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Measuring information integration
Open accessAbstract
BACKGROUND: To understand the functioning of distributed networks such as the brain, it is important to characterize their ability to integrate information. The paper considers a measure based on effective information, a quantity capturing all causal interactions that can occur between two parts of a system. RESULTS: The capacity to integrate information, or Phi, is given by the minimum amount of effective information that can be exchanged between two complementary parts of a subset. It is shown that this measure can be used to identify the subsets of a system that can integrate information, or complexes. The analysis is applied to idealized neural systems that differ in the organization of their connections. The results indicate that Phi is maximized by having each element develop a different connection pattern with the rest of the complex (functional specialization) while ensuring that a large amount of information can be exchanged across any bipartition of the network (functional integration). CONCLUSION: Based on this analysis, the connectional organization of certain neural architectures, such as the thalamocortical system, are well suited to information integration, while that of others, such as the cerebellum, are not, with significant functional consequences. The proposed analysis of information integration should be applicable to other systems and networks.
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“IIT defines a property of a system called integrated information (Φ) and postulates that this is an exact measure of the quantity of consciousness of any system.”
From The Problem with Phi: A Critique of Integrated Information Theory (2015) · cites this paper as [1]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“In either case, IIT appears to be identifying consciousness with a specific measure of information.”
From The Problem with Phi: A Critique of Integrated Information Theory (2015) · cites this paper as [1]
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“Further simulations show that it is possible to construct a large complex of high Φ by joining smaller complexes through reciprocal connections [8].”
From An information integration theory of consciousness (2004) · cites this paper as [8]
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“This notion of brain complexity-defined as the coexistence of integration and differentiation-was at the core of IIT's original notion of F 89,90 and remains central to the empirical predictions of IIT in its mature form.95”
From An integrative, multiscale view on neural theories of consciousness (2024) · cites this paper as [90]
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“A graph-theoretical analysis indicates that connection matrices yielding the highest values of information integration (Φ = 74 bits) share two key characteristics [8]. First, connection patterns are different for different elements, ensuring functional specialization. Second, all elements can be reached from all other elements of the network, ensuring functional integration.”
From An information integration theory of consciousness (2004) · cites this paper as [8]
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“Tononi’s response is not surprising given that IIT implies a form of panpsychism [1–8].”
From The Problem with Phi: A Critique of Integrated Information Theory (2015) · cites this paper as [1]
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Citations
Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
A Mathematical Theory of Communication
Shannon, C. E. (1948) · The MIT Press eBooks · doi:10.1002/j.1538-7305.1948.tb00917.x · Cited by 50,387
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 37,139
James, W. (1891) · Personality Culture Society · doi:10.1037/11059-000 · Cited by 25,876
Everitt, B. J., Landau, S., & Leese, M. (1974) · Cited by 9,186
The brainweb: phase synchronization and large-scale integration
Varela, F., Lachaux, J.-P., Rodriguez, E., & Martinerie, J. (2001) · Nature Reviews Neuroscience · doi:10.1038/35067550 · Cited by 4,761
The Integrative Action of the Nervous System.
Sherrington, C. (1907) · The Journal of Nervous and Mental Disease · doi:10.1097/00005053-190734120-00021 · Cited by 4,136
(1988) · Cited by 3,656
Dynamic predictions: Oscillations and synchrony in top–down processing
Engel, A. K., Fries, P., & Singer, W. (2001) · Nature Reviews Neuroscience · doi:10.1038/35094565 · Cited by 3,454
Spikes: Exploring the Neural Code
Rieke, F. M., Warland, D., van Steveninck, R. R. D. R., & Bialek, W. (1996) · Medical Entomology and Zoology · Cited by 2,844
Research works printed but not linked: 1
- Zeki S: A vision of the brain. 1993, Oxford ; Boston: Blackwell Scientific Publications
Cited by
Sorted by citations
The economy of brain network organization
Bullmore, E., & Sporns, O. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3214 · Cited by 3,284
Self-citationOrganization, development and function of complex brain networks
SPORNS, O., CHIALVO, D., KAISER, M., & HILGETAG, C. (2004) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2004.07.008 · Cited by 2,055
Self-citationAn information integration theory of consciousness
Tononi, G. (2004) · BMC Neuroscience · doi:10.1186/1471-2202-5-42 · Cited by 1,958
“Further simulations show that it is possible to construct a large complex of high Φ by joining smaller complexes through reciprocal connections [8].”
