Dehaene–Changeux model explained

The Dehaene–Changeux model (DCM), also known as the global neuronal workspace, or global cognitive workspace model, is a part of Bernard Baars's global workspace model for consciousness.

It is a computer model of the neural correlates of consciousness programmed as a neural network. It attempts to reproduce the swarm behaviour of the brain's higher cognitive functions such as consciousness, decision-making[1] and the central executive functions. It was developed by cognitive neuroscientists Stanislas Dehaene and Jean-Pierre Changeux beginning in 1986.[2] It has been used to provide a predictive framework to the study of inattentional blindness and the solving of the Tower of London test.[3] [4]

History

The Dehaene–Changeux model was initially established as a spin glass neural network attempting to represent learning and to then provide a stepping stone towards artificial learning among other objectives. It would later be used to predict observable reaction times within the priming paradigm[5] and in inattentional blindness.

Structure

General structure

The Dehaene–Changeux model is a meta neural network (i.e. a network of neural networks) composed of a very large number of integrate-and-fire neurons programmed in either a stochastic or deterministic way. The neurons are organised in complex thalamo-cortical columns with long-range connexions and a critical role played by the interaction between von Economo's areas. Each thalamo-cortical column is composed of pyramidal cells and inhibitory interneurons receiving a long-distance excitatory neuromodulation which could represent noradrenergic input.

A swarm and a multi-agent system composed of neural networks

Among others Cohen & Hudson (2002) had already used "meta neural networks as intelligent agents for diagnosis".[6] Similarly to Cohen & Hudson, Dehaene & Changeux have established their model as an interaction of meta-neural networks (thalamocortical columns) themselves programmed in the manner of a "hierarchy of neural networks that together act as an intelligent agent", in order to use them as a system composed of a large scale of inter-connected intelligent agents for predicting the self-organized behaviour of the neural correlates of consciousness. Jain et al. (2002) had already clearly identified spiking neurons as intelligent agents[7] since the lower bound for computational power of networks of spiking neurons is the capacity to simulate in real-time for boolean-valued inputs any Turing machine.[8] The DCM being composed of a very large number of interacting sub-networks which are themselves intelligent agents, it is formally a multi-agent system programmed as a swarm or neural networks and a fortiori of spiking neurons.

Behavior

The DCM exhibits several surcritical emergent behaviors such as multistability and a Hopf bifurcation between two very different regimes which may represent either sleep or arousal with a various all-or-none behaviors which Dehaene et al. use to determine a testable taxonomy between different states of consciousness.[9]

Scholarly reception

Self-organized criticality

The Dehaene–Changeux model contributed to the study of nonlinearity and self-organized criticality in particular as an explanatory model of the brain's emergent behaviors, including consciousness. Studying the brain's phase-locking and large-scale synchronization, Kitzbichler et al. (2011a) confirmed that criticality is a property of human brain functional network organization at all frequency intervals in the brain's physiological bandwidth.[10]

Furthermore, exploring the neural dynamics of cognitive efforts after, inter alia, the Dehaene–Changeux model, Kitzbichler et al. (2011b) demonstrated how cognitive effort breaks the modularity of mind to make human brain functional networks transiently adopt a more efficient but less economical configuration.[11] Werner (2007a) used the Dehaene–Changeux global neuronal workspace to defend the use of statistical physics approaches for exploring phase transitions, scaling and universality properties of the so-called "Dynamic Core" of the brain, with relevance to the macroscopic electrical activity in EEG and EMG.[12] Furthermore, building from the Dehaene–Changeux model, Werner (2007b) proposed that the application of the twin concepts of scaling and universality of the theory of non-equilibrium phase transitions can serve as an informative approach for elucidating the nature of underlying neural-mechanisms, with emphasis on the dynamics of recursively reentrant activity flow in intracortical and cortico-subcortical neuronal loops. Friston (2000) also claimed that "the nonlinear nature of asynchronous coupling enables the rich, context-sensitive interactions that characterize real brain dynamics, suggesting that it plays a role in functional integration that may be as important as synchronous interactions".[13]

