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      Consciousness is supported by near-critical slow cortical electrodynamics

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          Significance

          What changes in the brain when we lose consciousness? One possibility is that the loss of consciousness corresponds to a transition of the brain’s electric activity away from edge-of-chaos criticality, or the knife’s edge in between stability and chaos. Recent mathematical developments have produced tools for testing this hypothesis, which we apply to cortical recordings from diverse brain states. We show that the electric activity of the cortex is indeed poised near the boundary between stability and chaos during conscious states and transitions away from this boundary during unconsciousness and that this transition disrupts cortical information processing.

          Abstract

          Mounting evidence suggests that during conscious states, the electrodynamics of the cortex are poised near a critical point or phase transition and that this near-critical behavior supports the vast flow of information through cortical networks during conscious states. Here, we empirically identify a mathematically specific critical point near which waking cortical oscillatory dynamics operate, which is known as the edge-of-chaos critical point, or the boundary between stability and chaos. We do so by applying the recently developed modified 0-1 chaos test to electrocorticography (ECoG) and magnetoencephalography (MEG) recordings from the cortices of humans and macaques across normal waking, generalized seizure, anesthesia, and psychedelic states. Our evidence suggests that cortical information processing is disrupted during unconscious states because of a transition of low-frequency cortical electric oscillations away from this critical point; conversely, we show that psychedelics may increase the information richness of cortical activity by tuning low-frequency cortical oscillations closer to this critical point. Finally, we analyze clinical electroencephalography (EEG) recordings from patients with disorders of consciousness (DOC) and show that assessing the proximity of slow cortical oscillatory electrodynamics to the edge-of-chaos critical point may be useful as an index of consciousness in the clinical setting.

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          Most cited references121

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          Neural networks and physical systems with emergent collective computational abilities.

          J Hopfield (1982)
          Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.
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            Real-time computing without stable states: a new framework for neural computation based on perturbations.

            A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real time. We propose a new computational model for real-time computing on time-varying input that provides an alternative to paradigms based on Turing machines or attractor neural networks. It does not require a task-dependent construction of neural circuits. Instead, it is based on principles of high-dimensional dynamical systems in combination with statistical learning theory and can be implemented on generic evolved or found recurrent circuitry. It is shown that the inherent transient dynamics of the high-dimensional dynamical system formed by a sufficiently large and heterogeneous neural circuit may serve as universal analog fading memory. Readout neurons can learn to extract in real time from the current state of such recurrent neural circuit information about current and past inputs that may be needed for diverse tasks. Stable internal states are not required for giving a stable output, since transient internal states can be transformed by readout neurons into stable target outputs due to the high dimensionality of the dynamical system. Our approach is based on a rigorous computational model, the liquid state machine, that, unlike Turing machines, does not require sequential transitions between well-defined discrete internal states. It is supported, as the Turing machine is, by rigorous mathematical results that predict universal computational power under idealized conditions, but for the biologically more realistic scenario of real-time processing of time-varying inputs. Our approach provides new perspectives for the interpretation of neural coding, the design of experiments and data analysis in neurophysiology, and the solution of problems in robotics and neurotechnology.
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              On the Complexity of Finite Sequences

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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                10 February 2022
                15 February 2022
                10 February 2022
                : 119
                : 7
                : e2024455119
                Affiliations
                [1] aDepartment of Psychology, University of California , Los Angeles, CA 90095;
                [2] bHelen Wills Neuroscience Institute, University of California , Berkeley, CA 94704;
                [3] cDepartment of Psychology, University of California , Berkeley, CA 94704;
                [4] dLaboratory of Neuro Imaging, Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California , Los Angeles, CA 90033;
                [5] eDepartment of Anesthesiology and Intensive Care, University Medical Center , 72076 Tübingen, Germany;
                [6] fConsejo Nacional de Investigaciones Científicas y Técnicas de Argentina , C1425 Buenos Aires, Argentina;
                [7] gFacultad de Ciencia y Tecnología, Universidad Autónoma de Entre Ríos , E3202 Paraná, Entre Ríos, Argentina;
                [8] hGrupo de Análisis de Neuroimágenes, Instituo de Matemática Aplicada del Litoral , S3000 Santa Fe, Argentina;
                [9] iSchool of Pharmacy, Faculty of Medical and Health Sciences, The University of Auckland , 1010 Auckland, New Zealand;
                [10] jNeuropsychopharmacology Unit, Centre for Psychiatry, Imperial College London , London SW7 2AZ, United Kingdom;
                [11] kCentre for Psychedelic Research, Department of Psychiatry, Imperial College London , London SW7 2AZ, United Kingdom;
                [12] lDepartment of Neurological Surgery, University of California , Irvine, CA 92697;
                [13] mBrain Injury Research Center, Department of Neurosurgery, University of California , Los Angeles, CA 90095;
                [14] nRedwood Center for Theoretical Neuroscience, University of California , Berkeley, CA 94704
                Author notes
                1To whom correspondence may be addressed. Email: danieltoker@ 123456g.ucla.edu .

                Edited by Emery Brown, Department of Anesthesia and Critical Care, Massachusetts General Hospital, Boston, MA; received December 2, 2020; accepted December 20, 2021

                Author contributions: D.T., I.P., J.D.L., F.T.S., R.T.K., and M.D. designed research; D.T., J.D.L., J.F., D.M.M., S.M., R.C.-H., M.P., P.M.V., and M.M.M. performed research; D.M.M. contributed new reagents/analytic tools; D.T., J.D.L., and J.F. analyzed data; and D.T. wrote the paper.

                Author information
                https://orcid.org/0000-0002-1967-6110
                https://orcid.org/0000-0001-8382-4344
                https://orcid.org/0000-0002-1953-0875
                https://orcid.org/0000-0002-6780-2855
                https://orcid.org/0000-0001-5511-3780
                https://orcid.org/0000-0001-8686-1685
                Article
                202024455
                10.1073/pnas.2024455119
                8851554
                35145021
                610253f4-3664-4a31-a5f2-f454ce54b96b
                Copyright © 2022 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                : 20 December 2021
                Page count
                Pages: 12
                Funding
                Funded by: HHS | National Institutes of Health (NIH) 100000002
                Award ID: MH111737
                Award Recipient : Mark D'Esposito
                Funded by: HHS | NIH | National Institute of Neurological Disorders and Stroke (NINDS) 100000065
                Award ID: NS21135
                Award Recipient : Robert T Knight
                Categories
                424
                Biological Sciences
                Neuroscience

                consciousness,criticality,anesthesia,epilepsy,psychedelics
                consciousness, criticality, anesthesia, epilepsy, psychedelics

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