BackgroundInfluentialSelf-citationConsciousness as integrated information: a provisional manifesto
Tononi, G. (2008) · The Biological Bulletin · doi:10.2307/25470707 · Cited by 1,529
Self-citationConscious Processing and the Global Neuronal Workspace Hypothesis
Mashour, G. A., Roelfsema, P., Changeux, J.-P., & Dehaene, S. (2020) · Neuron · doi:10.1016/j.neuron.2020.01.026 · Cited by 1,164
“IIT is primarily a mathematical theory: it introduces a quantity called F, which quantifies the degree of consciousness of any system, biological or artificial, and suggests that at any given moment, the neural correlate of consciousness is a complex yielding a maximum of irreducible, intrinsic cause–effect power (Oiz…”
BackgroundAdaptive reconfiguration of fractal small-world human brain functional networks
Bassett, D. S., Meyer-Lindenberg, A., Achard, S., Duke, T., & Bullmore, E. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0606005103 · Cited by 814
Sporns, O., & Kötter, R. (2004) · PLoS Biology · doi:10.1371/journal.pbio.0020369 · Cited by 812
Self-citationRethinking segregation and integration: contributions of whole-brain modelling
Deco, G., Tononi, G., Boly, M., & Kringelbach, M. L. (2015) · Nature reviews. Neuroscience · doi:10.1038/nrn3963 · Cited by 658
Self-citationMeasuring consciousness: relating behavioural and neurophysiological approaches
Seth, A. K., Dienes, Z., Cleeremans, A., Overgaard, M., & Pessoa, L. (2008) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2008.04.008 · Cited by 458
“bipartition for which the normalized EI is minimum, the informational ‘weakest link’ [ 65 ].”
Integrated information in discrete dynamical systems: motivation and theoretical framework
Balduzzi, D., & Tononi, G. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000091 · Cited by 419
Self-citationThe neurocognitive bases of human multimodal food perception: Sensory integration
Verhagen, J. V., & Engelen, L. (2006) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2005.11.003 · Cited by 411
A tutorial in connectome analysis: Topological and spatial features of brain networks
Kaiser, M. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.05.025 · Cited by 371
Quantifying causal emergence shows that macro can beat micro
Hoel, E. P., Albantakis, L., & Tononi, G. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1314922110 · Cited by 378
Self-citationSchartner, M., Seth, A., Noirhomme, Q., Boly, M., Bruno, M.-A., Laureys, S., & Barrett, A. (2015) · PLOS ONE · doi:10.1371/journal.pone.0133532 · Cited by 287
Theories and measures of consciousness: An extended framework
Seth, A. K., Izhikevich, E. M., Reeke, G. N., & Edelman, G. M. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0604347103 · Cited by 340
Mapping Information Flow in Sensorimotor Networks
Lungarella, M., & Sporns, O. (2006) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0020144 · Cited by 294
Self-citationAlbantakis, L., Barbosa, L., Findlay, G., Grasso, M., Haun, A. M., Marshall, W., Mayner, W. G. P., Zaeemzadeh, A., Boly, M., Juel, B. E., et al. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011465 · Cited by 292
Self-citationInformation Flows in Causal Networks
AY, N., & POLANI, D. (2008) · Advances in Complex Systems · doi:10.1142/s0219525908001465 · Cited by 287
The Information Integration Theory of Consciousness
