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      Neuronal oscillations on an ultra-slow timescale: daily rhythms in electrical activity and gene expression in the mammalian master circadian clockwork

      1 , 2 , 3
      European Journal of Neuroscience
      Wiley

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          Mechanisms of gamma oscillations.

          Gamma rhythms are commonly observed in many brain regions during both waking and sleep states, yet their functions and mechanisms remain a matter of debate. Here we review the cellular and synaptic mechanisms underlying gamma oscillations and outline empirical questions and controversial conceptual issues. Our main points are as follows: First, gamma-band rhythmogenesis is inextricably tied to perisomatic inhibition. Second, gamma oscillations are short-lived and typically emerge from the coordinated interaction of excitation and inhibition, which can be detected as local field potentials. Third, gamma rhythm typically concurs with irregular firing of single neurons, and the network frequency of gamma oscillations varies extensively depending on the underlying mechanism. To document gamma oscillations, efforts should be made to distinguish them from mere increases of gamma-band power and/or increased spiking activity. Fourth, the magnitude of gamma oscillation is modulated by slower rhythms. Such cross-frequency coupling may serve to couple active patches of cortical circuits. Because of their ubiquitous nature and strong correlation with the "operational modes" of local circuits, gamma oscillations continue to provide important clues about neuronal population dynamics in health and disease.
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            Dynamic predictions: oscillations and synchrony in top-down processing.

            Classical theories of sensory processing view the brain as a passive, stimulus-driven device. By contrast, more recent approaches emphasize the constructive nature of perception, viewing it as an active and highly selective process. Indeed, there is ample evidence that the processing of stimuli is controlled by top-down influences that strongly shape the intrinsic dynamics of thalamocortical networks and constantly create predictions about forthcoming sensory events. We discuss recent experiments indicating that such predictions might be embodied in the temporal structure of both stimulus-evoked and ongoing activity, and that synchronous oscillations are particularly important in this process. Coherence among subthreshold membrane potential fluctuations could be exploited to express selective functional relationships during states of expectancy or attention, and these dynamic patterns could allow the grouping and selection of distributed neuronal responses for further processing.
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              Coordinated transcription of key pathways in the mouse by the circadian clock.

              In mammals, circadian control of physiology and behavior is driven by a master pacemaker located in the suprachiasmatic nuclei (SCN) of the hypothalamus. We have used gene expression profiling to identify cycling transcripts in the SCN and in the liver. Our analysis revealed approximately 650 cycling transcripts and showed that the majority of these were specific to either the SCN or the liver. Genetic and genomic analysis suggests that a relatively small number of output genes are directly regulated by core oscillator components. Major processes regulated by the SCN and liver were found to be under circadian regulation. Importantly, rate-limiting steps in these various pathways were key sites of circadian control, highlighting the fundamental role that circadian clocks play in cellular and organismal physiology.
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                Author and article information

                Journal
                European Journal of Neuroscience
                Eur J Neurosci
                Wiley
                0953816X
                October 2018
                October 2018
                February 19 2018
                : 48
                : 8
                : 2696-2717
                Affiliations
                [1 ]Institute of Clinical and Biomedical Sciences; University of Exeter Medical School; University of Exeter; Exeter EX4 4PS UK
                [2 ]Department of Mathematical Sciences; New Jersey Institute of Technology; Newark NJ USA
                [3 ]Institute for Brain and Neuroscience Research; New Jersey Institute of Technology; Newark NJ USA
                Article
                10.1111/ejn.13856
                29396876
                a112bb40-d34b-4c64-9039-b42358fde40b
                © 2018

                http://doi.wiley.com/10.1002/tdm_license_1.1

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