The Emergence of Integrated Information, Complexity, and ‘Consciousness’ at Criticality
Integrated Information Theory (IIT) posits that integrated information ( Φ ) represents the quantity of a conscious experience. Here, the generalized Ising model was used to calculate Φ as a function of temperature in toy models of fully connected neural networks. A Monte–Carlo simulation was run on...
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| Published in | Entropy (Basel, Switzerland) Vol. 22; no. 3; p. 339 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
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16.03.2020
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| ISSN | 1099-4300 1099-4300 |
| DOI | 10.3390/e22030339 |
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| Abstract | Integrated Information Theory (IIT) posits that integrated information ( Φ ) represents the quantity of a conscious experience. Here, the generalized Ising model was used to calculate Φ as a function of temperature in toy models of fully connected neural networks. A Monte–Carlo simulation was run on 159 normalized, random, positively weighted networks analogous to small five-node excitatory neural network motifs. Integrated information generated by this sample of small Ising models was measured across model parameter spaces. It was observed that integrated information, as an order parameter, underwent a phase transition at the critical point in the model. This critical point was demarcated by the peak of the generalized susceptibility (or variance in configuration due to temperature) of integrated information. At this critical point, integrated information was maximally receptive and responsive to perturbations of its own states. The results of this study provide evidence that Φ can capture integrated information in an empirical dataset, and display critical behavior acting as an order parameter from the generalized Ising model. |
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| AbstractList | Integrated Information Theory (IIT) posits that integrated information ( Φ ) represents the quantity of a conscious experience. Here, the generalized Ising model was used to calculate Φ as a function of temperature in toy models of fully connected neural networks. A Monte-Carlo simulation was run on 159 normalized, random, positively weighted networks analogous to small five-node excitatory neural network motifs. Integrated information generated by this sample of small Ising models was measured across model parameter spaces. It was observed that integrated information, as an order parameter, underwent a phase transition at the critical point in the model. This critical point was demarcated by the peak of the generalized susceptibility (or variance in configuration due to temperature) of integrated information. At this critical point, integrated information was maximally receptive and responsive to perturbations of its own states. The results of this study provide evidence that Φ can capture integrated information in an empirical dataset, and display critical behavior acting as an order parameter from the generalized Ising model. Integrated Information Theory (IIT) posits that integrated information ( Φ ) represents the quantity of a conscious experience. Here, the generalized Ising model was used to calculate Φ as a function of temperature in toy models of fully connected neural networks. A Monte–Carlo simulation was run on 159 normalized, random, positively weighted networks analogous to small five-node excitatory neural network motifs. Integrated information generated by this sample of small Ising models was measured across model parameter spaces. It was observed that integrated information, as an order parameter, underwent a phase transition