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35 - Models of Multi-Level Motor Control

from Part IV - Computational Modeling in Various Cognitive Fields

Published online by Cambridge University Press:  21 April 2023

By
Martin Giese ,
David Ungarish  and
Tamar Flash
Edited by
Ron Sun
Ron Sun
Affiliation:
Rensselaer Polytechnic Institute, New York
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Summary

Motor neuroscience centers on characterizing human movement, and the way it is represented and generated by the brain. A key concept in this field is that despite the rich repertoire of human movements and their variability across individuals, both the behavioral and neuronal aspects of movement are highly stereotypical, and can be understood in terms of basic principles or low dimensional systems. Highlighting this concept, this chapter outlines three core topics in this research field: (1) Trajectory planning, where prominent theories based on optimal control and geometric invariance aim at describing end-effector kinematics using basic unifying principles; (2) Compositionality, and specifically the ideas of motor primitives and muscle synergies that account for motion generation and muscle activations, using hierarchical low-dimensional structures; and (3) Neural control models, which regard the neural machinery that gives rise to sequences of motor commands, exploiting dynamical systems and artificial neural network approaches.

Keywords

trajectory planningcompositionalitymotor primitivesmuscle synergiesneural networkgeometric modelscontrol modelsneural population dynamics
Type
Chapter
Information
The Cambridge Handbook of Computational Cognitive Sciences , pp. 1135 - 1162
Publisher: Cambridge University Press
Print publication year: 2023

