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      Selection of cortical dynamics for motor behaviour by the basal ganglia

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          Abstract

          The basal ganglia and cortex are strongly implicated in the control of motor preparation and execution. Re-entrant loops between these two brain areas are thought to determine the selection of motor repertoires for instrumental action. The nature of neural encoding and processing in the motor cortex as well as the way in which selection by the basal ganglia acts on them is currently debated. The classic view of the motor cortex implementing a direct mapping of information from perception to muscular responses is challenged by proposals viewing it as a set of dynamical systems controlling muscles. Consequently, the common idea that a competition between relatively segregated cortico-striato-nigro-thalamo-cortical channels selects patterns of activity in the motor cortex is no more sufficient to explain how action selection works. Here, we contribute to develop the dynamical view of the basal ganglia–cortical system by proposing a computational model in which a thalamo-cortical dynamical neural reservoir is modulated by disinhibitory selection of the basal ganglia guided by top-down information, so that it responds with different dynamics to the same bottom-up input. The model shows how different motor trajectories can so be produced by controlling the same set of joint actuators. Furthermore, the model shows how the basal ganglia might modulate cortical dynamics by preserving coarse-grained spatiotemporal information throughout cortico-cortical pathways.

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          The online version of this article (doi:10.1007/s00422-015-0662-6) contains supplementary material, which is available to authorized users.

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          Parallel organization of functionally segregated circuits linking basal ganglia and cortex.

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            Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication.

            We present a method for learning nonlinear systems, echo state networks (ESNs). ESNs employ artificial recurrent neural networks in a way that has recently been proposed independently as a learning mechanism in biological brains. The learning method is computationally efficient and easy to use. On a benchmark task of predicting a chaotic time series, accuracy is improved by a factor of 2400 over previous techniques. The potential for engineering applications is illustrated by equalizing a communication channel, where the signal error rate is improved by two orders of magnitude.
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              Neural population dynamics during reaching

              Most theories of motor cortex have assumed that neural activity represents movement parameters. This view derives from an analogous approach to primary visual cortex, where neural activity represents patterns of light. Yet it is unclear how well that analogy holds. Single-neuron responses in motor cortex appear strikingly complex, and there is marked disagreement regarding which movement parameters are represented. A better analogy might be with other motor systems, where a common principle is rhythmic neural activity. We found that motor cortex responses during reaching contain a brief but strong oscillatory component, something quite unexpected for a non-periodic behavior. Oscillation amplitude and phase followed naturally from the preparatory state, suggesting a mechanistic role for preparatory neural activity. These results demonstrate unexpected yet surprisingly simple structure in the population response. That underlying structure explains many of the confusing features of individual-neuron responses.
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                Author and article information

                Contributors
                +39-06-44595230 , francesco.mannella@istc.cnr.it
                gianluca.baldassarre@istc.cnr.it
                Journal
                Biol Cybern
                Biol Cybern
                Biological Cybernetics
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0340-1200
                1432-0770
                4 November 2015
                4 November 2015
                2015
                : 109
                : 6
                : 575-595
                Affiliations
                Laboratory of Computational Embodied Neuroscience, Institute of Cognitive Sciences and Technologies, National Research Council (CNR-ISTC-LOCEN), Via San Martino della Battaglia 44, 00185 Rome, Italy
                Author information
                http://orcid.org/0000-0002-7308-0844
                Article
                662
                10.1007/s00422-015-0662-6
                4656718
                26537483
                8558f1fc-5ad7-43e0-9312-5d323e8d03dd
                © The Author(s) 2015

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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.

                History
                : 21 October 2014
                : 29 September 2015
                Categories
                Original Article
                Custom metadata
                © Springer-Verlag Berlin Heidelberg 2015

                Robotics
                basal ganglia,cortex,motor action,selection ,cortical dynamics,reservoir computing
                Robotics
                basal ganglia, cortex, motor action, selection , cortical dynamics, reservoir computing

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