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      On the functional organization and operational principles of the motor cortex

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          Recent studies on the functional organization and operational principles of the motor cortex (MCx), taken together, strongly support the notion that the MCx controls the muscle synergies subserving movements in an integrated manner. For example, during pointing the shoulder, elbow and wrist muscles appear to be controlled as a coupled functional system, rather than singly and separately. The recurrent pattern of intrinsic synaptic connections between motor cortical points is likely part of the explanation for this operational principle. So too is the reduplicated, non-contiguous and intermingled representation of muscles in the MCx. A key question addressed in this article is whether the selection of movement related muscle synergies is a dynamic process involving the moment to moment functional linking of a variety of motor cortical points, or rather the selection of fixed patterns embedded in the MCx circuitry. It will be suggested that both operational principles are probably involved. We also discuss the neural mechanisms by which cortical points may be dynamically linked to synthesize movement related muscle synergies. Separate corticospinal outputs sum linearly and lead to a blending of the movements evoked by activation of each point on its own. This operational principle may simplify the synthesis of motor commands. We will discuss two possible mechanisms that may explain linear summation of outputs. We have observed that the final posture of the arm when pointing to a given spatial location is relatively independent of its starting posture. From this observation and the recurrent nature of the MCx intrinsic connectivity we hypothesize that the basic mode of operation of the MCx is to associate spatial location to final arm posture. We explain how the recurrent network connectivity operates to generate the muscle activation patterns (synergies) required to move the arm and hold it in its final position.

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          Most cited references 54

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          Chaos in neuronal networks with balanced excitatory and inhibitory activity.

          Neurons in the cortex of behaving animals show temporally irregular spiking patterns. The origin of this irregularity and its implications for neural processing are unknown. The hypothesis that the temporal variability in the firing of a neuron results from an approximate balance between its excitatory and inhibitory inputs was investigated theoretically. Such a balance emerges naturally in large networks of excitatory and inhibitory neuronal populations that are sparsely connected by relatively strong synapses. The resulting state is characterized by strongly chaotic dynamics, even when the external inputs to the network are constant in time. Such a network exhibits a linear response, despite the highly nonlinear dynamics of single neurons, and reacts to changing external stimuli on time scales much smaller than the integration time constant of a single neuron.
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            Neocortical network activity in vivo is generated through a dynamic balance of excitation and inhibition.

            The recurrent excitatory and inhibitory connections between and within layers of the cerebral cortex are fundamental to the operation of local cortical circuits. Models of cortical function often assume that recurrent excitation and inhibition are balanced, and we recently demonstrated that spontaneous network activity in vitro contains a precise balance of excitation and inhibition; however, the existence of a balance between excitation and inhibition in the intact and spontaneously active cerebral cortex has not been directly tested. We examined this hypothesis in the prefrontal cortex in vivo, during the slow (<1 Hz) oscillation in ketamine-xylazine-anesthetized ferrets. We measured persistent network activity (Up states) with extracellular multiple unit and local field potential recording, while simultaneously recording synaptic currents in nearby cells. We determined the reversal potential and conductance change over time during Up states and found that the body of Up state activity exhibited a steady reversal potential (-37 mV on average) for hundreds of milliseconds, even during substantial (21 nS on average) changes in membrane conductance. Furthermore, we found that both the initial and final segments of the Up state were characterized by significantly more depolarized reversal potentials and concomitant increases in excitatory conductance, compared with the stable middle portions of Up states. This ongoing temporal evolution between excitation and inhibition, which exhibits remarkable proportionality within and across neurons in active local networks, may allow for rapid transitions between relatively stable network states, permitting the modulation of neuronal responsiveness in a behaviorally relevant manner.
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              Instantaneous correlation of excitation and inhibition during ongoing and sensory-evoked activities.

              Temporal and quantitative relations between excitatory and inhibitory inputs in the cortex are central to its activity, yet they remain poorly understood. In particular, a controversy exists regarding the extent of correlation between cortical excitation and inhibition. Using simultaneous intracellular recordings in pairs of nearby neurons in vivo, we found that excitatory and inhibitory inputs are continuously synchronized and correlated in strength during spontaneous and sensory-evoked activities in the rat somatosensory cortex.

                Author and article information

                1Brain and Movement Laboratory, Section of Biomedical Engineering, Department of Electrical Engineering, Danish Technical University Lyngby, Denmark
                2Laboratoire de Neurophysique et Physiologie du Systeme Moteur, CNRS UMR 8119, Université Paris-Descartes Paris, France
                3Department of Physiology, Northwestern University Chicago, IL, USA
                4Department of Health and Human Physiology, University of Iowa Iowa City, IA, USA
                Author notes

                Edited by: Gordon M. G. Shepherd, Northwestern University, USA

                Reviewed by: Jon H. Kaas, Vanderbilt University, USA; Paul D. Cheney, University of Kansas Medical Center, USA

                *Correspondence: Charles Capaday, Brain and Movement Laboratory, Section of Biomedical Engineering, Department of Electrical Engineering, Danish Technical University, Ørsteds Plads, Building 349, 2800 Kgs. Lyngby, Denmark. e-mail: charles.capaday@
                Front Neural Circuits
                Front Neural Circuits
                Front. Neural Circuits
                Frontiers in Neural Circuits
                Frontiers Media S.A.
                18 April 2013
                : 7
                Copyright © 2013 Capaday, Ethier, Van Vreeswijk and Darling.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.

                Figures: 9, Tables: 0, Equations: 0, References: 64, Pages: 15, Words: 10610
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