Для цитирования:
Нажесткин И. А., Сварник О. Е. Теория интегрированной информации и её применение к анализу нейронной активности головного мозга // Известия вузов. ПНД. 2023. Т. 31, вып. 2. С. 180-201. DOI: 10.18500/0869-6632-003033, EDN: JSTBXP
Теория интегрированной информации и её применение к анализу нейронной активности головного мозга
Цель настоящего обзора — рассмотреть возможность применения теории интегрированной информации к анализу нейронной активности головного мозга. Ранее было показано, что коэффициент интегрированной информации Ф отражает степень динамической сложности системы и способен предсказывать степень успешности её работы, определяемую классическими наблюдаемыми критериями. Исходя из этого, становится актуальным вопрос относительно применения теории интегрированной информации к анализу изменений в спайковой активности головного мозга в процессе приобретения нового опыта.
Заключение. Были рассмотрены основы теории интегрированной информации и её возможное применение в нейробиологии для исследования процесса приобретения нового опыта. Показано, что коэффициент интегрированной информации Ф является метрикой, способной оценить динамическую сложность нейронных сетей головного мозга, увеличивающуюся с приобретением опыта. Предложены методы, позволяющие на практике вычислить значение коэффициента Ф для данных нейронной активности.
- Tononi G. An information integration theory of consciousness // BMC Neuroscience. 2004. Vol. 5. P. 42. DOI: 10.1186/1471-2202-5-42.
- Tononi G. Consciousness as integrated information: a provisional manifesto // The Biological Bulletin. 2008. Vol. 215, no. 3. P. 216–242. DOI: 10.2307/25470707.
- Balduzzi D., Tononi G. Integrated information in discrete dynamical systems: motivation and theoretical framework // PLoS Comput. Biol. 2008. Vol. 4, no. 6. P. e1000091. DOI: 10.1371/ journal.pcbi.1000091.
- Oizumi M., Albantakis L., Tononi G. From the phenomenology to the mechanisms of consciousness: Integrated information theory 3.0 // PLoS Comput. Biol. 2014. Vol. 10, no. 5. P. e1003588. DOI: 10.1371/journal.pcbi.1003588.
- Tononi G., Koch C. Consciousness: here, there and everywhere? // Phil. Trans. R. Soc. B. 2015. Vol. 370, no. 1668. P. 20140167. DOI: 10.1098/rstb.2014.0167.
- Massimini M., Ferrarelli F., Huber R., Esser S. K., Singh H., Tononi G. Breakdown of cortical effective connectivity during sleep // Science. 2005. Vol. 309, no. 5744. P. 2228–2232. DOI: 10.1126/science.1117256.
- Alkire M. T., Hudetz A. G., Tononi G. Consciousness and anesthesia // Science. 2008. Vol. 322, no. 5903. P. 876–880. DOI: 10.1126/science.1149213.
- Gosseries O., Di H., Laureys S., Boly M. Measuring consciousness in severely damaged brains // Annual Review of Neuroscience. 2014. Vol. 37. P. 457–478. DOI: 10.1146/annurev-neuro-062012- 170339.
- Casali A. G., Olivia Gosseries O., Rosanova M., Boly M., Sarasso S., Casali K. R., Casarotto S., Bruno M.-A., Laureys S., Tononi G., Massimini M. A theoretically based index of consciousness independent of sensory processing and behavior // Science Translational Medicine. 2013. Vol. 5, no. 198. P. 198ra105. DOI: 10.1126/scitranslmed.3006294.
- King J. R., Sitt J. D., Faugeras F., Rohaut B., Karoui I. E., Cohen L., Naccache L., Dehaene S. Information sharing in the brain indexes consciousness in noncommunicative patients // Current Biology. 2013. Vol. 23, no. 19. P. 1914–1919. DOI: 10.1016/j.cub.2013.07.075.
- Searle J. R. Can information theory explain consciousness? [Electronic resource] // The New York Review of Books. 10 January 2013. Available from: https://www.nybooks.com/articles/2013/01/10/can-information-theory-expla....
- Barrett A. B., Mediano P. A. M. The Phi measure of integrated information is not well-defined for general physical systems // Journal of Consciousness Studies. 2019. Vol. 26, no. 1–2. P. 11–20.
- Edlund J. A., Chaumont N., Hintze A., Koch C., Tononi G., Adami C. Integrated information increases with fitness in the evolution of animats // PLoS Comput. Biol. 2011. Vol. 7, no. 10. P. e1002236. DOI: 10.1371/journal.pcbi.1002236.