States of consciousness and phenomenology

It contributed to the study of phase transition in the brain under sedation, and notably GABA-ergic sedation such as that induced by propofol (Murphy et al. 2011, Stamatakis et al. 2010).[14] [15] The Dehaene–Changeux model was contrasted and cited in the study of collective consciousness and its pathologies (Wallace et al. 2007).[16] Boly et al. (2007) used the model for a reverse somatotopic study, demonstrating a correlation between baseline brain activity and somatosensory perception in humans.[17] Boly et al. (2008) also used the DCM in a study of the baseline state of consciousness of the human brain's default network.[18]

Adversarial collaboration to test the Dehaene–Changeux model and integrated information theory

In 2019, the Templeton Foundation announced funding in excess of $6,000,000 to test opposing empirical predictions of Dehaene–Changeux model and a rival theory (integrated information theory, or IIT).[19] [20] The originators of both theories signed off on experimental protocols and data analyses as well as the exact conditions that satisfy if their championed theory correctly predicted the outcome or not.[21] Initial results were revealed in June 2023.[22] None of the Dehaene–Changeux model predictions passed what was agreed upon pre-registration while two out of three of IIT's predictions passed that threshold.[23]

Publications

See also

External links

Notes and References

  1. Dehaene S, Changeux JP. "Reward-dependent learning in neuronal networks for planning and decision making". Prog Brain Res. 2000;126:217-29.
  2. Dehaene S, Changeux JP. "Experimental and theoretical approaches to conscious processing". Neuron. 2011 Apr 28;70(2):200-27.
  3. Changeux JP, Dehaene S. "Hierarchical neuronal modeling of cognitive functions: from synaptic transmission to the Tower of London". Comptes Rendus de l'Académie des Sciences, Série III. 1998 Feb–Mar;321(2–3):241-7.
  4. Dehaene S, Changeux JP, Nadal JP. "Neural networks that learn temporal sequences by selection". Proc Natl Acad Sci U S A. 1987 May;84(9):2727-31.
  5. Epub 2010 Jan 25. Van den Bussche E, Hughes G, Humbeeck NV, Reynvoet B "The relation between consciousness and attention: an empirical study using the priming paradigm". Conscious Cogn. 2010 Mar;19(1):86–97.
  6. Cohen, M.E.; Hudson, D.L.. "Meta neural networks as intelligent agents for diagnosis". Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference, 233–238.
  7. Book: Jain . Lakhmi C. . Chen . Zhengxin . Ichalkaranje . Nikhil . 2002 . Intelligent agents and their applications . Studies in fuzziness and soft computing . 98 . Heidelberg; New York . Physica-Verlag . 3790814695 . 49285672 . 10.1007/978-3-7908-1786-7.
  8. Maass . Wolfgang . January 1996 . Lower bounds for the computational power of networks of spiking neurons . Neural Computation . 8 . 1 . 1–40 . 10.1162/neco.1996.8.1.1 . 10.1.1.55.933.
  9. Dehaene S, Changeux JP, Naccache L, Sackur J, Sergent C. "Conscious, preconscious, and subliminal processing: a testable taxonomy". Trends Cogn Sci. 2006 May;10(5):204-11. Epub 2006 Apr 17.
  10. Kitzbichler MG, Smith ML, Christensen SR, Bullmore E. "Broadband criticality of human brain network synchronization". PLoS Comput. Biol. 2009 Mar;5(3):e1000314. Epub 2009 Mar 20.
