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      The Local Field Potential Reflects Surplus Spike Synchrony

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          Abstract

          While oscillations of the local field potential (LFP) are commonly attributed to the synchronization of neuronal firing rate on the same time scale, their relationship to coincident spiking in the millisecond range is unknown. Here, we present experimental evidence to reconcile the notions of synchrony at the level of spiking and at the mesoscopic scale. We demonstrate that only in time intervals of significant spike synchrony that cannot be explained on the basis of firing rates, coincident spikes are better phase locked to the LFP than predicted by the locking of the individual spikes. This effect is enhanced in periods of large LFP amplitudes. A quantitative model explains the LFP dynamics by the orchestrated spiking activity in neuronal groups that contribute the observed surplus synchrony. From the correlation analysis, we infer that neurons participate in different constellations but contribute only a fraction of their spikes to temporally precise spike configurations. This finding provides direct evidence for the hypothesized relation that precise spike synchrony constitutes a major temporally and spatially organized component of the LFP.

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          Most cited references71

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          Neuronal synchrony: a versatile code for the definition of relations?

          W. Singer (1999)
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            Large-scale recording of neuronal ensembles.

            How does the brain orchestrate perceptions, thoughts and actions from the spiking activity of its neurons? Early single-neuron recording research treated spike pattern variability as noise that needed to be averaged out to reveal the brain's representation of invariant input. Another view is that variability of spikes is centrally coordinated and that this brain-generated ensemble pattern in cortical structures is itself a potential source of cognition. Large-scale recordings from neuronal ensembles now offer the opportunity to test these competing theoretical frameworks. Currently, wire and micro-machined silicon electrode arrays can record from large numbers of neurons and monitor local neural circuits at work. Achieving the full potential of massively parallel neuronal recordings, however, will require further development of the neuron-electrode interface, automated and efficient spike-sorting algorithms for effective isolation and identification of single neurons, and new mathematical insights for the analysis of network properties.
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              Internal brain state regulates membrane potential synchrony in barrel cortex of behaving mice.

              Internal brain states form key determinants for sensory perception, sensorimotor coordination and learning. A prominent reflection of different brain states in the mammalian central nervous system is the presence of distinct patterns of cortical synchrony, as revealed by extracellular recordings of the electroencephalogram, local field potential and action potentials. Such temporal correlations of cortical activity are thought to be fundamental mechanisms of neuronal computation. However, it is unknown how cortical synchrony is reflected in the intracellular membrane potential (V(m)) dynamics of behaving animals. Here we show, using dual whole-cell recordings from layer 2/3 primary somatosensory barrel cortex in behaving mice, that the V(m) of nearby neurons is highly correlated during quiet wakefulness. However, when the mouse is whisking, an internally generated state change reduces the V(m) correlation, resulting in a desynchronized local field potential and electroencephalogram. Action potential activity was sparse during both quiet wakefulness and active whisking. Single action potentials were driven by a large, brief and specific excitatory input that was not present in the V(m) of neighbouring cells. Action potential initiation occurs with a higher signal-to-noise ratio during active whisking than during quiet periods. Therefore, we show that an internal brain state dynamically regulates cortical membrane potential synchrony during behaviour and defines different modes of cortical processing.
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                Author and article information

                Journal
                Cereb Cortex
                cercor
                cercor
                Cerebral Cortex (New York, NY)
                Oxford University Press
                1047-3211
                1460-2199
                December 2011
                20 April 2011
                20 April 2011
                : 21
                : 12
                : 2681-2695
                Affiliations
                [1 ]RIKEN Brain Science Institute, Wako-shi, Saitama 351-0198, Japan
                [2 ]Mediterranean Institute of Cognitive Neuroscience (INCM), Centre National de la Recherche Scientifique—University Aix-Marseille 2, 13402 Marseille Cedex 20, France
                [3 ]Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway
                [4 ]Institute of Neuroscience and Medicine (INM-6), Research Center Jülich, 52425 Jülich, Germany
                [5 ]Bernstein Center for Computational Neuroscience, 10115 Berlin, Germany
                Author notes
                Address correspondence to Dr. Michael Denker, RIKEN Brain Science Institute, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan. Email: mdenker@ 123456brain.riken.jp .
                Article
                10.1093/cercor/bhr040
                3209854
                21508303
                a4eb3d47-b062-430a-b5aa-d5b05b2298d1
                © The Authors 2011. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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                Categories
                Articles

                Neurology
                oscillation,population signals,motor cortex,synchrony
                Neurology
                oscillation, population signals, motor cortex, synchrony

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