- Engel D., Malone T. W. Integrated information as a metric for group interaction // PLoS ONE. 2018. Vol. 13, no. 10. P. e0205335. DOI: 10.1371/journal.pone.0205335.
- Albantakis L., Tononi G. The intrinsic cause-effect power of discrete dynamical systems–from elementary cellular automata to adapting animats // Entropy. 2015. Vol. 17, no. 8. P. 5472–5502. DOI: 10.3390/e17085472.
- Niizato T., Sakamoto K., Mototake Y.-I., Murakami H., Tomaru T., Hoshika T., Fukushima T. Finding continuity and discontinuity in fish schools via integrated information theory // PLoS ONE. 2020. Vol. 15, no. 2. P. e0229573. DOI: 10.1371/journal.pone.0229573.
- Fujii K., Kanazawa H., Kuniyoshi Y. Spike timing dependent plasticity enhances integrated information at the EEG level: A large-scale brain simulation experiment // In: 2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). 19-22 August 2019, Oslo, Norway. New York: IEEE, 2019. P. 137–142. DOI: 10.1109/DEVLRN.2019.8850724.
- Niessing J., Friedrich R. W. Olfactory pattern classification by discrete neuronal network states // Nature. 2010. Vol. 465, no. 7294. P. 47–52. DOI: 10.1038/nature08961.
- Mazzucato L., Fontanini A., La Camera G. Dynamics of multistable states during ongoing and evoked cortical activity // Journal of Neuroscience. 2015. Vol. 35, no. 21. P. 8214–8231. DOI: 10.1523/JNEUROSCI.4819-14.2015.
- Isler J. R., Stark R. I., Grieve P. G., Welch M. G., Myers M. M. Integrated information in the EEG of preterm infants increases with family nurture intervention, age, and conscious state // PLoS ONE. 2018. Vol. 13, no. 10. P. e0206237. DOI:10.1371/journal.pone.0206237.
- Nazhestkin I., Svarnik O. Different approximation methods for calculation of integrated information coefficient in the brain during instrumental learning // Brain Sciences. 2022. Vol. 12, no. 5. P. 596. DOI: 10.3390/brainsci12050596.
- Nazhestkin I. A., Svarnik O. E. Integrated information coefficient estimated from neuronal activity in hippocampus-amygdala complex of rats as a measure of learning success // Journal of Integrative Neuroscience. 2022. Vol. 21, no. 5. P. 128. DOI: 10.31083/j.jin2105128.
- Sporns O. Network attributes for segregation and integration in the human brain // Current Opinion in Neurobiology. 2013. Vol. 23, no. 2. P. 162–171. DOI: 10.1016/j.conb.2012.11.015.
- Brodmann K. Vergleichende Lokalisationslehre der Grosshirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues. Leipzig: Barth, 1909. 324 s.
- Vogt C., Vogt O. Allgemeine ergebnisse unserer hirnforschung. Bd. 25. JA Barth, 1919. 190 s.
- Sporns O., Chialvo D. R., Kaiser M., Hilgetag C. C. Organization, development and function of complex brain networks // Trends in Cognitive Sciences. 2004. Vol. 8, no. 9. P. 418–425. DOI: 10.1016/j.tics.2004.07.008.
- Hilgetag C.-C., Burns G. A. P. S., O Neill M. A., Scannell J. W., Young M. P. Anatomical connectivity defines the organization of clusters of cortical areas in the macaque monkey and the cat // Phil. Trans. R. Soc. Lond. B. 2000. Vol. 355, no. 1393. P. 91–110. DOI: 10.1098/rstb.2000.0551.
- Sporns O., Zwi J. D. The small world of the cerebral cortex // Neuroinformatics. 2004. Vol. 2, no. 2. P. 145–162. DOI: 10.1385/NI:2:2:145.
- Barabasi A.-L., Albert R. Emergence of scaling in random networks // Science. 1999. Vol. 286, no. 5439. P. 509–512. DOI: 10.1126/science.286.5439.509.
- Bassett D. S., Bullmore E. D. Small-world brain networks // The Neuroscientist. 2006. Vol. 12, no. 6. P. 512–523. DOI: 10.1177/1073858406293182.
- Muldoon S. F., Bridgeford E. W., Bassett D. S. Small-world propensity and weighted brain networks // Scientific Reports. 2016. Vol. 6, no. 1. P. 22057. DOI: 10.1038/srep22057.
- Bassett D. S., Bullmore E. T. Small-world brain networks revisited // The Neuroscientist. 2017. Vol. 23, no. 5. P. 499–516. DOI: 10.1177/1073858416667720.
