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      Asymmetry in kinematic generalization between visual and passive lead-in movements are consistent with a forward model in the sensorimotor system

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          Abstract

          In our daily life we often make complex actions comprised of linked movements, such as reaching for a cup of coffee and bringing it to our mouth to drink. Recent work has highlighted the role of such linked movements in the formation of independent motor memories, affecting the learning rate and ability to learn opposing force fields. In these studies, distinct prior movements (lead-in movements) allow adaptation of opposing dynamics on the following movement. Purely visual or purely passive lead-in movements exhibit different angular generalization functions of this motor memory as the lead-in movements are modified, suggesting different neural representations. However, we currently have no understanding of how different movement kinematics (distance, speed or duration) affect this recall process and the formation of independent motor memories. Here we investigate such kinematic generalization for both passive and visual lead-in movements to probe their individual characteristics. After participants adapted to opposing force fields using training lead-in movements, the lead-in kinematics were modified on random trials to test generalization. For both visual and passive modalities, recalled compensation was sensitive to lead-in duration and peak speed, falling off away from the training condition. However, little reduction in force was found with increasing lead-in distance. Interestingly, asymmetric transfer between lead-in movement modalities was also observed, with partial transfer from passive to visual, but very little vice versa. Overall these tuning effects were stronger for passive compared to visual lead-ins demonstrating the difference in these sensory inputs in regulating motor memories. Our results suggest these effects are a consequence of state estimation, with differences across modalities reflecting their different levels of sensory uncertainty arising as a consequence of dissimilar feedback delays.

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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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            Learning of action through adaptive combination of motor primitives.

            Understanding how the brain constructs movements remains a fundamental challenge in neuroscience. The brain may control complex movements through flexible combination of motor primitives, where each primitive is an element of computation in the sensorimotor map that transforms desired limb trajectories into motor commands. Theoretical studies have shown that a system's ability to learn action depends on the shape of its primitives. Using a time-series analysis of error patterns, here we show that humans learn the dynamics of reaching movements through a flexible combination of primitives that have gaussian-like tuning functions encoding hand velocity. The wide tuning of the inferred primitives predicts limitations on the brain's ability to represent viscous dynamics. We find close agreement between the predicted limitations and the subjects' adaptation to new force fields. The mathematical properties of the derived primitives resemble the tuning curves of Purkinje cells in the cerebellum. The activity of these cells may encode primitives that underlie the learning of dynamics.
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              Consolidation in human motor memory.

              Learning a motor skill sets in motion neural processes that continue to evolve after practice has ended, a phenomenon known as consolidation. Here we present psychophysical evidence for this, and show that consolidation of a motor skill was disrupted when a second motor task was learned immediately after the first. There was no disruption if four hours elapsed between learning the two motor skills, with consolidation occurring gradually over this period. Previous studies in humans and other primates have found this time-dependent disruption of consolidation only in explicit memory tasks, which rely on brain structures in the medial temporal lobe. Our results indicate that motor memories, which do not depend on the medial temporal lobe, can be transformed by a similar process of consolidation. By extending the phenomenon of consolidation to motor memory, our results indicate that distinct neural systems share similar characteristics when encoding and storing new information.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: ResourcesRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                29 January 2020
                2020
                : 15
                : 1
                : e0228083
                Affiliations
                [1 ] Centre for Robotics and Neural Systems, School of Computing, Electronics and Mathematics, University of Plymouth, Plymouth, England, United Kingdom
                [2 ] Department of Electrical and Computer Engineering, Institute for Cognitive Systems, Technical University of Munich, Munich, Germany
                [3 ] Neuromuscular Diagnostics, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany
                University of Exeter, UNITED KINGDOM
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-6041-9669
                Article
                PONE-D-19-15362
                10.1371/journal.pone.0228083
                6988934
                31995588
                a059efdb-ea50-4bf7-96c2-486f47be2c3d
                © 2020 Howard et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 29 May 2019
                : 7 January 2020
                Page count
                Figures: 6, Tables: 1, Pages: 21
                Funding
                Funded by: Bayerisches Staatsministerium für Wissenschaft, Forschung und Kunst (DE)
                Award ID: TUM Visiting Professor Program
                Award Recipient :
                Financial support for ISH was provided by the Centre for Robotics and Neural Systems at the University of Plymouth and by the Bavarian State Ministry for Science, Research & the Arts. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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                Custom metadata
                Data underlying the study is available from the Dryad repository (DOI: 10.5061/dryad.513bv1v).

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