The DOI matches its Crossref record; 50 of the 51 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Items 101–200 of 293
Structural Determinants of Functional Brain Dynamics
Sporns, O., & Tononi, G. (2007) · Understanding complex systems · doi:10.1007/978-3-540-71512-2_4 · Cited by 31
Self-citationConflicting emergences. Weak vs. strong emergence for the modelling of brain function
Turkheimer, F., Hellyer, P. J., Kehagia, A. A., Expert, P., Lord, L.-D., Vohryzek, J., Dafflon, J., Brammer, M. J., & Leech, R. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.01.023 · Cited by 35
Causal composition: Structural differences among dynamically equivalent systems
Albantakis, L., & Tononi, G. (2019) · Entropy · doi:10.3390/e21100989 · Cited by 30
Self-citation‘Consciousnessoids’: clues and insights from human cerebral organoids for the study of consciousness
Lavazza, A. (2021) · Neuroscience of Consciousness · doi:10.1093/nc/niab029 · Cited by 30
The Intrinsic Connectome of the Rat Amygdala
Schmitt, O., Eipert, P., Philipp, K., Kettlitz, R., Fuellen, G., & Wree, A. (2012) · Frontiers in Neural Circuits · doi:10.3389/fncir.2012.00081 · Cited by 31
Finding continuity and discontinuity in fish schools via integrated information theory
Niizato, T., Sakamoto, K., Mototake, Y.-I., Murakami, H., Tomaru, T., Hoshika, T., & Fukushima, T. (2020) · PLoS ONE · doi:10.1371/journal.pone.0229573 · Cited by 27
Flack, J. C., & Krakauer, D. C. (2011) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.3643063 · Cited by 29
Genomic instantiation of consciousness in neurons through a biophoton field theory
Cacha, L. A., & Poznański, R. R. (2014) · Journal of Integrative Neuroscience · doi:10.1142/s0219635214400081 · Cited by 29
Varley, T. F., & Kaminski, P. C. (2022) · Entropy · doi:10.3390/e24101387 · Cited by 32
fMRI methods for studying the neurobiology of language under naturalistic conditions
Andric, M., & Small, S. L. (2015) · Cambridge University Press eBooks · doi:10.1017/cbo9781107323667.002 · Cited by 24
Awareness and integrated information theory identify plant meristems as sites of conscious activity
TREWAVAS, A. J. (2021) · PROTOPLASMA · doi:10.1007/s00709-021-01633-1 · Cited by 29
Are Information or Data Patterns Correlated with Consciousness?
Gamez, D. (2014) · Topoi · doi:10.1007/s11245-014-9246-7 · Cited by 25
Changes in the thalamocortical connectivity during anesthesia-induced transitions in consciousness
Kim, S.-P., Hwang, E., Kang, J.-H., Kim, S., & Choi, J. H. (2012) · Neuroreport · doi:10.1097/wnr.0b013e3283509ba0 · Cited by 25
Schmidt, S. L., Chew, E. Y., Bennett, D., Hammad, M. A., & Frӧhlich, F. (2013) · Neuropharmacology · doi:10.1016/j.neuropharm.2013.04.045 · Cited by 24
Seth, A. K., & Edelman, G. M. (2009) · Springer eBooks · doi:10.1007/978-0-387-30440-3_94 · Cited by 24
A measure centrality index for systematic empirical comparison of consciousness theories
Chis-Ciure, R., Melloni, L., & Northoff, G. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105670 · Cited by 22
Neural Information Squeezer for Causal Emergence
Zhang, J., & Liu, K. (2022) · Entropy · doi:10.3390/e25010026 · Cited by 27
Integrated information for large complex networks