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References

Abeles, M., Diesmann, M., Flash, T., Geisel, T., Herrmann, M., & Teicher, M. (2013). Compositionality in neural control: an interdisciplinary study of scribbling movements in primates. Frontiers in Computational Neuroscience, 7, 103. https://doi.org/10.3389/fncom.2013.00103CrossRefGoogle ScholarPubMed
Abend, W., Bizzi, E., & Morasso, P. (1982). Human arm trajectory formation. Brain, 105 (Pt 2), 331–348. https://doi.org/10.1093/brain/105.2.331Google Scholar
Alessandro, C., Carbajal, J. P., & d’Avella, A. (2013). A computational analysis of motor synergies by dynamic response decomposition. Frontiers in Computational Neuroscience, 7, 191. https://doi.org/10.3389/fncom.2013.00191Google Scholar
Aoi, S., & Funato, T. (2016). Neuromusculoskeletal models based on the muscle synergy hypothesis for the investigation of adaptive motor control in locomotion via sensory-motor coordination. Neuroscience Research, 104, 88–95. https://doi.org/10.1016/j.neures.2015.11.005Google Scholar
Bennequin, D., Fuchs, R., Berthoz, A., & Flash, T. (2009). Movement timing and invariance arise from several geometries. PLoS Computational Biology, 5 (7), e1000426. https://doi.org/10.1371/journal.pcbi.1000426Google Scholar
Berger, D. J., Gentner, R., Edmunds, T., Pai, D. K., & d’Avella, A. (2013). Differences in adaptation rates after virtual surgeries provide direct evidence for modularity. Journal of Neuroscience, 33 (30), 12384–12394. https://doi.org/10.1523/JNEUROSCI.0122-13.2013Google Scholar
Berniker, M., & Kording, K. P. (2015). Deep networks for motor control functions. Frontiers in Computational Neuroscience, 9, 32. https://doi.org/10.3389/fncom.2015.00032Google Scholar
Bernstein, N. (1967). The Coordination and Regulation of Movements: Oxford: Pergamon Press.Google Scholar
Binet, A., & Courtier, J. (1893). Sur la vitesse des mouvements graphiques. Revue Philosophique de la France et de l’Étranger, Presses Universitaires de France Stable, pp. 664–671.Google Scholar
Bizzi, E., Giszter, S. F., Loeb, E., Mussa-Ivaldi, F. A., & Saltiel, P. (1995). Modular organization of motor behavior in the frog’s spinal cord. Trends in Neuroscience, 18 (10), 442–446. https://doi.org/10.1016/0166-2236(95)94494-pCrossRefGoogle ScholarPubMed
Buono, P. L., & Golubitsky, M. (2001). Models of central pattern generators for quadruped locomotion I. Primary gaits. Journal of Mathematical Biology, 42 (4), 291–326. https://doi.org/10.1007/s002850000058Google Scholar
Byadarhaly, K. V., Perdoor, M. C., & Minai, A. A. (2012). A modular neural model of motor synergies. Neural Networks, 32, 96–108. https://doi.org/10.1016/j.neunet.2012.02.003Google Scholar
Cabel, D. W., Cisek, P., & Scott, S. H. (2001). Neural activity in primary motor cortex related to mechanical loads applied to the shoulder and elbow during a postural task. Journal of Neurophysiology, 86 (4), 2102–2108. https://doi.org/10.1152/jn.2001.86.4.2102Google Scholar
Caminiti, R., Johnson, P. B., Galli, C., Ferraina, S., & Burnod, Y. (1991). Making arm movements within different parts of space: the premotor and motor cortical representation of a coordinate system for reaching to visual targets. Journal of Neuroscience, 11 (5), 1182–1197. www.ncbi.nlm.nih.gov/pubmed/2027042Google Scholar
Cartan, E. (1937). La théorie des groupes finis et continus et la géométrie différentielle, traitées par la méthode du repère mobile. Paris: Gauthier-Villars.Google Scholar
Catavitello, G., Ivanenko, Y., & Lacquaniti, F. (2018). A kinematic synergy for terrestrial locomotion shared by mammals and birds. Elife, 7. https://doi.org/10.7554/eLife.38190Google Scholar
Cheney, P. D., & Fetz, E. E. (1980). Functional classes of primate corticomotoneuronal cells and their relation to active force. Journal of Neurophysiology, 44 (4), 773–791. https://doi.org/10.1152/jn.1980.44.4.773Google Scholar
Chiovetto, E., Berret, B., & Pozzo, T. (2010). Tri-dimensional and triphasic muscle organization of whole-body pointing movements. Neuroscience, 170 (4), 1223–1238. https://doi.org/10.1016/j.neuroscience.2010.07.006Google Scholar
Chiovetto, E., d’Avella, A., & Giese, M. A. (2016). A unifying framework for the identification of motor primitives. BioArXiv, 1603.06879.Google Scholar
