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      Spinal microcircuits comprising dI3 interneurons are necessary for motor functional recovery following spinal cord transection.

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          Abstract

          The spinal cord has the capacity to coordinate motor activities such as locomotion. Following spinal transection, functional activity can be regained, to a degree, following motor training. To identify microcircuits involved in this recovery, we studied a population of mouse spinal interneurons known to receive direct afferent inputs and project to intermediate and ventral regions of the spinal cord. We demonstrate that while dI3 interneurons are not necessary for normal locomotor activity, locomotor circuits rhythmically inhibit them and dI3 interneurons can activate these circuits. Removing dI3 interneurons from spinal microcircuits by eliminating their synaptic transmission left locomotion more or less unchanged, but abolished functional recovery, indicating that dI3 interneurons are a necessary cellular substrate for motor system plasticity following transection. We suggest that dI3 interneurons compare inputs from locomotor circuits with sensory afferent inputs to compute sensory prediction errors that then modify locomotor circuits to effect motor recovery.

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          Most cited references56

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          NIH Image to ImageJ: 25 years of image analysis.

          For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
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            Principles of sensorimotor learning.

            The exploits of Martina Navratilova and Roger Federer represent the pinnacle of motor learning. However, when considering the range and complexity of the processes that are involved in motor learning, even the mere mortals among us exhibit abilities that are impressive. We exercise these abilities when taking up new activities - whether it is snowboarding or ballroom dancing - but also engage in substantial motor learning on a daily basis as we adapt to changes in our environment, manipulate new objects and refine existing skills. Here we review recent research in human motor learning with an emphasis on the computational mechanisms that are involved.
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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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                Author and article information

                Journal
                Elife
                eLife
                eLife Sciences Organisation, Ltd.
                2050-084X
                2050-084X
                Dec 15 2016
                : 5
                Affiliations
                [1 ] Department of Biology, Brain and Mind Research Institute, University of Ottawa, Ottawa, Canada.
                [2 ] Department of Medical Neuroscience, Dalhousie University, Halifax, Canada.
                [3 ] Division of Neurosurgery, Department of Surgery, Dalhousie University, Halifax, Canada.
                [4 ] Sobell Department of Motor Neuroscience and Movement Disorders, Institute of Neurology, University College London, London, United Kingdom.
                Article
                10.7554/eLife.21715
                5218533
                27977000
                378033c0-536e-4082-aa5a-bc11f08bf431
                History

                comparator neurons,motor learning,mouse,neuroscience,sensory prediction error,spinal cord injury,treadmill training

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