Arsiwalla, X. D., & Verschure, P. F. M. J. (2013) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2013.6706794 · Cited by 21
Alagapan, S., Franca, E., Pan, L., Leondopulos, S. S., Wheeler, B. C., & DeMarse, T. B. (2016) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2016.00015 · Cited by 21
The Morphospace of ConsciousnessPreprint
Arsiwalla, X. D., Moulin-Frier, C., Herreros, I., Sánchez-Fibla, M., & Verschure, P. F. M. J. (2017) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/emhnp · Cited by 19
A novel perturbation based compression complexity measure for networks
Virmani, M., & Nagaraj, N. (2019) · Heliyon · doi:10.1016/j.heliyon.2019.e01181 · Cited by 19
A novel qEEG measure of teamwork for human error analysis: An EEG hyperscanning study
Cha, K.-M., & Lee, H. (2018) · Nuclear Engineering and Technology · doi:10.1016/j.net.2018.11.009 · Cited by 19
The global dynamical complexity of the human brain network
Arsiwalla, X. D., & Verschure, P. F. M. J. (2016) · Applied Network Science · doi:10.1007/s41109-016-0018-8 · Cited by 21
Dynamical complexity in the C.elegans neural network
Antonopoulos, C. G., FOKAS, A. S., & Bountis, T. (2016) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2016-02670-3 · Cited by 15
Neurocontrol: Methods, models and technologies for manipulating dynamics in the brain
Ritt, J. T., & Ching, S. (2015) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.2015.7171915 · Cited by 16
An Integrated Information Theory of Consciousness
Tononi, G. (2009) · Elsevier eBooks · doi:10.1016/b978-012373873-8.00014-1 · Cited by 17
Self-citationFinding emergence in data by maximizing effective information
Yang, M., Wang, Z., Liu, K., Rong, Y., Yuan, B., & Zhang, J. (2024) · National Science Review · doi:10.1093/nsr/nwae279 · Cited by 16
Neural complexity: A graph theoretic interpretation
Barnett, L., Buckley, C. L., & Bullock, S. (2011) · Physical Review E · doi:10.1103/physreve.83.041906 · Cited by 16
Axioms, properties and criteria: Roles for synthesis in the science of consciousness
Clowes, R. W., & Seth, A. K. (2008) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2008.07.009 · Cited by 16
High Integrated Information in Complex Networks Near Criticality
Arsiwalla, X. D., & Verschure, P. F. M. J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-44778-0_22 · Cited by 16
Agent Above, Atom Below: How Agents Causally Emerge from Their Underlying Microphysics
Hoel, E. P. (2018) · The frontiers collection · doi:10.1007/978-3-319-75726-1_6 · Cited by 16
Information Dynamics: Its Theory and Application to Embodied Cognitive Systems
Williams, P. L. (2011) · doi:10.13140/2.1.1401.2162 · Cited by 17
Gao, Y., Ascoli, G. A., & Zhao, L. (2021) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2021.567482 · Cited by 16
Computing Integrated Information (Φ) in Discrete Dynamical Systems with Multi-Valued Elements
Gomez, J. D., Mayner, W. G. P., Beheler-Amass, M., Tononi, G., & Albantakis, L. (2020) · Entropy · doi:10.3390/e23010006 · Cited by 16
Self-citationModelling Sleep and General Anaesthesia
SLEIGH, J. W., Voss, L. J., Steyn‐Ross, M. L., Steyn‐Ross, D. A., & Wilson, M. T. (2011) · Springer eBooks · doi:10.1007/978-1-4614-0173-5_2 · Cited by 15
Causal emergence from effective information: Neither causal nor emergent?