at the critical point in the model. This critical point was demarcated by the peak of the generalized susceptibility (or variance in configuration due to temperature) of integrated information. At this critical point, integrated information was maximally receptive and responsive to perturbations of its own states. The results of this study provide evidence that Φ can capture integrated information in an empirical dataset, and display critical behavior acting as an order parameter from the generalized Ising model. Integrated Information Theory (IIT) posits that integrated information ( Φ ) represents the quantity of a conscious experience. Here, the generalized Ising model was used to calculate Φ as a function of temperature in toy models of fully connected neural networks. A Monte-Carlo simulation was run on 159 normalized, random, positively weighted networks analogous to small five-node excitatory neural network motifs. Integrated information generated by this sample of small Ising models was measured across model parameter spaces. It was observed that integrated information, as an order parameter, underwent a phase transition at the critical point in the model. This critical point was demarcated by the peak of the generalized susceptibility (or variance in configuration due to temperature) of integrated information. At this critical point, integrated information was maximally receptive and responsive to perturbations of its own states. The results of this study provide evidence that Φ can capture integrated information in an empirical dataset, and display critical behavior acting as an order parameter from the generalized Ising model.Integrated Information Theory (IIT) posits that integrated information ( Φ ) represents the quantity of a conscious experience. Here, the generalized Ising model was used to calculate Φ as a function of temperature in toy models of fully connected neural networks. A Monte-Carlo simulation was run on 159 normalized, random, positively weighted networks analogous to small five-node excitatory neural network motifs. Integrated information generated by this sample of small Ising models was measured across model parameter spaces. It was observed that integrated information, as an order parameter, underwent a phase transition at the critical point in the model. This critical point was demarcated by the peak of the generalized susceptibility (or variance in configuration due to temperature) of integrated information. At this critical point, integrated information was maximally receptive and responsive to perturbations of its own states. The results of this study provide evidence that Φ can capture integrated information in an empirical dataset, and display critical behavior acting as an order parameter from the generalized Ising model. Integrated Information Theory (IIT) posits that integrated information ( Φ ) represents the quantity of a conscious experience. Here, the generalized Ising model was used to calculate Φ as a function of temperature in toy models of fully connected neural networks. A Monte−Carlo simulation was run on 159 normalized, random, positively weighted networks analogous to small five-node excitatory neural network motifs. Integrated information generated by this sample of small Ising models was measured across model parameter spaces. It was observed that integrated information, as an order parameter, underwent a phase transition at the critical point in the model. This critical point was demarcated by the peak of the generalized susceptibility (or variance in configuration due to temperature) of integrated information. At this critical point, integrated information was maximally receptive and responsive to perturbations of its own states. The results of this study provide evidence that Φ can capture integrated information in an empirical dataset, and display critical behavior acting as an order parameter from the generalized Ising model. |
