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      Spontaneous cortical activity is transiently poised close to criticality

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          Abstract

          Brain activity displays a large repertoire of dynamics across the sleep-wake cycle and even during anesthesia. It was suggested that criticality could serve as a unifying principle underlying the diversity of dynamics. This view has been supported by the observation of spontaneous bursts of cortical activity with scale-invariant sizes and durations, known as neuronal avalanches, in recordings of mesoscopic cortical signals. However, the existence of neuronal avalanches in spiking activity has been equivocal with studies reporting both its presence and absence. Here, we show that signs of criticality in spiking activity can change between synchronized and desynchronized cortical states. We analyzed the spontaneous activity in the primary visual cortex of the anesthetized cat and the awake monkey, and found that neuronal avalanches and thermodynamic indicators of criticality strongly depend on collective synchrony among neurons, LFP fluctuations, and behavioral state. We found that synchronized states are associated to criticality, large dynamical repertoire and prolonged epochs of eye closure, while desynchronized states are associated to sub-criticality, reduced dynamical repertoire, and eyes open conditions. Our results show that criticality in cortical dynamics is not stationary, but fluctuates during anesthesia and between different vigilance states.

          Author summary

          Cortical activity spontaneously displays a large diversity of dynamics in different behavioral states and at multiple spatiotemporal scales. In the last decades, a unifying principle has been proposed to underlie the diversity and scale-invariance of brain dynamics: Criticality, a particular state of complex systems in which order and disorder coexist. On the other hand, the cortex can exhibit a continuum of states with different levels of collective synchrony and LFP fluctuations. Here, we ask how criticality measures vary as the cortex spontaneously fluctuates across different states. Using recordings from the primary visual cortex of the anaesthetized cat and awake monkey, we show that spiking patterns of cortical states differ in their proximity to criticality and their relation to the vigilance state.

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          Most cited references64

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          Alpha-band oscillations, attention, and controlled access to stored information

          Alpha-band oscillations are the dominant oscillations in the human brain and recent evidence suggests that they have an inhibitory function. Nonetheless, there is little doubt that alpha-band oscillations also play an active role in information processing. In this article, I suggest that alpha-band oscillations have two roles (inhibition and timing) that are closely linked to two fundamental functions of attention (suppression and selection), which enable controlled knowledge access and semantic orientation (the ability to be consciously oriented in time, space, and context). As such, alpha-band oscillations reflect one of the most basic cognitive processes and can also be shown to play a key role in the coalescence of brain activity in different frequencies.
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            A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters

            J. C. Dunn (1973)
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              Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons.

              N Brunel (2000)
              The dynamics of networks of sparsely connected excitatory and inhibitory integrate-and-fire neurons are studied analytically. The analysis reveals a rich repertoire of states, including synchronous states in which neurons fire regularly; asynchronous states with stationary global activity and very irregular individual cell activity; and states in which the global activity oscillates but individual cells fire irregularly, typically at rates lower than the global oscillation frequency. The network can switch between these states, provided the external frequency, or the balance between excitation and inhibition, is varied. Two types of network oscillations are observed. In the fast oscillatory state, the network frequency is almost fully controlled by the synaptic time scale. In the slow oscillatory state, the network frequency depends mostly on the membrane time constant. Finite size effects in the asynchronous state are also discussed.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                24 May 2017
                May 2017
                : 13
                : 5
                : e1005543
                Affiliations
                [1 ]Unité de Neuroscience, Information et Complexité (UNIC), CNRS, Gif-sur-Yvette, France
                [2 ]Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
                [3 ]Institut des Neurosciences de la Timone, CNRS, Marseille, France
                [4 ]Bernstein Center for Computational Neuroscience, Freiburg, Germany
                [5 ]Dept. of Computational Science and Technology, School of Computer Science and Communication, KTH, Royal Institute of Technology, Stockholm, Sweden
                [6 ]Institució Catalana de la Recerca i Estudis Avançats, Universitat Pompeu Fabra, Barcelona, Spain
                [7 ]Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
                [8 ]School of Psychological Sciences, Monash University, Melbourne, Clayton, Victoria, Australia
                Hamburg University, GERMANY
                Author notes

                The authors have declared that no competing interests exist.

                • Conceptualization: GH APA.

                • Formal analysis: GH APA.

                • Investigation: GH CM GB.

                • Methodology: GH APA.

                • Supervision: AK FC GD YF.

                • Writing – original draft: GH APA CM GB AK FC GD YF.

                Author information
                http://orcid.org/0000-0002-7069-0639
                http://orcid.org/0000-0003-1446-7392
                http://orcid.org/0000-0002-5234-6260
                http://orcid.org/0000-0001-7916-2640
                Article
                PCOMPBIOL-D-16-01987
                10.1371/journal.pcbi.1005543
                5464673
                28542191
                4eafce42-ca03-4da3-96a2-cbf3278e50c0
                © 2017 Hahn et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 8 December 2016
                : 26 April 2017
                Page count
                Figures: 9, Tables: 3, Pages: 29
                Funding
                GH, CM and YF were supported by the CNRS, the Agence Nationale de la Recherche (ANR: V1-Complex) https://www.cnrs.fr. GH and GB were financed by the initial training network program FACETS-ITN (PITN-GA-2009- 237955) http://facets.kip.uni-heidelberg.de/ITN/. APA was supported by SEMAINE ERA-Net NEURON Project and by a Juan de la Cierva fellowship (IJCI-2014-21066) from the Spanish Ministry of Economy and Competitiveness. CM, GD and YF received funding from the EC grants BrainScales (FP7-2010- IST-FETPI 269921) and the flagship Human Brain Project (n.604102) https://www.humanbrainproject.eu/. The Utah array recordings were made possible through a loan by S.Grün (Research Center Jülich, INM6, Germany) and were part of a collaborative work with S. Grün and A. Riehle (INT, Marseille). AK received funding from the German Federal Ministry of Education and Research (BMBF 01GQ0420 to BCCN Freiburg and 01GQ0830 to BFNT Freiburg/Tübingen) https://www.bmbf.de/en/. GD is supported by the ERC Advanced Grant: DYSTRUCTURE (n. 295129), by the Spanish Research Project PSI2016-75688-P and by the the European Union’s Horizon 2020 research and innovation programme under grant agreement n. 720270 (HBP SGA1). GD obtained support from the ERC Advanced Grant DYSTRUCTURE (n. 295129) gustavodecolab.com/dystructure/ and the Spanish Research Project PSI2013-42091- P. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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                Custom metadata
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                2017-06-08
                All relevant data are within the paper and its Supporting Information files.

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