widely unobserved systems. Nature Communications, 9 (1), 2325, jun. 2018. URL https://www.nature.com/articles/s41467-018-04725-4. 116 [222] Hardstone, R., Poil, S.-S., Schiavone, G., Jansen, R., Nikulin, V., Mansvelder, H., et al. Detrended Fluctuation Analysis: A Scale-Free View on Neuronal Oscillations. Frontiers in Physiology, 3, 2012. URL https://www.frontiersin.org/articles/10.3389/fphys.2012.00450. 116 [223] Scheffer, M., Bascompte, J., Brock, W. A., Brovkin, V., Carpenter, S. R., Dakos, V., et al. Early-warning signals for critical transitions. Nature, 461 (7260), 53–59, sep. 2009. URL https://www.nature.com/articles/nature08227. 116 [224] Wang, R., Dearing, J. A., Langdon, P. G., Zhang, E., Yang, X., Dakos, V., et al. Flickering gives early warning signals of a critical transition to a eutrophic lake state. Nature, 492 (7429), 419–422, dic. 2012. URL https://www.nature.com/articles/nature11655. 116 [225] Li, X., Polygiannakis, J., Kapiris, P., Peratzakis, A., Eftaxias, K., Yao, X. Fractal spectral analysis of pre-epileptic seizures in terms of criticality. Journal of Neural Engineering, 2 (2), 11, mar. 2005. URL https://iopscience.iop.org/article/10.1088/1741-2560/2/2/002/meta. 117 [226] Bédard, C., Kröger, H., Destexhe, A. Does the $1/f$ Frequency Scaling of Brain Signals Reflect Self-Organized Critical States? Physical Review Letters, 97 (11), 118102, sep. 2006. URL https://link.aps.org/doi/10.1103/PhysRevLett. 97.118102. 117 [227] Botcharova, M., Farmer, S. F., Berthouze, L. Power-law distribution of phaselocking intervals does not imply critical interaction. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics, 86 (5 Pt 1), 051920, nov. 2012. 117 [228] Galinsky, V. L., Frank, L. R. Neuronal avalanches: Sandpiles of self-organized criticality or critical dynamics of brain waves? Frontiers of Physics, 18 (4), 45301, mar. 2023. URL https://doi.org/10.1007/s11467-023-1273-7. 117 [229] Yu, S., Klaus, A., Yang, H., Plenz, D. Scale-Invariant Neuronal Avalanche Dynamics and the Cut-Off in Size Distributions. PLOS ONE, 9 (6), e99761, jun. 2014. URL https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0099761. 117 [230] Dehghani, N., Hatsopoulos, N. G., Haga, Z. D., Parker, R. A., Greger, B., Halgren, E., et al. Avalanche Analysis from Multielectrode Ensemble Recordings in Cat, Monkey, and Human Cerebral Cortex during Wakefulness and Sleep. Frontiers in Physiology, 3, 302, ago. 2012. 117 [231] Çiftçi, K. Synaptic noise facilitates the emergence of self-organized criticality in the Caenorhabditis elegans neuronal network. Network (Bristol, England), 29 (1-4), 1–19, 2018. 118 [232] Yin, J., Pei, Z., Gao, M. C. Neural network-based order parameter for phase transitions and its applications in high-entropy alloys. Nature Computational Science, 1 (10), 686–693, oct. 2021. URL https://www.nature.com/articles/s43588-021-00139-3. 122 [233] Kleman, M., Lavrentovich, O. D. The Order Parameter: Amplitude and Phase. En: Soft Matter Physics: An Introduction, Partially Ordered Systems, págs. 76–104. New York, NY: Springer, 2003. URL https://doi.org/10.1007/978-0-387-21759-8_3. 122 [234] Torquato, S. Percolation and Clustering. En: S. Torquato (ed.) Random Heterogeneous Materials: Microstructure and Macroscopic Properties, Interdisciplinary Applied Mathematics, págs. 210–233. New York, NY: Springer, 2002. URL https://doi.org/10.1007/978-1-4757-6355-3_9. 122, 130 [235] Li, M., Liu, R.-R., Lü, L., Hu, M.-B., Xu, S., Zhang, Y.