Tononi, G. (2007) · Blackwell Publishing Ltd eBooks · doi:10.1002/9780470751466.ch23 · Cited by 315
Self-citationDevelopment of a large-scale functional brain network during human non-rapid eye movement sleep
Spoormaker, V. I., Schröter, M. S., Gleiser, P. M., Andrade, K. C., Dresler, M., Wehrle, R., Sämann, P. G., & Czisch, M. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.2015-10.2010 · Cited by 273
The bioelectric code: An ancient computational medium for dynamic control of growth and form
Levin, M. G., & Martyniuk, C. J. (2017) · Biosystems · doi:10.1016/j.biosystems.2017.08.009 · Cited by 237
Causal connectivity of evolved neural networks during behavior
Seth, A. K. (2005) · Network Computation in Neural Systems · doi:10.1080/09548980500238756 · Cited by 242
Theiner, G., Allen, C., & Goldstone, R. L. (2010) · Cognitive Systems Research · doi:10.1016/j.cogsys.2010.07.002 · Cited by 231
Practical Measures of Integrated Information for Time-Series Data
Barrett, A. B., & Seth, A. K. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001052 · Cited by 225
Ryali, S., Chen, T., Supekar, K., & Menon, V. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.11.054 · Cited by 221
Cerebral organoids: ethical issues and consciousness assessment
Lavazza, A., & Massimini, M. (2018) · Journal of Medical Ethics · doi:10.1136/medethics-2017-104555 · Cited by 217
King, J. R., Faugeras, F., Gramfort, A., Schurger, A., El Karoui, I., Sitt, J. D., Rohaut, B., Wacongne, C., Labyt, E., Bekinschtein, T., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.07.013 · Cited by 197
Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends
Chiarion, G., Sparacino, L., Antonacci, Y., Faes, L., & Mesin, L. (2023) · Bioengineering · doi:10.3390/bioengineering10030372 · Cited by 231
The rise of machine consciousness: Studying consciousness with computational models
Reggia, J. A. (2013) · Neural Networks · doi:10.1016/j.neunet.2013.03.011 · Cited by 211
A perturbational approach for evaluating the brain’s capacity for consciousness
Massimini, M., Boly, M., Casali, A., Rosanova, M., & Tononi, G. (2009) · Progress in Brain Research · doi:10.1016/s0079-6123(09)17714-2 · Cited by 184
Self-citationLee, U., Kim, S., Noh, G., Choi, B., Hwang, E., & Mashour, G. A. (2009) · Consciousness and Cognition · doi:10.1016/j.concog.2009.04.004 · Cited by 190
Consciousness, information integration, and the brain
Tononi, G. (2005) · Progress in brain research · doi:10.1016/s0079-6123(05)50009-8 · Cited by 180
Self-citationConsciousness and complexity: a consilience of evidence
Sarasso, S., Casali, A. G., Casarotto, S., Rosanova, M., Sinigaglia, C., & Massimini, M. (2021) · Neuroscience of Consciousness · doi:10.1093/nc/niab023 · Cited by 171
Towards a comparative science of emotion: Affect and consciousness in humans and animals
Paul, E. S., Sher, S., Tamietto, M., Winkielman, P., & Mendl, M. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.11.014 · Cited by 180
Metastability, criticality and phase transitions in brain and its models
Werner, G. (2006) · Biosystems · doi:10.1016/j.biosystems.2006.12.001 · Cited by 157
Breakdown of the brain’s functional network modularity with awareness
Godwin, D., Barry, R. L., & Marois, R. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1414466112 · Cited by 145
Explanatory correlates of consciousness: theoretical and computational challenges.