- Liao X., Vasilakos A. V., He Y. Small-world human brain networks: Perspectives and challenges // Neuroscience and Biobehavioral Reviews. 2017. Vol. 77. P. 286–300. DOI: 10.1016/j.neubiorev. 2017.03.018.
- Milgram S. The small-world problem // Psychology Today. 1967. Vol. 1, no. 1. P. 61–67.
- Watts D. J., Strogatz S. H. Collective dynamics of "small-world" networks // Nature. 1998. Vol. 393, no. 6684. P. 440–442. DOI: 10.1038/30918.
- Latora V., Marchiori M. Efficient behavior of small-world networks // Phys. Rev. Lett. 2001. Vol. 87, no. 19. P. 198701. DOI: 10.1103/PhysRevLett.87.198701.
- Latora V., Marchiori M. Economic small-world behavior in weighted networks // The European Physical Journal B – Condensed Matter and Complex Systems. 2003. Vol. 32, no. 2. P. 249–263. DOI: 10.1140/epjb/e2003-00095-5.
- Simard D., Nadeau L., Kroger H. Fastest learning in small-world neural networks // Physics Letters A. 2005. Vol. 336, no. 1. P. 8–15. DOI: 10.1016/j.physleta.2004.12.078.
- Lynn C. W., Bassett D. S. The physics of brain network structure, function and control // Nature Reviews Physics. 2019. Vol. 1, no. 5. P. 318–332. DOI: 10.1038/s42254-019-0040-8.
- Анохин К. В. Когнитом: в поисках фундаментальной нейронаучной теории сознания // Журнал высшей нервной деятельности им. И. П. Павлова. 2021. Т. 71, № 1. С. 39–71. DOI: 10.31857/S0044467721010032.
- Rulkov N. F., Sushchik M. M., Tsimring L. S., Abarbanel H. D. I. Generalized synchronization of chaos in directionally coupled chaotic systems // Phys. Rev. E. 1995. Vol. 51, no. 2. P. 980–994. DOI: 10.1103/PhysRevE.51.980.
- Aertsen A. M., Gerstein G. L., Habib M. K., Palm G. Dynamics of neuronal firing correlation: modulation of "effective connectivity" // Journal of Neurophysiology. 1989. Vol. 61, no. 5. P. 900–917. DOI: 10.1152/jn.1989.61.5.900.
- Boccaletti S., Kurths J., Osipov G., Valladares D. L., Zhou C. S. The synchronization of chaotic systems//Physics Reports. 2002.Vol. 366, no. 1–2. P. 1–101.DOI: 10.1016/S0370-1573(02)00137-0.
- Rosenblum M., Pikovsky A. Synchronization: From pendulum clocks to chaotic lasers and chemical oscillators // Contemporary Physics. 2003. Vol. 44, no. 5. P. 401–416. DOI: 10.1080/ 00107510310001603129.
- Malliaros F. D., Vazirgiannis M. Clustering and community detection in directed networks: A survey // Physics Reports. 2013. Vol. 533, no. 4. P. 95–142. DOI: 10.1016/j.physrep.2013.08.002.
- Garcia J. O., Ashourvan A., Muldoon S., Vettel J. M., Bassett D. S. Applications of community detection techniques to brain graphs: Algorithmic considerations and implications for neural function // Proceedings of the IEEE. 2018. Vol. 106, no. 5. P. 846–867. DOI: 10.1109/JPROC. 2017.2786710.
- van den Heuvel M. P., Hulshoff Pol H. E. Exploring the brain network: A review on restingstate fMRI functional connectivity // European Neuropsychopharmacology. 2010. Vol. 20, no. 8. P. 519–534. DOI: 10.1016/j.euroneuro.2010.03.008.
- Liao W., Ding J., Marinazzo D., Xu Q., Wang Z., Yuan C., Zhang Z., Lu G., Chen H. Small-world directed networks in the human brain: Multivariate Granger causality analysis of resting-state fMRI // NeuroImage. 2011. Vol. 54, no. 4. P. 2683–2694. DOI: 10.1016/j.neuroimage.2010.11.007.
- Stam C. J., van Straaten E. C. W. The organization of physiological brain networks // Clinical Neurophysiology. 2012. Vol. 123, no. 6. P. 1067–1087. DOI: 10.1016/j.clinph.2012.01.011.
- Kahnt T., Chang L. J., Park S. Q., Heinzle J., Haynes J.-D. Connectivity-based parcellation of the human orbitofrontal cortex // Journal of Neuroscience. 2012. Vol. 32, no. 18. P. 6240–6250. DOI: 10.1523/JNEUROSCI.0257-12.2012.