-C. Percolation on complex networks: Theory and application. Physics Reports, 907, 1–68, abr. 2021. URL https://www.sciencedirect.com/science/article/pii/S0370157320304269. 122, 128 [236] Berkowitz, B., Ewing, R. P. Percolation Theory and Network Modeling Applications in Soil Physics. Surveys in Geophysics, 19 (1), 23–72, ene. 1998. URL https://doi.org/10.1023/A:1006590500229. 123, 129 [237] Stauffer, D. Scaling theory of percolation clusters. Physics Reports, 54 (1), 1– 74, jul. 1979. URL https://www.sciencedirect.com/science/article/pii/0370157379900607. 123, 133, 139, 234 [238] Dorogovtsev, S. N., Goltsev, A. V., Mendes, J. F. F. Critical phenomena in complex networks. Reviews of Modern Physics, 80 (4), 1275–1335, oct. 2008. URL https://link.aps.org/doi/10.1103/RevModPhys.80.1275. 124 [239] Rong, Q., Zhang, J., Sun, X., Wandelt, S. On the estimation of percolation thresholds for real networks. Chaos, Solitons & Fractals, 158, 111968, mayo 2022. URL https://www.sciencedirect.com/science/article/pii/S0960077922001783. [240] Newman, M. Networks. Second edition, new to this edition:, second edition, new to this edition: edón. Oxford, New York: Oxford University Press, 2018. [241] ariel yadin. an introduction to percolation. https://www.math.bgu.ac.il/~yadina/percolation.pdf, 2020. [242] Hugo Duminil-Copin. Introduction to bernoulli percolation. https://www.ihes. fr/~duminil/publi/2017percolation.pdf, 2018. [243] Hammersley, J. M., Welsh, D. J. A. Percolation theory and its ramifications. Contemporary Physics, 21 (6), 593–605, nov. 1980. URL https://doi.org/10.1080/00107518008210661. [244] Bunde, A., Havlin, S. Fractals and Disordered Systems. Springer Science & Business Media, 2012. 125, 139, 143 [245] Grimmett, G. Probability on Graphs: Random Processes on Graphs and Lattices. Institute of Mathematical Statistics Textbooks, 2a edón. Cambridge: Cambridge University Press, 2018. URL https://www.cambridge.org/core/books/probability-on-graphs/22F3EFAA32AEF9E161C263FBDE4F71BE. [246] Albert, R., Barabási, A.-L. Statistical mechanics of complex networks. Reviews of Modern Physics, 74 (1), 47–97, ene. 2002. URL https://link.aps.org/doi/10.1103/RevModPhys.74.47. [247] Boccaletti, S., Bianconi, G., Criado, R., del Genio, C., Gómez-Gardeñes, J., Romance, M., et al. The structure and dynamics of multilayer networks. Physics Reports, 544 (1), 1–122, nov. 2014. URL https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7332224/. 124 [248] Hunt, A., Ewing, R., Ghanbarian, B. Percolation Theory for Flow in Porous Media, tomo 880 de Lecture Notes in Physics. Cham: Springer International Publishing, 2014. URL https://link.springer.com/10.1007/978-3-319-03771-4. 124 [249] Flory, P. J. Molecular Size Distribution in Three Dimensional Polymers. I. Gelation1. Journal of the American Chemical Society, 63 (11), 3083–3090, nov. 1941. URL https://doi.org/10.1021/ja01856a061. 124 [250] Stockmayer, W. H. Theory of Molecular Size Distribution and Gel Formation in Branched-Chain Polymers. The Journal of Chemical Physics, 11 (2), 45–55, dic. 2004. URL https://doi.org/10.1063/1.1723803. 124 [251] Chalupa, J., Leath, P. L., Reich, G. R. Bootstrap percolation on a Bethe lattice. Journal of Physics C: Solid State Physics, 12 (1), L31, ene. 1979. URL https://dx.doi.org/10.1088/0022-3719/12/1/008. 125 [252] Rosso, M., Gouyet, J. F., Sapoval, B. Gradient percolation in three dimensions and relation to diffusion fronts. Physical Review Letters, 57 (25), 3195–3198, dic. 1986. URL https://link.aps.org/doi/10.1103/PhysRevLett.57.3195. 