Chiovetto, E., & Giese, M. A. (2013). Kinematics of the coordination of pointing during locomotion. PLoS One, 8 (11), e79555. https://doi.org/10.1371/journal.pone.0079555CrossRefGoogle ScholarPubMed
Churchland, M. M., Cunningham, J. P., Kaufman, M. T., Foster, J. D., Nuyujukian, P., Ryu, S. I., & Shenoy, K. V. (2012). Neural population dynamics during reaching. Nature, 487 (7405), 51–56. https://doi.org/10.1038/nature11129Google Scholar
Churchland, M. M., & Shenoy, K. V. (2007a). Delay of movement caused by disruption of cortical preparatory activity. Journal of Neurophysiology, 97 (1), 348–359. https://doi.org/10.1152/jn.00808.2006Google Scholar
Churchland, M. M., & Shenoy, K. V. (2007b). Temporal complexity and heterogeneity of single-neuron activity in premotor and motor cortex. Journal of Neurophysiology, 97 (6), 4235–4257. https://doi.org/10.1152/jn.00095.2007CrossRefGoogle ScholarPubMed
Churchland, M. M., Yu, B. M., Ryu, S. I., Santhanam, G., & Shenoy, K. V. (2006). Neural variability in premotor cortex provides a signature of motor preparation. Journal of Neuroscience, 26 (14), 3697–3712. https://doi.org/10.1523/JNEUROSCI.3762-05.2006Google Scholar
Chvatal, S. A., Torres-Oviedo, G., Safavynia, S. A., & Ting, L. H. (2011). Common muscle synergies for control of center of mass and force in nonstepping and stepping postural behaviors. Journal of Neurophysiology, 106 (2), 999–1015. https://doi.org/10.1152/jn.00549.2010Google Scholar
D’Andola, M., Cesqui, B., Portone, A., Fernandez, L., Lacquaniti, F., & d’Avella, A. (2013). Spatiotemporal characteristics of muscle patterns for ball catching. Frontiers in Computational Neuroscience, 7, 107. https://doi.org/10.3389/fncom.2013.00107Google Scholar
d’Avella, A., & Bizzi, E. (2005). Shared and specific muscle synergies in natural motor behaviors. Proceedings of the National Academy of Sciences of the United States of America, 102 (8), 3076–3081. https://doi.org/10.1073/pnas.0500199102Google Scholar
D’Avella, A., Fernandez, L., Portone, A., & Lacquaniti, F. (2008). Modulation of phasic and tonic muscle synergies with reaching direction and speed. Journal of Neurophysiology, 100 (3), 1433–1454. https://doi.org/10.1152/jn.01377.2007Google Scholar
d’Avella, A., Giese, M., Ivanenko, Y. P., Schack, T., & Flash, T. (2015). Editorial: Modularity in motor control: from muscle synergies to cognitive action representation. Frontiers in Computational Neuroscience, 9, 126. https://doi.org/10.3389/fncom.2015.00126Google ScholarPubMed
d’Avella, A., Portone, A., Fernandez, L., & Lacquaniti, F. (2006). Control of fast-reaching movements by muscle synergy combinations. Journal of Neuroscience, 26 (30), 7791–7810. https://doi.org/10.1523/JNEUROSCI.0830-06.2006Google Scholar
d’Avella, A., Saltiel, P., & Bizzi, E. (2003). Combinations of muscle synergies in the construction of a natural motor behavior. Nature Neuroscience, 6 (3), 300–308. https://doi.org/10.1038/nn1010Google Scholar
d’Avella, A., & Tresch, M. C. (2002). Modularity in the motor system: decomposition of muscle patterns as combinations of time-varying synergies. Advances in Neural Information Processing Systems, 1, 141–148.Google Scholar
Dayan, E., Casile, A., Levit-Binnun, N., Giese, M. A., Hendler, T., & Flash, T. (2007). Neural representations of kinematic laws of motion: evidence for action-perception coupling. Proceedings of the National Academy of Sciences of the United States of America, 104 (51), 20582–20587. https://doi.org/10.1073/pnas.0710033104Google Scholar
Delis, I., Panzeri, S., Pozzo, T., & Berret, B. (2014). A unifying model of concurrent spatial and temporal modularity in muscle activity. Journal of Neurophysiology, 111 (3), 675–693. https://doi.org/10.1152/jn.00245.2013Google Scholar
DeWolf, T., Stewart, T. C., Slotine, J. J., & Eliasmith, C. (2016). A spiking neural model of adaptive arm control. Biological Sciences, 283 (1843). https://doi.org/10.1098/rspb.2016.2134Google Scholar
Dominici, N., Ivanenko, Y. P., Cappellini, G., et al. (2011). Locomotor primitives in newborn babies and their development. Science, 334 (6058), 997–999. https://doi.org/10.1126/science.1210617Google Scholar
Elsayed, G. F., Lara, A. H., Kaufman, M. T., Churchland, M. M., & Cunningham, J. P. (2016). Reorganization between preparatory and movement population responses in motor cortex. Nature Communications, 7, 13239. https://doi.org/10.1038/ncomms13239Google Scholar
