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      Decoupling of timescales reveals sparse convergent CPG network in the adult spinal cord

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          Abstract

          During the generation of rhythmic movements, most spinal neurons receive an oscillatory synaptic drive. The neuronal architecture underlying this drive is unknown, and the corresponding network size and sparseness have not yet been addressed. If the input originates from a small central pattern generator (CPG) with dense divergent connectivity, it will induce correlated input to all receiving neurons, while sparse convergent wiring will induce a weak correlation, if any. Here, we use pairwise recordings of spinal neurons to measure synaptic correlations and thus infer the wiring architecture qualitatively. A strong correlation on a slow timescale implies functional relatedness and a common source, which will also cause correlation on fast timescale due to shared synaptic connections. However, we consistently find marginal coupling between slow and fast correlations regardless of neuronal identity. This suggests either sparse convergent connectivity or a CPG network with recurrent inhibition that actively decorrelates common input.

          Abstract

          Spinal CPGs transmit movement commands through rhythmic synaptic drive onto the spinal premotor network. Here, the authors use paired recordings to demonstrate that spinal neurons have decorrelated synaptic activity suggesting a CPG network with sparse convergent connectivity.

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          Most cited references59

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          Generating coherent patterns of activity from chaotic neural networks.

          Neural circuits display complex activity patterns both spontaneously and when responding to a stimulus or generating a motor output. How are these two forms of activity related? We develop a procedure called FORCE learning for modifying synaptic strengths either external to or within a model neural network to change chaotic spontaneous activity into a wide variety of desired activity patterns. FORCE learning works even though the networks we train are spontaneously chaotic and we leave feedback loops intact and unclamped during learning. Using this approach, we construct networks that produce a wide variety of complex output patterns, input-output transformations that require memory, multiple outputs that can be switched by control inputs, and motor patterns matching human motion capture data. Our results reproduce data on premovement activity in motor and premotor cortex, and suggest that synaptic plasticity may be a more rapid and powerful modulator of network activity than generally appreciated.
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            Decoding the organization of spinal circuits that control locomotion.

            Ole Kiehn (2016)
            Unravelling the functional operation of neuronal networks and linking cellular activity to specific behavioural outcomes are among the biggest challenges in neuroscience. In this broad field of research, substantial progress has been made in studies of the spinal networks that control locomotion. Through united efforts using electrophysiological and molecular genetic network approaches and behavioural studies in phylogenetically diverse experimental models, the organization of locomotor networks has begun to be decoded. The emergent themes from this research are that the locomotor networks have a modular organization with distinct transmitter and molecular codes and that their organization is reconfigured with changes to the speed of locomotion or changes in gait.
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              Decorrelated neuronal firing in cortical microcircuits.

              Correlated trial-to-trial variability in the activity of cortical neurons is thought to reflect the functional connectivity of the circuit. Many cortical areas are organized into functional columns, in which neurons are believed to be densely connected and to share common input. Numerous studies report a high degree of correlated variability between nearby cells. We developed chronically implanted multitetrode arrays offering unprecedented recording quality to reexamine this question in the primary visual cortex of awake macaques. We found that even nearby neurons with similar orientation tuning show virtually no correlated variability. Our findings suggest a refinement of current models of cortical microcircuit architecture and function: Either adjacent neurons share only a few percent of their inputs or, alternatively, their activity is actively decorrelated.
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                Author and article information

                Contributors
                runeb@sund.ku.dk
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                3 July 2019
                3 July 2019
                2019
                : 10
                : 2937
                Affiliations
                [1 ]ISNI 0000 0001 0674 042X, GRID grid.5254.6, Department of Neuroscience, Faculty of Health and Medical Sciences, , University of Copenhagen, ; Blegdamsvej 3, DK-2200 Copenhagen N, Denmark
                [2 ]ISNI 0000 0004 1936 8753, GRID grid.137628.9, Present Address: Neuroscience Institute, , New York University, ; New York, NY 10016 USA
                [3 ]ISNI 0000 0001 1014 0849, GRID grid.419491.0, Present Address: Department of Neuroscience, , Max Delbrück Center for Molecular Medicine (MDC), ; 13125 Berlin-Buch, Germany
                Author information
                http://orcid.org/0000-0002-8323-6160
                http://orcid.org/0000-0001-6175-1498
                http://orcid.org/0000-0002-2092-4791
                http://orcid.org/0000-0001-5630-4095
                http://orcid.org/0000-0002-8939-8099
                http://orcid.org/0000-0001-6376-9368
                Article
                10822
                10.1038/s41467-019-10822-9
                6610135
                31270315
                66736ec3-e628-4117-b291-b9c9567e02fe
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 13 August 2018
                : 4 June 2019
                Categories
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                Custom metadata
                © The Author(s) 2019

                Uncategorized
                spinal cord,neural circuits,sensorimotor processing,neurophysiology
                Uncategorized
                spinal cord, neural circuits, sensorimotor processing, neurophysiology

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