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      Spike avalanches in vivo suggest a driven, slightly subcritical brain state

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          Abstract

          In self-organized critical (SOC) systems avalanche size distributions follow power-laws. Power-laws have also been observed for neural activity, and so it has been proposed that SOC underlies brain organization as well. Surprisingly, for spiking activity in vivo, evidence for SOC is still lacking. Therefore, we analyzed highly parallel spike recordings from awake rats and monkeys, anesthetized cats, and also local field potentials from humans. We compared these to spiking activity from two established critical models: the Bak-Tang-Wiesenfeld model, and a stochastic branching model. We found fundamental differences between the neural and the model activity. These differences could be overcome for both models through a combination of three modifications: (1) subsampling, (2) increasing the input to the model (this way eliminating the separation of time scales, which is fundamental to SOC and its avalanche definition), and (3) making the model slightly sub-critical. The match between the neural activity and the modified models held not only for the classical avalanche size distributions and estimated branching parameters, but also for two novel measures (mean avalanche size, and frequency of single spikes), and for the dependence of all these measures on the temporal bin size. Our results suggest that neural activity in vivo shows a mélange of avalanches, and not temporally separated ones, and that their global activity propagation can be approximated by the principle that one spike on average triggers a little less than one spike in the next step. This implies that neural activity does not reflect a SOC state but a slightly sub-critical regime without a separation of time scales. Potential advantages of this regime may be faster information processing, and a safety margin from super-criticality, which has been linked to epilepsy.

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          Long-range temporal correlations and scaling behavior in human brain oscillations.

          The human brain spontaneously generates neural oscillations with a large variability in frequency, amplitude, duration, and recurrence. Little, however, is known about the long-term spatiotemporal structure of the complex patterns of ongoing activity. A central unresolved issue is whether fluctuations in oscillatory activity reflect a memory of the dynamics of the system for more than a few seconds. We investigated the temporal correlations of network oscillations in the normal human brain at time scales ranging from a few seconds to several minutes. Ongoing activity during eyes-open and eyes-closed conditions was recorded with simultaneous magnetoencephalography and electroencephalography. Here we show that amplitude fluctuations of 10 and 20 Hz oscillations are correlated over thousands of oscillation cycles. Our analyses also indicated that these amplitude fluctuations obey power-law scaling behavior. The scaling exponents were highly invariant across subjects. We propose that the large variability, the long-range correlations, and the power-law scaling behavior of spontaneous oscillations find a unifying explanation within the theory of self-organized criticality, which offers a general mechanism for the emergence of correlations and complex dynamics in stochastic multiunit systems. The demonstrated scaling laws pose novel quantitative constraints on computational models of network oscillations. We argue that critical-state dynamics of spontaneous oscillations may lend neural networks capable of quick reorganization during processing demands.
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            Crackling Noise

            Crackling noise arises when a system responds to changing external conditions through discrete, impulsive events spanning a broad range of sizes. A wide variety of physical systems exhibiting crackling noise have been studied, from earthquakes on faults to paper crumpling. Because these systems exhibit regular behavior over many decades of sizes, their behavior is likely independent of microscopic and macroscopic details, and progress can be made by the use of very simple models. The fact that simple models and real systems can share the same behavior on a wide range of scales is called universality. We illustrate these ideas using results for our model of crackling noise in magnets, explaining the use of the renormalization group and scaling collapses. This field is still developing: we describe a number of continuing challenges.
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              Real-time computation at the edge of chaos in recurrent neural networks.

              Depending on the connectivity, recurrent networks of simple computational units can show very different types of dynamics, ranging from totally ordered to chaotic. We analyze how the type of dynamics (ordered or chaotic) exhibited by randomly connected networks of threshold gates driven by a time-varying input signal depends on the parameters describing the distribution of the connectivity matrix. In particular, we calculate the critical boundary in parameter space where the transition from ordered to chaotic dynamics takes place. Employing a recently developed framework for analyzing real-time computations, we show that only near the critical boundary can such networks perform complex computations on time series. Hence, this result strongly supports conjectures that dynamical systems that are capable of doing complex computational tasks should operate near the edge of chaos, that is, the transition from ordered to chaotic dynamics.
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                Author and article information

                Contributors
                Journal
                Front Syst Neurosci
                Front Syst Neurosci
                Front. Syst. Neurosci.
                Frontiers in Systems Neuroscience
                Frontiers Media S.A.
                1662-5137
                24 June 2014
                2014
                : 8
                : 108
                Affiliations
                [1] 1Department of Non-linear Dynamics, Max Planck Institute for Dynamics and Self-Organization Göttingen, Germany
                [2] 2Bernstein Center for Computational Neuroscience Göttingen, Germany
                [3] 3Frankfurt Institute for Advanced Studies Frankfurt, Germany
                [4] 4Department of Neurophysiology, Max Planck Institute for Brain Research Frankfurt, Germany
                [5] 5Magnetoencephalography Unit, Brain Imaging Center, Johann Wolfgang Goethe University Frankfurt, Germany
                [6] 6Ernst Strüngmann Institute for Neuroscience in Cooperation with Max Planck Society Frankfurt, Germany
                [7] 7Department of Biomedical Engineering, University of Los Andes Bogotá, Colombia
                [8] 8Neural Information Processing Group, Department of Software Engineering and Theoretical Computer Science, TU Berlin Berlin, Germany
                [9] 9Bernstein Center for Computational Neuroscience Berlin, Germany
                [10] 10Centre de Recherche de l’Institut du Cerveau et de la Moelle épinière, Hôpital de la Pitié-Salpêtrière, INSERM UMRS 975—CNRS UMR 7225-UPMC Paris, France
                [11] 11Department of Psychology, Faculty of Humanities and Social Sciences, University of Zagreb Zagreb, Croatia
                [12] 12Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics Tübingen, Germany
                Author notes

                Edited by: Valentina Pasquale, Fondazione Istituto Italiano di Tecnologia, Italy

                Reviewed by: John M. Beggs, Indiana University, USA; Mauro Copelli, Federal University of Pernambuco, Brazil; Silvia Scarpetta, University of Salerno, Italy; Miguel Angel Muñoz, Universidad de Granada, Spain

                *Correspondence: Viola Priesemann, Max Planck Institute for Dynamics and Self-Organization, Am Fassberg 17, 37077 Göttingen, Germany e-mail: v.priesemann@ 123456gmx.de

                This article was submitted to the journal Frontiers in Systems Neuroscience.

                Article
                10.3389/fnsys.2014.00108
                4068003
                25009473
                72d49012-1355-4fae-ab50-1fd1f0b24743
                Copyright © 2014 Priesemann, Wibral, Valderrama, Pröpper, Le Van Quyen, Geisel, Triesch, Nikolić and Munk.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 08 February 2014
                : 21 May 2014
                Page count
                Figures: 11, Tables: 0, Equations: 5, References: 90, Pages: 17, Words: 15282
                Categories
                Neuroscience
                Original Research Article

                Neurosciences
                self-organized criticality,human intracranial recordings,spike train analysis,highly parallel recordings,spiking neural networks,multiunit activity,cortex,monkeys

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