- Yu C., Zhou Y., Liu Y., Jiang T., Dong H., Zhang Y., Walter M. Functional segregation of the human cingulate cortex is confirmed by functional connectivity based neuroanatomical parcellation // NeuroImage. 2011. Vol. 54, no. 4. P. 2571–2581. DOI: 10.1016/j.neuroimage.2010.11.018.
- Kashtan N., Alon U. Spontaneous evolution of modularity and network motifs // Proc. Natl. Acad. Sci. U.S.A. 2005. Vol. 102, no. 39. P. 13773–13778. DOI: 10.1073/pnas.0503610102.
- Lipson H., Pollack J. B., Suh N. P. On the origin of modular variation // Evolution. 2002. Vol. 56, no. 8. P. 1549–1556. DOI: 10.1111/j.0014-3820.2002.tb01466.x.
- Rota G.-C. The number of partitions of a set // The American Mathematical Monthly. 1964. Vol. 71, no. 5. P. 498–504. DOI: 10.1080/00029890.1964.11992270.
- Berend D., Tassa T. Improved bounds on Bell numbers and on moments of sums of random variables // Probability and Mathematical Statistics. 2010. Vol. 30, no. 2. P. 185–205.
- Graham R. L., Knuth D. E., Patashnik O. Concrete Mathematics: A Foundation for Computer Science. Reading, MA, USA: Addison–Wesley, 1994. 657 p.
- Toker D., Sommer F. T. Information integration in large brain networks // PLoS Comput. Biol. 2019. Vol. 15, no. 2. P. e1006807. DOI: 10.1371/journal.pcbi.1006807.
- Mediano P. A. M., Seth A. K., Barrett A. B. Measuring integrated information: Comparison of candidate measures in theory and simulation // Entropy. 2019. Vol. 21, no. 1. P. 17. DOI: 10.3390/ e21010017.
- Blondel V. D., Guillaume J.-L., Lambiotte R., Lefebvre E. Fast unfolding of communities in large networks // Journal of Statistical Mechanics: Theory and Experiment. 2008. Vol. 2008, no. 10. P. P10008. DOI: 10.1088/1742-5468/2008/10/P10008.
- Ng A., Jordan M., Weiss Y. On Spectral Clustering: Analysis and an algorithm // In: Advances in Neural Information Processing Systems 14. NIPS, 2001.
- Faskowitz J., Yan X., Zuo X.-N., Sporns O. Weighted stochastic block models of the human connectome across the life span // Scientific Reports. 2018. Vol. 8, no. 1. P. 12997. DOI: 10.1038/ s41598-018-31202-1.
- Barrett A. B., Seth A. K. Practical measures of integrated information for time-series data // PLoS Comput. Biol. 2011. Vol. 7, no. 1. P. e1001052. DOI: 10.1371/journal.pcbi.1001052.
- Tononi G. Integrated information theory of consciousness: an updated account // Archives Italiennes de Biologie. 2012. Vol. 150, no. 2–3. P. 56–90. DOI: 10.4449/aib.v149i5.1388.
- Griffith V. A principled infotheoretic phi-like measure // arXiv:1401.0978. arXiv Preprint, 2014. 18 p. DOI: 10.48550/arXiv.1401.0978.
- Oizumi M., Tsuchiya N., Amari S. Unified framework for information integration based on information geometry // Proc. Natl. Acad. Sci. U.S.A. 2016. Vol. 113, no. 51. P. 14817–14822. DOI: 10.1073/pnas.1603583113.
- Oizumi M., Amari S., Yanagawa T., Fujii N., Tsuchiya N. Measuring integrated information from the decoding perspective // PLoS Comput. Biol. 2016. Vol. 12, no. 1. P. e1004654. DOI: 10.1371/journal.pcbi.1004654.
- VanRullen R. Perceptual cycles // Trends in Cognitive Sciences. 2016. Vol. 20, no. 10. P. 723–735. DOI: 10.1016/j.tics.2016.07.006.
- Fiebelkorn I. C., Pinsk M. A., Kastner S. A dynamic interplay within the frontoparietal network underlies rhythmic spatial attention // Neuron. 2018. Vol. 99, no. 4. P. 842–853. DOI: 10.1016/ j.neuron.2018.07.038.
- Helfrich R. F., Fiebelkorn I. C., Szczepanski S. M., Lin J. J., Parvizi J., Knight R. T., Kastner S. Neural mechanisms of sustained attention are rhythmic // Neuron. 2018. Vol. 99, no. 4. P. 854–865. DOI: 10.1016/j.neuron.2018.07.032.
- 2009 просмотров