Seth, A. (2009) · Cognitive Computation · doi:10.1007/s12559-009-9007-x · Cited by 146
Segregation of the Brain into Gray and White Matter: A Design Minimizing Conduction Delays
Wen, Q., & Chklovskii, D. B. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0010078 · Cited by 150
Lee, U., Mashour, G. A., Kim, S., Noh, G., & Choi, B. (2008) · Consciousness and Cognition · doi:10.1016/j.concog.2008.10.005 · Cited by 141
The integrated information theory of consciousness: A case of mistaken identity
Merker, B., Williford, K., & Rudrauf, D. (2022) · Behavioral and Brain Sciences · doi:10.1017/s0140525x21000881 · Cited by 49
Causal density and integrated information as measures of conscious level
Seth, A. K., Barrett, A. B., & Barnett, L. (2011) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2011.0079 · Cited by 135
Integrating the Science of Consciousness and Anesthesia
Mashour, G. A. (2006) · Anesthesia & Analgesia · doi:10.1213/01.ane.0000232442.69757.4a · Cited by 131
Network Properties in Transitions of Consciousness during Propofol-induced Sedation
Lee, M., Sanders, R. D., Yeom, S.-K., Won, D.-O., Seo, K., Kim, H. J., Tononi, G., & Lee, S. (2017) · Scientific Reports · doi:10.1038/s41598-017-15082-5 · Cited by 131
Self-citationJiang, L., Xu, T., He, Y., Hou, X., Wang, J., Cao, X., Wei, G., Yang, Z., Yong He, A., & Zuo, X. (2014) · Brain Structure and Function · doi:10.1007/s00429-014-0795-8 · Cited by 125
An integrative, multiscale view on neural theories of consciousness
Storm, J. F., Klink, P. C., Aru, J., Senn, W., Goebel, R., Pigorini, A., Avanzini, P., Vanduffel, W., Roelfsema, P. R., Massimini, M., et al. (2024) · Neuron · doi:10.1016/j.neuron.2024.02.004 · Cited by 114
“32,168,169 This notion of brain complexity—defined as the coexistence of integration and differentiation—was at the core of IIT’s original notion of F 89,90 and remains central to the empirical predictions of IIT in its mature form.”
The Problem with Phi: A Critique of Integrated Information Theory
Cerullo, M. A. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004286 · Cited by 114
Measuring integrated information: comparison of candidate measures in theory and simulation
Mediano, P., Seth, A., & Barrett, A. (2018) · Entropy · doi:10.3390/e21010017 · Cited by 145
When the Map Is Better Than the Territory
Hoel, E. (2017) · Entropy · doi:10.3390/e19050188 · Cited by 135
Jobst, B., Hindriks, R., Laufs, H., Tagliazucchi, E., Hahn, G., Ponce‐Alvarez, A., Stevner, A. B. A., Kringelbach, M. L., & Deco, G. (2017) · Scientific Reports · doi:10.1038/s41598-017-04522-x · Cited by 113
Measuring integrated information: comparison of \ncandidate measures in theory and simulation
Mediano, P. A. M., Seth, A. K., & Barrett, A. B. (2018) · Figshare · Cited by 114
The use of information theory in evolutionary biology
Adami, C. (2012) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2011.06422.x · Cited by 113
Al‐Ezzi, A. A. A., Kamel, N., Faye, I., & Ebenezer, E. G. M. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.00730 · Cited by 124
The strength of weak integrated information theory
Mediano, P. A. M., Rosas, F. E., Bor, D., Seth, A. K., & Barrett, A. B. (2022) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2022.04.008 · Cited by 124
Mashour, G. A., Orser, B. A., Avidan, M. S., & Warner, D. S. (2011) · Anesthesiology · doi:10.1097/aln.0b013e31820fc9b6 · Cited by 111
Krichmar, J. L., Seth, A. K., Nitz, D. A., Fleischer, J., & Edelman, G. M. (2005) · Neuroinformatics · doi:10.1385/ni:3:3:197 · Cited by 105
Hoffman, D. D., & Prakash, C. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.00577 · Cited by 103
Albantakis, L., Hintze, A., Koch, C., Adami, C., & Tononi, G. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003966 · Cited by 101
Self-citationEsser, S. K., Hill, S., & Tononi, G. (2009) · Journal of Neurophysiology · doi:10.1152/jn.00059.2009 · Cited by 97
Self-citationInformation Geometry on Complexity and Stochastic Interaction
Ay, N. (2015) · Entropy · doi:10.3390/e17042432 · Cited by 96
Neural Darwinism and consciousness
