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      The Cortical States of Wakefulness


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          Cortical neurons process information on a background of spontaneous, ongoing activity with distinct spatiotemporal profiles defining different cortical states. During wakefulness, cortical states alter constantly in relation to behavioral context, attentional level or general motor activity. In this review article, we will discuss our current understanding of cortical states in awake rodents, how they are controlled, their impact on sensory processing, and highlight areas for future research. A common observation in awake rodents is the rapid change in spontaneous cortical activity from high-amplitude, low-frequency (LF) fluctuations, when animals are quiet, to faster and smaller fluctuations when animals are active. This transition is typically thought of as a change in global brain state but recent work has shown variation in cortical states across regions, indicating the presence of a fine spatial scale control system. In sensory areas, the cortical state change is mediated by at least two convergent inputs, one from the thalamus and the other from cholinergic inputs in the basal forebrain. Cortical states have a major impact on the balance of activity between specific subtypes of neurons, on the synchronization between nearby neurons, as well as the functional coupling between distant cortical areas. This reorganization of the activity of cortical networks strongly affects sensory processing. Thus cortical states provide a dynamic control system for the moment-by-moment regulation of cortical processing.

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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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            Über das Elektrenkephalogramm des Menschen

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              Neural correlations, population coding and computation.

              How the brain encodes information in population activity, and how it combines and manipulates that activity as it carries out computations, are questions that lie at the heart of systems neuroscience. During the past decade, with the advent of multi-electrode recording and improved theoretical models, these questions have begun to yield answers. However, a complete understanding of neuronal variability, and, in particular, how it affects population codes, is missing. This is because variability in the brain is typically correlated, and although the exact effects of these correlations are not known, it is known that they can be large. Here, we review studies that address the interaction between neuronal noise and population codes, and discuss their implications for population coding in general.

                Author and article information

                Front Syst Neurosci
                Front Syst Neurosci
                Front. Syst. Neurosci.
                Frontiers in Systems Neuroscience
                Frontiers Media S.A.
                08 January 2019
                : 12
                : 64
                [1] 1Neural Circuits and Behaviour, Department of Neuroscience, Max Delbrück Center for Molecular Medicine (MDC) , Berlin, Germany
                [2] 2Neuroscience Research Center and Cluster of Excellence NeuroCure, Charité-Universitätsmedizin Berlin , Berlin, Germany
                [3] 3Laboratory of Sensory Processing, Brain Mind Institute, Faculty of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL) , Lausanne, Switzerland
                [4] 4Lyon Neuroscience Research Center, INSERM U1028/CNRS UMR5292, University Lyon 1 , Lyon, France
                Author notes

                Edited by: Keith B. Hengen, Washington University in St. Louis, United States

                Reviewed by: Ramón Reig, Universidad Miguel Hernández de Elche, Spain; Mehdi Adibi, University of New South Wales, Australia

                *Correspondence: James F. A. Poulet james.poulet@ 123456mdc-berlin.de Sylvain Crochet sylvain.crochet@ 123456epfl.ch
                Copyright © 2019 Poulet and Crochet.

                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) and the copyright owner(s) 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.

                : 30 September 2018
                : 11 December 2018
                Page count
                Figures: 6, Tables: 0, Equations: 0, References: 197, Pages: 18, Words: 15215
                Funded by: Ecole Polytechnique Fédérale de Lausanne 10.13039/501100001703

                brain states,barrel cortex,sensory processing,synchrony,acetylcholine
                brain states, barrel cortex, sensory processing, synchrony, acetylcholine


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