| Author | Owen, Adrian M. Popiel, Nicholas J.M. Khajehabdollahi, Sina Nichols, Emily S. Soddu, Andrea Riganello, Francesco Abeyasinghe, Pubuditha M. |
| AuthorAffiliation | 1 Department of Physics and Astronomy, Western University, 151 Richmond St, London, ON N6A 3K7, Canada; sina.abdollahi@gmail.com (S.K.); enicho4@uwo.ca (E.S.N.); asoddu@uwo.ca (A.S.) 3 Research in Advanced Neurorehabilitation (RAN), S. Anna Institute, Via Siris 11, 88900 Crotone, Italy; francescoriganello@gmail.com 4 Brain and Mind Institute, Western University, 151 Richmond St, London, ON N6A 3K7, Canada; aowen6@uwo.ca 5 Department of Psychology and Department of Physiology and Pharmacology, 151 Richmond St, London, ON N6A 3K7, Canada 2 Faculty of Medicine Nursing and Health Sciences, Monash University, Wellington Rd, Clayton VIC 3800, Australia; pubu.abeyasinghe@monash.edu |
| AuthorAffiliation_xml | – name: 1 Department of Physics and Astronomy, Western University, 151 Richmond St, London, ON N6A 3K7, Canada; sina.abdollahi@gmail.com (S.K.); enicho4@uwo.ca (E.S.N.); asoddu@uwo.ca (A.S.) – name: 5 Department of Psychology and Department of Physiology and Pharmacology, 151 Richmond St, London, ON N6A 3K7, Canada – name: 4 Brain and Mind Institute, Western University, 151 Richmond St, London, ON N6A 3K7, Canada; aowen6@uwo.ca – name: 2 Faculty of Medicine Nursing and Health Sciences, Monash University, Wellington Rd, Clayton VIC 3800, Australia; pubu.abeyasinghe@monash.edu – name: 3 Research in Advanced Neurorehabilitation (RAN), S. Anna Institute, Via Siris 11, 88900 Crotone, Italy; francescoriganello@gmail.com |
| Author_xml | – sequence: 1 givenname: Nicholas J.M. surname: Popiel fullname: Popiel, Nicholas J.M. – sequence: 2 givenname: Sina orcidid: 0000-0003-3701-1734 surname: Khajehabdollahi fullname: Khajehabdollahi, Sina – sequence: 3 givenname: Pubuditha M. orcidid: 0000-0002-7595-5237 surname: Abeyasinghe fullname: Abeyasinghe, Pubuditha M. – sequence: 4 givenname: Francesco surname: Riganello fullname: Riganello, Francesco – sequence: 5 givenname: Emily S. orcidid: 0000-0003-0541-9233 surname: Nichols fullname: Nichols, Emily S. – sequence: 6 givenname: Adrian M. surname: Owen fullname: Owen, Adrian M. – sequence: 7 givenname: Andrea surname: Soddu fullname: Soddu, Andrea |
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| Cites_doi | 10.1038/scientificamerican0191-46 10.1103/PhysRevE.79.061922 10.1103/PhysRevE.99.032408 10.3390/e21100981 10.1098/rsta.2007.2092 10.1007/978-3-642-69689-3 10.3389/fphys.2016.00425 10.1371/journal.pcbi.1003588 10.3390/e21121198 10.1103/PhysRevLett.113.238102 10.1103/PhysRevLett.110.178101 10.1017/CBO9781139696463 10.3389/fncom.2012.00068 10.1371/journal.pone.0093616 10.1103/Physics.7.120 10.1038/17290 10.1371/journal.pcbi.1005123 10.1016/S0375-9601(99)00757-4 10.1103/RevModPhys.77.137 10.1089/brain.2017.0516 10.1103/RevModPhys.47.773 10.1088/1742-5468/2016/04/043301 10.1371/journal.pcbi.1003111 10.3389/fphys.2012.00015 10.1073/pnas.1319166111 10.1073/pnas.0912289107 10.1146/annurev-conmatphys-062910-140509 10.1007/s10955-011-0229-4 10.1038/35065675 10.1177/1073858412445487 10.1098/rsif.2010.0416 10.20944/preprints201904.0077.v1 10.1103/PhysRevLett.42.859 10.1038/nphys1803 10.1103/PhysRevLett.104.025701 10.1038/nphys2190 10.1103/PhysRevB.29.4030 10.1371/journal.pcbi.1002236 10.1016/j.physd.2006.12.004 10.1093/oso/9780195135138.001.0001 10.1017/CBO9781316348772 10.3389/fnsys.2014.00166 10.1103/PhysRev.65.117 10.1038/ncomms3521 10.1038/376046a0 10.1371/journal.pcbi.1006343 10.1038/srep35831 10.1209/0295-5075/23/5/011 10.1016/j.cub.2015.10.014 10.1371/journal.pone.0002456 10.1016/j.physa.2004.05.064 10.1038/srep29640 10.1523/JNEUROSCI.23-35-11167.2003 |