125 [253] Chandler, R., Koplik, J., Lerman, K., Willemsen, J. F. Capillary displacement and percolation in porous media. Journal of Fluid Mechanics, 119, 249–267, jun. 1982. 125 [254] Nickel, B., Wilkinson, D. Invasion Percolation on the Cayley Tree: Exact Solution of a Modified Percolation Model. Physical Review Letters, 51 (2), 71–74, jul. 1983. URL https://link.aps.org/doi/10.1103/PhysRevLett.51.71. [255] Wilkinson, D., Willemsen, J. F. Invasion percolation: a new form of percolation theory. Journal of Physics A: Mathematical and General, 16 (14), 3365, oct. 1983. URL https://dx.doi.org/10.1088/0305-4470/16/14/028. 125 [256] Aizenman, M. On the number of incipient spanning clusters. Nuclear Physics B, 485 (3), 551–582, feb. 1997. URL https://www.sciencedirect.com/science/article/pii/S0550321396006268. 125 [257] Novozhilov, A. S. Mathematics of networks, 2013. URL: https://math.mit.edu/classes/18.783/2015/lectures.html. 125 [258] Fisher, M. E., Essam, J. W. Some Cluster Size and Percolation Problems. Journal of Mathematical Physics, 2 (4), 609–619, dic. 2004. URL https://doi.org/10.1063/1.1703745. 125 [259] Kesten, H. The critical probability of bond percolation on the square lattice equals 1/2. Communications in Mathematical Physics, 74 (1), 41–59, feb. 1980. URL https://doi.org/10.1007/BF01197577. 126 [260] Wierman, J. C. A bond percolation critical probability determination based on the star-triangle transformation. Journal of Physics A: Mathematical and General, 17 (7), 1525, mayo 1984. URL https://dx.doi.org/10.1088/0305-4470/17/7/020. 126 [261] Grimmett, G. R. Percolation. Springer Science & Business Media, 2013. Google-Books-ID: Vnz1CAAAQBAJ. 126, 137 [262] Aharony, D. S., Ammon. Introduction To Percolation Theory: Second Edition.2a edón. London: Taylor & Francis, 2017. 128, 134 [263] Beffara, V., Sidoravicius, V. Percolation Theory. En: J.-P. Françoise, G. L. Naber,T. S. Tsun (eds.) Encyclopedia of Mathematical Physics, págs. 21–28. Oxford: Academic Press, 2006. URL https://www.sciencedirect.com/science/article/pii/B0125126662002029. 130 [264] Broadbent, S. R., Hammersley, J. M. Percolation processes: I. Crystals and mazes. Mathematical Proceedings of the Cambridge Philosophical Society, 53 (3), 629–641, jul. 1957. URL https://www.cambridge.org/core/journals/mathematical-proceedings-of-the-cambridge-philosophical-society/article/abs/percolation-processes/C00CC4943F48228F8AC8031092FE84EC.131 [265] Barrat, A., Barthélemy, M., Vespignani, A. Dynamical Processes on ComplexNetworks. Cambridge: Cambridge University Press, 2008. URL https://www. cambridge.org/core/books/dynamical-processes-on-complex-networks/D0173F07E0F05CEE9CF7A6BDAF48E9FC. 134, 139 [266] Saberi, A. A. Recent advances in percolation theory and its applications. Physics Reports, 578, 1–32, mayo 2015. URL https://www.sciencedirect.com/science/article/pii/S0370157315002008. 135 [267] Djordjevic, Z. V., Stanley, H. E. Scaling properties of the perimeter distribution for lattice animals, perc |
| Materias: | Física > Sistemas complejos Ingeniería > Inteligencia artificial Medicina > Neurociencias |
| Divisiones: | Gcia. de área de Investigación y aplicaciones no nucleares > Gcia. de Física > Física médica |
| Código ID: | 1256 |
| Depositado Por: | Tamara Cárcamo |
| Depositado En: | 12 Sep 2024 15:34 |
| Última Modificación: | 12 Sep 2024 15:34 |
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