Dewhurst, J., & Philosophy Documentation Center (2021) · Thought: A Journal of Philosophy · doi:10.1002/tht3.489 · Cited by 12
Guerrero, L. E., Castillo, L. F., Arango-López, J., & Moreira, F. (2025) · Neural Computing and Applications · doi:10.1007/s00521-023-08328-z · Cited by 11
Separability, Contextuality, and the Quantum Frame Problem
Fields, C., & Glazebrook, J. F. (2023) · International Journal of Theoretical Physics · doi:10.1007/s10773-023-05406-9 · Cited by 13
Examining the Causal Structures of Deep Neural Networks Using Information Theory
Marrow, S., Michaud, E. J., & Hoel, E. P. (2020) · Entropy · doi:10.3390/e22121429 · Cited by 16
Informational Theories of Consciousness: A Review and Extension
Aleksander, I., & Gamez, D. (2011) · Advances in experimental medicine and biology · doi:10.1007/978-1-4614-0164-3_12 · Cited by 14
The Global dynamical complexity of the human brain network
Arsiwalla, X. D., & Verschure, P. F. M. J. (n.d.) · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · Cited by 13
CONSCIOUSNESS AS A QUANTUM DYNAMIC EFFECT
Poznański, R. R., Cacha, L. A., Ali, J., Aur, D., Velázquez, J. L. P., Bahadoran, M., Yupapin, P. P., & Yunus, J. (2016) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789814644266_0011 · Cited by 12
Dynamical reversibility and a new theory of causal emergence based on SVD
Zhang, J., Tao, R., Leong, K. H., Yang, M., & Yuan, B. (2025) · npj Complexity · doi:10.1038/s44260-025-00028-0 · Cited by 14
Computing Information Integration in Brain Networks
Arsiwalla, X. D., & Verschure, P. F. M. J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-28361-6_11 · Cited by 12
Information theory for complex systems scientists: What, why, and how
Varley, T. F. (2025) · Physics Reports · doi:10.1016/j.physrep.2025.09.007 · Cited by 38
Chvykov, P., & Hoel, E. P. (2020) · Entropy · doi:10.3390/e23010024 · Cited by 16
Varley, T. F., & Bongard, J. (2024) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0200425 · Cited by 11
Interactions and information: Exploring task allocation in ant colonies using network analysis
Swain, A., Williams, S. D., Felice, L. J. D., & Hobson, E. A. (2021) · Animal Behaviour · doi:10.1016/j.anbehav.2022.04.015 · Cited by 11
The vulnerability of communities in complex networks: A causal perspective on dynamic retentivity
Zhou, K., Yan, C., & Xu, Y. D. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.111171 · Cited by 10
Prediction and modularity in dynamical systems
Kolchinsky, A., & Rocha, L. M. (2011) · European Conference on Artificial Life · doi:10.7551/978-0-262-29714-1-ch065 · Cited by 10
Liang, Z., Chang, Y., Liu, X., Cao, S., Chen, Y., Wang, T., Xu, J., Li, D., & Zhang, J. (2023) · British Journal of Anaesthesia · doi:10.1016/j.bja.2023.11.033 · Cited by 12
RetractedGamez, D. (2018) · Open Book Publishers · doi:10.11647/obp.0107.11 · Cited by 10
Information structure of heterogeneous criticality in a fish school
Niizato, T., Sakamoto, K., Mototake, Y.-I., Murakami, H., & Tomaru, T. (2024) · Scientific Reports · doi:10.1038/s41598-024-79232-2 · Cited by 11
Quantification of degeneracy in Hodgkin–Huxley neurons on Newman–Watts small world network
Man, M., Zhang, Y., Ma, G., Friston, K. J., & Liu, S. (2016) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2016.05.004 · Cited by 8
Four-Types of IIT-Induced Group Integrity of Plecoglossus altivelis
Niizato, T., Sakamoto, K., Mototake, Y.-I., Murakami, H., Tomaru, T., Hoshika, T., & Fukushima, T. (2020) · Entropy · doi:10.3390/e22070726 · Cited by 9
Large-Scale Network Dynamics in Neurocognitive Function
McIntosh, A. R. (2007) · Springer eBooks · doi:10.1007/978-3-540-74479-5_9 · Cited by 9
Why the Brain Might Operate Near the Edge of Criticality
Arsiwalla, X. D., & Verschure, P. F. M. J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68600-4_38 · Cited by 9
A scalable synergy-first backbone decomposition of higher-order structures in complex systems
Varley, T. F. (2024) · npj Complexity · doi:10.1038/s44260-024-00011-1 · Cited by 8
Complex of global functional network as the core of consciousness
Onoda, K., & Akama, H. (2022) · Neuroscience Research · doi:10.1016/j.neures.2022.12.007 · Cited by 9
Causal Emergence: When Distortions in a Map Obscure the Territory