  11. Kitzbichler MG, Henson RN, Smith ML, Nathan PJ, Bullmore ET. "Cognitive effort drives workspace configuration of human brain functional networks". J Neurosci. 2011 Jun 1;31(22):8259-70.
  12. Werner G. "Brain dynamics across levels of organization". J Physiol Paris. 2007 Jul–Nov;101(4–6):273-9. Epub 2008 Jan 8.
  13. 1692735 . 10724457 . 10.1098/rstb.2000.0560 . 355 . 1394. The labile brain. I. Neuronal transients and nonlinear coupling . 2000 . Philos Trans R Soc Lond B Biol Sci . 215–36 . Friston . KJ.
  14. Murphy M, Bruno MA, Riedner BA, Boveroux P, Noirhomme Q, Landsness EC, Brichant JF, Phillips C, Massimini M, Laureys S, Tononi G, Boly M. "Propofol anesthesia and sleep: a high-density EEG study". Sleep. 2011 Mar 1;34(3):283-91A.
  15. Stamatakis EA, Adapa RM, Absalom AR, Menon DK. "Changes in resting neural connectivity during propofol sedation". PLoS One. 2010 Dec 2;5(12):e14224.
  16. Wallace RM, Fullilove MT, Fullilove RE, Wallace DN. "Collective consciousness and its pathologies: understanding the failure of AIDS control and treatment in the United States" Theor Biol Med Model. 2007 Feb 26;4:10.
  17. Boly M, Balteau E, Schnakers C, Degueldre C, Moonen G, Luxen A, Phillips C, Peigneux P, Maquet P, Laureys S. "Baseline brain activity fluctuations predict somatosensory perception in humans". Proc Natl Acad Sci U S A. 2007 Jul 17;104(29):12187-92. Epub 2007 Jul 6.
  18. Boly M, Phillips C, Tshibanda L, Vanhaudenhuyse A, Schabus M, Dang-Vu TT, Moonen G, Hustinx R, Maquet P, Laureys S. "Intrinsic brain activity in altered states of consciousness: how conscious is the default mode of brain function?" Ann N Y Acad Sci. 2008;1129:119-29. Review.
  19. Web site: Accelerating Research on Consciousness: An Adversarial Collaboration to Test Contradictory Predictions of Global Neuronal Workspace and Integrated Information Theory . 2023-07-16 . www.templetonworldcharity.org . en.
  20. Melloni . Lucia . Mudrik . Liad . Pitts . Michael . Koch . Christof . 2021-05-28 . Making the hard problem of consciousness easier . Science . en . 372 . 6545 . 911–912 . 10.1126/science.abj3259 . 34045342 . 0036-8075.
  21. Melloni . Lucia . Mudrik . Liad . Pitts . Michael . Bendtz . Katarina . Ferrante . Oscar . Gorska . Urszula . Hirschhorn . Rony . Khalaf . Aya . Kozma . Csaba . Lepauvre . Alex . Liu . Ling . Mazumder . David . Richter . David . Zhou . Hao . Blumenfeld . Hal . 2023-02-10 . An adversarial collaboration protocol for testing contrasting predictions of global neuronal workspace and integrated information theory . PLOS ONE . en . 18 . 2 . e0268577 . 10.1371/journal.pone.0268577 . 1932-6203 . 9916582 . 36763595 . free . Initial preregistration: Melloni . Lucia . Mudrik . Liad . Pitts . Michael . Blumenfeld . Hal . Lange . Floris de . Jensen . Ole . Kreiman . Gabriel . Luo . Huan . Chalmers . David . Dehaene . Stanislas . 2019-01-16 . Adversarial Collaboration to test GNW and IIT . . en.
  22. Finkel . Elizabeth . 2023-06-30 . Consciousness hunt yields results but not clarity . Science . en . 380 . 6652 . 1309–1310 . 10.1126/science.adj4498 . 37384689 . 0036-8075.
  23. Cogitate Consortium, Ferrante, O., ..., Melloni, L. . bioRxiv . 2023-06-26 . An adversarial collaboration to critically evaluate theories of consciousness . bioRxiv. 10.1101/2023.06.23.546249 .

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