Seth, A. K., & Baars, B. J. (2004) · Consciousness and Cognition · doi:10.1016/j.concog.2004.08.008 · Cited by 102
The Emergence of Informative Higher Scales in Complex Networks
Klein, B., & Hoel, E. P. (2020) · Complexity · doi:10.1155/2020/8932526 · Cited by 98
Human cerebral organoids and consciousness: a double-edged sword
Lavazza, A. (2020) · Bioethics News · doi:10.1007/s40592-020-00116-y · Cited by 96
Korzeniewska, A., Franaszczuk, P. J., Crainiceanu, C. M., Kuś, R., & Crone, N. E. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.03.030 · Cited by 88
Salvador, R. M., Martínez-Pérez, Á., Pomarol‐Clotet, E., Sarró, S., John Suckling, A., & Bullmore, E. T. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.12.001 · Cited by 89
Keep Your Options Open: An Information-Based Driving Principle for Sensorimotor Systems
Klyubin, A. S., Polani, D., & Nehaniv, C. L. (2008) · PLoS ONE · doi:10.1371/journal.pone.0004018 · Cited by 97
Andric, M., & Small, S. L. (2012) · Frontiers in Psychology · doi:10.3389/fpsyg.2012.00099 · Cited by 90
Causal networks in simulated neural systems
Seth, A. K. (2007) · Cognitive Neurodynamics · doi:10.1007/s11571-007-9031-z · Cited by 83
Foundational perspectives on causality in large-scale brain networks
Mannino, M., & Bressler, S. L. (2015) · Physics of Life Reviews · doi:10.1016/j.plrev.2015.09.002 · Cited by 80
Multiscale memory and bioelectric error correction in the cytoplasm–cytoskeleton‐membrane system
Fields, C., & Levin, M. G. (2017) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1410 · Cited by 76
Neuronal integration in visual cortex elevates face category tuning to conscious face perception
Fahrenfort, J. J., Snijders, T. M., Heinen, K., van Gaal, S., Scholte, H. S., & Lamme, V. A. F. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1207414110 · Cited by 76
Information integration in large brain networks
Toker, D., & Sommer, F. T. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006807 · Cited by 76
Integrated information as a common signature of dynamical and information-processing complexity
Mediano, P. A. M., Rosas, F. E., Farah, J. C., Shanahan, M., Bor, D., & Barrett, A. B. (2022) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0063384 · Cited by 68
Superconducting optoelectronic loop neurons
Shainline, J. M., Buckley, S. M., McCaughan, A. N., Chiles, J. T., Salim, A. J., Castellanos-Beltran, M. A., Donnelly, C. A., Schneider, M. L., Mirin, R. P., & Nam, S. W. (2019) · Journal of Applied Physics · doi:10.1063/1.5096403 · Cited by 66
Emergence as the conversion of information: a unifying theory
Varley, T. F., & Hoel, E. P. (2022) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2021.0150 · Cited by 75
Lavazza, A. (2020) · Brain Research · doi:10.1016/j.brainres.2020.147146 · Cited by 62
(2008) · Cited by 61
COALIA: A Computational Model of Human EEG for Consciousness Research
Bensaid, S., Modolo, J., Merlet, I., Wendling, F., & Benquet, P. (2019) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2019.00059 · Cited by 62
Top-Down Causation and the Rise of Information in the Emergence of Life
Walker, S. I. (2014) · Information · doi:10.3390/info5030424 · Cited by 59
neuroVIISAS: Approaching Multiscale Simulation of the Rat Connectome
Schmitt, O., & Eipert, P. (2012) · Neuroinformatics · doi:10.1007/s12021-012-9141-6 · Cited by 51
Relations Between the Geometry of Cortical Gyrification and White-Matter Network Architecture
Henderson, J. A., & Robinson, P. A. (2014) · Brain Connectivity · doi:10.1089/brain.2013.0183 · Cited by 53
Measuring Consciousness in the Intensive Care Unit
Edlow, B. L., Fecchio, M., Bodien, Y. G., Comanducci, A., Rosanova, M., Casarotto, S., Young, M. J., Li, J., Dougherty, D. D., Koch, C., et al. (2023) · Neurocritical Care · doi:10.1007/s12028-023-01706-4 · Cited by 50
Self-citationElectroencephalogram approximate entropy influenced by both age and sleep
Lee, G. M., Fattinger, S., Mouthon, A., Noirhomme, Q., & Huber, R. (2013) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2013.00033 · Cited by 53
Theoretical approaches to the diagnosis of altered states of consciousness