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| References | ref_50 Pawley (ref_49) 1984; 29 Chialvo (ref_29) 2004; 340 Newman (ref_45) 1999; 263 ref_14 ref_13 Sarasso (ref_22) 2015; 25 ref_12 Herrmann (ref_9) 2010; 107 Sethna (ref_16) 2001; 410 ref_53 ref_52 ref_51 Lux (ref_33) 1999; 397 Expert (ref_3) 2011; 8 ref_15 Beggs (ref_2) 2003; 23 ref_25 Crutchfield (ref_1) 2012; 8 ref_24 ref_23 ref_21 Bak (ref_11) 1991; 264 Hesse (ref_8) 2014; 8 Hansel (ref_54) 1993; 23 Wilson (ref_44) 1975; 47 ref_27 ref_26 Bonilla (ref_55) 2005; 77 Swendsen (ref_48) 1979; 42 Mora (ref_18) 2011; 144 ref_36 ref_35 ref_32 ref_30 Goldenfeld (ref_20) 2011; 2 Mantegna (ref_34) 1995; 376 Onsager (ref_28) 1944; 65 ref_39 Beggs (ref_10) 2008; 366 ref_38 ref_37 Shew (ref_17) 2013; 19 Cumin (ref_56) 2007; 226 ref_47 ref_46 Abeyasinghe (ref_31) 2018; 8 ref_43 ref_42 ref_41 ref_40 ref_5 ref_4 ref_7 ref_6 Hidalgo (ref_19) 2014; 111 |
| References_xml | – volume: 264 start-page: 46 year: 1991 ident: ref_11 article-title: Self-organized criticality publication-title: Sci. Am. doi: 10.1038/scientificamerican0191-46 – ident: ref_12 doi: 10.1103/PhysRevE.79.061922 – ident: ref_27 doi: 10.1103/PhysRevE.99.032408 – ident: ref_26 doi: 10.3390/e21100981 – volume: 366 start-page: 329 year: 2008 ident: ref_10 article-title: The criticality hypothesis: How local cortical networks might optimize information processing publication-title: Philos. Trans. R. Soc. A doi: 10.1098/rsta.2007.2092 – ident: ref_53 doi: 10.1007/978-3-642-69689-3 – ident: ref_6 doi: 10.3389/fphys.2016.00425 – ident: ref_21 doi: 10.1371/journal.pcbi.1003588 – ident: ref_25 doi: 10.3390/e21121198 – ident: ref_35 doi: 10.1103/PhysRevLett.113.238102 – ident: ref_14 doi: 10.1103/PhysRevLett.110.178101 – ident: ref_38 doi: 10.1017/CBO9781139696463 – ident: ref_13 doi: 10.3389/fncom.2012.00068 – ident: ref_39 – ident: ref_15 doi: 10.1371/journal.pone.0093616 – ident: ref_36 doi: 10.1103/Physics.7.120 – volume: 397 start-page: 498 year: 1999 ident: ref_33 article-title: Scaling and criticality in a stochastic multi-agent model of a financial market publication-title: Nature doi: 10.1038/17290 – ident: ref_23 doi: 10.1371/journal.pcbi.1005123 – volume: 263 start-page: 341 year: 1999 ident: ref_45 article-title: Renormalization group analysis of the small-world network model publication-title: Phys. Lett. A doi: 10.1016/S0375-9601(99)00757-4 – volume: 77 start-page: 137 year: 2005 ident: ref_55 article-title: The Kuramoto model: A simple paradigm for synchronization phenomena publication-title: Rev. Mod. Phys. doi: 10.1103/RevModPhys.77.137 – volume: 8 start-page: 444 year: 2018 ident: ref_31 article-title: Role of Dimensionality in Predicting the Spontaneous Behavior of the Brain Using the Classical Ising Model and the Ising Model Implemented on a Structural Connectome publication-title: Brain Connect. doi: 10.1089/brain.2017.0516 – volume: 47 start-page: 773 year: 1975 ident: ref_44 article-title: The renormalization group: Critical phenomena and the Kondo problem publication-title: Rev. Mod. Phys. doi: 10.1103/RevModPhys.47.773 – ident: ref_52 – ident: ref_40 doi: 10.1088/1742-5468/2016/04/043301 – ident: ref_50 doi: 10.1371/journal.pcbi.1003111 – ident: ref_4 doi: 10.3389/fphys.2012.00015 – volume: 111 start-page: 10095 year: 2014 ident: ref_19 article-title: Information-based fitness and the emergence of criticality in living systems publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.1319166111 – volume: 107 start-page: 3977 year: 2010 ident: ref_9 article-title: Learning as a phenomenon occurring in a critical state publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.0912289107 – volume: 