Eberhardt, F., & Lee, L. L. (2022) · Philosophies · doi:10.3390/philosophies7020030 · Cited by 8
The Morphospace of Consciousness: Three Kinds of Complexity for Minds and Machines
Arsiwalla, X. D., Solé, R. V., Moulin-Frier, C., Herreros, I., Sánchez-Fibla, M., & Verschure, P. F. M. J. (2023) · NeuroSci · doi:10.3390/neurosci4020009 · Cited by 10
Theories and methods in the scientific study of consciousness
Klink, P. C., Self, M. W., Lamme, V. A. F., & Roelfsema, P. R. (2015) · Advances in consciousness research · doi:10.1075/aicr.92.02kli · Cited by 9
Consilience in Causation: Causal Emergence Is Found Across Measures of Causation
Comolatti, R., & Hoel, E. P. (2025) · Entropy · doi:10.3390/e27080825 · Cited by 27
8. Information and Consciousness
Randall, M. (2019) · Rutgers University Press eBooks · doi:10.36019/9780813546452-010 · Cited by 7
WORLD-RELATED INTEGRATED INFORMATION: ENACTIVIST AND PHENOMENAL PERSPECTIVES
BEATON, M., & Aleksander, I. (2012) · International Journal of Machine Consciousness · doi:10.1142/s1793843012400252 · Cited by 7
The Mind-Brain Relationship as a Mathematical Problem
Ascoli, G. A. (2013) · ISRN Neuroscience · doi:10.1155/2013/261364 · Cited by 7
Toward a General Theory of Unconscious Processes in Psychoanalysis and Anesthesiology
Mashour, G. A. (2008) · Journal of the American Psychoanalytic Association · doi:10.1177/0003065108315692 · Cited by 7
AN ERGODIC THEORY OF CONSCIOUSNESS
Boyarsky, A., & Góra, P. (2009) · International Journal of Bifurcation and Chaos · doi:10.1142/s0218127409023731 · Cited by 7
Superconducting Optoelectronic Neurons I: General PrinciplesPreprint
Shainline, J. M., Buckley, S. M., McCaughan, A. N., Chiles, J., Mirin, R. P., & Nam, S. W. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.01929 · Cited by 11
Superconducting Optoelectronic Neurons V: Networks and ScalingPreprint
Shainline, J. M., Chiles, J., Buckley, S. M., McCaughan, A. N., Mirin, R. P., & Nam, S. W. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.01942 · Cited by 12
Observational causality by states and interaction type for scientific discovery
Martínez-Sánchez, Á., & Lozano-Durán, A. (2025) · Communications Physics · doi:10.1038/s42005-025-02447-w · Cited by 7
A Synergistic Perspective on Multivariate Computation and Causality in Complex Systems
Varley, T. F. (2024) · Entropy · doi:10.3390/e26100883 · Cited by 6
Azadi, P. (2025) · Biosystems · doi:10.1016/j.biosystems.2025.105563 · Cited by 6
Intrinsic units: identifying a system’s causal grain
Marshall, W., Findlay, G., Albantakis, L., & Tononi, G. (2026) · Neuroscience of Consciousness · doi:10.1093/nc/niag013 · Cited by 12
Self-citationNeural Binding, Consciousness, and Mental Disorders: Complexity as a Common Element
Assis, L. P. G. D. (2015) · Activitas Nervosa Superior · doi:10.1007/bf03379943 · Cited by 6
Causal Scale of Rotors in a Cardiac System
Ashikaga, H., Prieto‐Castrillo, F., Kawakatsu, M., & Dehghani, N. (2018) · Frontiers in Physics · doi:10.3389/fphy.2018.00030 · Cited by 6
The Largest Cognitive Systems Will be Optoelectronic
Shainline, J. M. (2018) · IEEE International Conference on Rebooting Computing (ICRC) · doi:10.1109/icrc.2018.8638599 · Cited by 7
Taking a Mental Stance Towards Artificial Systems
Gamez, D., & Aleksander, I. (2009) · Cited by 6
Schmid, G. B. (2015) · Schweizerische Zeitschrift für Ganzheitsmedizin / Swiss Journal of Integrative Medicine · doi:10.1159/000365558 · Cited by 5
Varley, T. F., Pai, V. P., Grasso, C., Lunshof, J. E., Levin, M. G., & Bongard, J. (2025) · Communicative & Integrative Biology · doi:10.1080/19420889.2025.2568307 · Cited by 4
A new measure based on degree distribution that links information theory and network graph analysis
Hadley, M. W., McGranaghan, M. F., Willey, A., Liew, C. W., & Reynolds, E. (2012) · Neural Systems & Circuits · doi:10.1186/2042-1001-2-7 · Cited by 5
Astakhov, V. (2009) · Methods in molecular biology · doi:10.1007/978-1-59745-524-4_6 · Cited by 5
Specific and Complete Local Integration of Patterns in Bayesian Networks
Biehl, M., Ikegami, T., & Polani, D. (2017) · Entropy · doi:10.3390/e19050230 · Cited by 3
Searching in the wrong place: Might consciousness reside in the brainstem?