Boly, M., Massimini, M., & Tononi, G. (2009) · Progress in brain research · doi:10.1016/s0079-6123(09)17727-0 · Cited by 52
Self-citationAn Integrated Information Theory of Consciousness
Tononi, G. (2009) · Elsevier eBooks · doi:10.1002/9781119132363.ch17 · Cited by 47
Self-citationDynamical independence: Discovering emergent macroscopic processes in complex dynamical systems
Barnett, L., & Seth, A. K. (2023) · Physical review. E · doi:10.1103/physreve.108.014304 · Cited by 44
Sleep Neuroimaging and Models of Consciousness
Tagliazucchi, E., Behrens, M., & Laufs, H. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00256 · Cited by 49
Fingelkurts, A. A., Fingelkurts, A. A., Bagnato, S., Boccagni, C., & Galardi, G. (2011) · Cognitive Processing · doi:10.1007/s10339-011-0416-x · Cited by 43
Information integration based predictions about the conscious states of a spiking neural network
Gamez, D. (2009) · Consciousness and Cognition · doi:10.1016/j.concog.2009.11.001 · Cited by 43
Information Integration and Mesoscopic Cortical Connectivity during Propofol Anesthesia
Liang, Z., Cheng, L., Shao, S., Jin, X., Yu, T., SLEIGH, J. W., & Li, X. (2019) · Anesthesiology · doi:10.1097/aln.0000000000003015 · Cited by 41
Sorrentino, P., Petkoski, S., Sparaco, M., Lopez, E. T., Signoriello, E., Baselice, F., Bonavita, S., Pirozzi, M. A., Quarantelli, M., Sorrentino, G., et al. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.0938-22.2022 · Cited by 40
Quantifying auditory event-related responses in multichannel human intracranial recordings
Boatman‐Reich, D. (2010) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2010.00004 · Cited by 38
A neurobehavioral model of flexible spatial language behaviors.
Lipinski, J., Schneegans, S., Sandamirskaya, Y., Spencer, J. P., & Schöner, G. (2011) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/a0022643 · Cited by 38
On the contribution of the senses to food emotional experience
Dantec, M., Mantel, M., Lafraire, J., Rouby, C., & Bensafi, M. (2020) · Food Quality and Preference · doi:10.1016/j.foodqual.2020.104120 · Cited by 45
Decoding the circuitry of consciousness: From local microcircuits to brain-scale networks
Modolo, J., Hassan, M., Wendling, F., & Benquet, P. (2020) · Network Neuroscience · doi:10.1162/netn_a_00119 · Cited by 39
DeMarse, T. B., Pan, L., Alagapan, S., Brewer, G. J., & Wheeler, B. C. (2016) · Frontiers in Neural Circuits · doi:10.3389/fncir.2016.00032 · Cited by 34
Human and Machine Consciousness
Gamez, D. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.11647/obp.0107 · Cited by 39
Measuring the Complexity of Consciousness
Arsiwalla, X. D., & Verschure, P. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00424 · Cited by 35
Werner, G. (2012) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2012.03.014 · Cited by 36
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Metadata
- Authors
- Tononi, G., & Sporns, O.
- Affiliations
- University of Wisconsin–Madison (US)
- Indiana University Bloomington (US)
- Corresponding author
- Tononi, Giulio
- Venue
- BMC Neuroscience
- Published
- 2003-12-02
- Volume
- 4
- Issue
- 1
- Pages
- 31-31
- ISSN
- 1471-2202 (ISSN-L)
- PMC
- PMC331407
- OpenAlex
- W1832461346
- Cited by
- 294
- References
- 51
- Citations by year
- 2026: 7 · 2025: 13 · 2024: 12 · 2023: 10 · 2022: 14 · 2021: 15 · 2020: 20 · 2019: 19 · 2018: 31 · 2017: 18 · 2016: 11 · 2015: 12 · 2014: 9 · 2013: 12 · 2012: 11
- FWCI
- 2.77
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Neuroscience (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, other-oa)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Systems and Dynamics
- Keywords
- information integration, effective information, phi, distributed networks, brain connectivity, functional specialization, functional integration, thalamocortical system, cerebellum, network organization, causal interaction
- MeSH terms
- Brain, Information Theory, Models, Neurological, Nerve Net, Synaptic Transmission, Stochastic Processes, Normal Distribution, Neural Networks, Computer
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