2 start-page: 375 year: 2011 ident: ref_20 article-title: Life is Physics: Evolution as a Collective Phenomenon Far From Equilibrium publication-title: Annu. Rev. Condens. Matter Phys. doi: 10.1146/annurev-conmatphys-062910-140509 – ident: ref_47 – volume: 144 start-page: 268 year: 2011 ident: ref_18 article-title: Are Biological Systems Poised at Criticality? publication-title: J. Stat. Phys. doi: 10.1007/s10955-011-0229-4 – volume: 410 start-page: 242 year: 2001 ident: ref_16 article-title: Crackling noise publication-title: Nature doi: 10.1038/35065675 – volume: 19 start-page: 88 year: 2013 ident: ref_17 article-title: The functional benefits of criticality in the cortex publication-title: Neuroscientist doi: 10.1177/1073858412445487 – volume: 8 start-page: 472 year: 2011 ident: ref_3 article-title: Self-similar correlation function in brain resting-state functional magnetic resonance imaging publication-title: J. R. Soc. Interface doi: 10.1098/rsif.2010.0416 – ident: ref_24 doi: 10.20944/preprints201904.0077.v1 – volume: 42 start-page: 859 year: 1979 ident: ref_48 article-title: Monte carlo renormalization group publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.42.859 – ident: ref_30 doi: 10.1038/nphys1803 – ident: ref_46 doi: 10.1103/PhysRevLett.104.025701 – volume: 8 start-page: 17 year: 2012 ident: ref_1 article-title: Between order and chaos publication-title: Nat. Phys. doi: 10.1038/nphys2190 – volume: 29 start-page: 4030 year: 1984 ident: ref_49 article-title: Monte Carlo renormalization-group calculations of critical behavior in the simple-cubic Ising model publication-title: Phys. Rev. B doi: 10.1103/PhysRevB.29.4030 – ident: ref_51 doi: 10.1371/journal.pcbi.1002236 – volume: 226 start-page: 181 year: 2007 ident: ref_56 article-title: Generalising the Kuramoto model for the study of neuronal synchronisation in the brain publication-title: Physica D doi: 10.1016/j.physd.2006.12.004 – ident: ref_42 doi: 10.1093/oso/9780195135138.001.0001 – ident: ref_43 doi: 10.1017/CBO9781316348772 – volume: 8 start-page: 166 year: 2014 ident: ref_8 article-title: Self-organized criticality as a fundamental property of neural systems publication-title: Front. Syst. Neurosci. doi: 10.3389/fnsys.2014.00166 – volume: 65 start-page: 117 year: 1944 ident: ref_28 article-title: Crystal statistics. I. A two-dimensional model with an order-disorder transition publication-title: Phys. Rev. doi: 10.1103/PhysRev.65.117 – ident: ref_7 doi: 10.1038/ncomms3521 – volume: 376 start-page: 46 year: 1995 ident: ref_34 article-title: Scaling behaviour in the dynamics of an economic index publication-title: Nature doi: 10.1038/376046a0 – ident: ref_37 doi: 10.1371/journal.pcbi.1006343 – ident: ref_5 doi: 10.1038/srep35831 – volume: 23 start-page: 367 year: 1993 ident: ref_54 article-title: Phase dynamics for weakly coupled hodgkin-huxley neurons publication-title: EPL doi: 10.1209/0295-5075/23/5/011 – volume: 25 start-page: 3099 year: 2015 ident: ref_22 article-title: Consciousness and complexity during unresponsiveness induced by propofol, xenon, and ketamine publication-title: Curr. Biol. doi: 10.1016/j.cub.2015.10.014 – ident: ref_32 doi: 10.1371/journal.pone.0002456 – volume: 340 start-page: 756 year: 2004 ident: ref_29 article-title: Critical brain networks publication-title: Physica A doi: 10.1016/j.physa.2004.05.064 – ident: ref_41 doi: 10.1038/srep29640 – volume: 23 start-page: 11167 year: 2003 ident: ref_2 article-title: Neuronal Avalanches in Neocortical Circuits publication-title: J. Neurosci. doi: 10.1523/JNEUROSCI.23-35-11167.2003 |
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| Title | The Emergence of Integrated Information, Complexity, and ‘Consciousness’ at Criticality |
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