Devor, M. G., Koukoui, M., & Baron, M. (2022) · Behavioral and Brain Sciences · doi:10.1017/s0140525x21001928 · Cited by 4
Veyrié, A., Noreña, A. J., Sarrazin, J.-C., & Pézard, L. (2023) · Biology · doi:10.3390/biology12070967 · Cited by 4
Spatially embedded dynamics and complexity
Buckley, C. L., Bullock, S., & Barnett, L. (2010) · Complexity · doi:10.1002/cplx.20337 · Cited by 4
Delegated causality of complex systems
Vidunas, R. (2019) · MPG.PuRe (Max Planck Society) · Cited by 4
Quantifying the Autonomy of Structurally Diverse Automata: A Comparison of Candidate Measures
Albantakis, L. (2021) · Entropy · doi:10.3390/e23111415 · Cited by 5
Robots and Machine Consciousness
Chella, A. (2022) · The MIT Press eBooks · doi:10.7551/mitpress/13780.003.0029 · Cited by 4
Cyclic and multilevel causation in evolutionary processes
Warrell, J., & Gerstein, M. (2020) · Biology & Philosophy · doi:10.1007/s10539-020-09753-3 · Cited by 4
Metastability and functional integration in anisotropically coupled map lattices
Pitti, A., Lungarella, M., & Kuniyoshi, Y. (2008) · The European Physical Journal B · doi:10.1140/epjb/e2008-00238-2 · Cited by 4
Iconic Training and Effective Information: Evaluating Meaning in Discrete Neural Networks.
Aleksander, I., & Gamez, D. (2009) · Biologically Inspired Cognitive Architectures · Cited by 4
Two-dimensional SVD for event detection in dynamic functional brain networks
Mahyari, A., & Aviyente, S. (2013) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2013.6736805 · Cited by 4
Informational Principles of Perception-Action Loops and Collective Behaviours
Capdepuy, P. (2011) · University of Hertfordshire Research Archive (University of Hertfordshire) · Cited by 4
Formal Approaches to a Definition of AgentsPreprint
Biehl, M. (2017) · arXiv (Cornell University) · doi:10.18745/th.17955 · Cited by 7
What does it mean for consciousness to be multidimensional? A narrative review
Páleník, J. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1430262 · Cited by 4
Anesthesia-Induced State Transitions in Neuronal Populations
SLEIGH, J. W., Steyn‐Ross, M. L., Steyn‐Ross, D. A., Voss, L. J., & Wilson, M. T. (2009) · Contemporary clinical neuroscience · doi:10.1007/978-1-60761-462-3_7 · Cited by 3
Emergence of Small-World Structure in Networks of Spiking Neurons Through STDP Plasticity
Basalyga, G., Gleiser, P. M., & Wennekers, T. (2011) · Advances in experimental medicine and biology · doi:10.1007/978-1-4614-0164-3_4 · Cited by 4
Sanfey, J. (2024) · Entropy · doi:10.3390/e26080647 · Cited by 3
How Physical Information Underlies Causation and the Emergence of Systems at all Biological Levels
Farnsworth, K. D. (2025) · Acta Biotheoretica · doi:10.1007/s10441-025-09495-3 · Cited by 5
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