Fetz, E. E., Perlmutter, S. I., Prut, Y., Seki, K., & Votaw, S. (2002). Roles of primate spinal interneurons in preparation and execution of voluntary hand movement. Brain Research Reviews, 40 (1–3), 53–65. https://doi.org/10.1016/s0165-0173(02)00188-1Google Scholar
Flash, T., & Handzel, A. A. (2007). Affine differential geometry analysis of human arm movements. Biological Cybernetics, 96 (6), 577–601. https://doi.org/10.1007/s00422-007-0145-5Google Scholar
Flash, T., & Hochner, B. (2005). Motor primitives in vertebrates and invertebrates. Current Opinion in Neurobiology, 15 (6), 660–666. https://doi.org/10.1016/j.conb.2005.10.011Google Scholar
Flash, T., & Hogan, N. (1985). The coordination of arm movements: an experimentally confirmed mathematical model. Journal of Neuroscience, 5 (7), 1688–1703.Google Scholar
Flash, T., Karklinsky, M., Fuchs, R., Berthoz, A., Bennequin, D., & Meirovitch, Y. (2019). Motor compositionality and timing: combined geometrical and optimization approaches. In Venture, G., Laumond, J. P., & Watier, B. (Eds.), Biomechanics of Anthropomorphic Systems. Springer Tracts in Advanced Robotics (Vol. 124, pp. 155–184). Cham: Springer.Google Scholar
Giszter, S. F. (2015). Motor primitives: new data and future questions. Current Opinion in Neurobiology, 33, 156–165. https://doi.org/10.1016/j.conb.2015.04.004Google Scholar
Giszter, S. F., Mussa-Ivaldi, F. A., & Bizzi, E. (1993). Convergent force fields organized in the frog’s spinal cord. Journal of Neuroscience, 13 (2), 467–491. www.ncbi.nlm.nih.gov/pubmed/8426224Google Scholar
Graziano, M. (2006). The organization of behavioral repertoire in motor cortex. Annual Review of Neuroscience, 29, 105–134. https://doi.org/10.1146/annurev.neuro.29.051605.112924Google Scholar
Gribble, P. L., & Ostry, D. J. (1996). Origins of the power law relation between movement velocity and curvature: modeling the effects of muscle mechanics and limb dynamics. Journal of Neurophysiology, 76 (5), 2853–2860. https://doi.org/10.1152/jn.1996.76.5.2853Google Scholar
Guigon, E., Baraduc, P., & Desmurget, M. (2007). Computational motor control: redundancy and invariance. Journal of Neurophysiology, 97 (1), 331–347. https://doi.org/10.1152/jn.00290.2006Google Scholar
Hagio, S., & Kouzaki, M. (2018). Modularity speeds up motor learning by overcoming mechanical bias in musculoskeletal geometry. Journal of the Royal Society Interface, 15 (147), 20180249. https://doi.org/10.1098/rsif.2018.0249Google Scholar
Harris, C. M., & Wolpert, D. M. (1998). Signal-dependent noise determines motor planning. Nature, 394 (6695), 780–784. https://doi.org/10.1038/29528Google Scholar
Hart, C. B., & Giszter, S. F. (2010). A neural basis for motor primitives in the spinal cord. Journal of Neuroscience, 30 (4), 1322–1336. https://doi.org/10.1523/JNEUROSCI.5894-08.2010CrossRefGoogle ScholarPubMed
Hogan, N. (1984). An organizing principle for a class of voluntary movements. Journal of Neuroscience, 4 (11), 2745–2754. www.ncbi.nlm.nih.gov/pubmed/6502203Google Scholar
Hogan, N., & Sternad, D. (2012). Dynamic primitives of motor behavior. Biological Cybernetics, 106 (11–12), 727–739. https://doi.org/10.1007/s00422-012-0527-1Google Scholar
Holden, D., Saito, J., & Komura, T. (2016). A deep learning framework for character motion synthesis and editing. ACM Transactions on Graphics, 138 (4).Google Scholar
Huh, D., & Sejnowski, T. J. (2015). Spectrum of power laws for curved hand movements. Proceedings of the National Academy of Sciences, 112 (29), E3950–E3958. https://doi.org/10.1073/pnas.1510208112Google Scholar
Huh, D., & Todorov, E. (2009). Real-time motor control using recurrent neural networks. In 2009 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (pp. 42–49). https://doi.org/10.1109/ADPRL.2009.4927524Google Scholar
Ijspeert, A. J. (2008). Central pattern generators for locomotion control in animals and robots: a review. Neural Networks, 21 (4), 642–653. https://doi.org/10.1016/j.neunet.2008.03.014Google Scholar
Ijspeert, A. J., Nakanishi, J., Hoffmann, H., Pastor, P., & Schaal, S. (2013). Dynamical movement primitives: learning attractor models for motor behaviors. Neural Computation, 25 (2), 328–373. https://doi.org/10.1162/NECO_a_00393Google Scholar
Ivanenko, Y. P., Poppele, R. E., & Lacquaniti, F. (2004). Five basic muscle activation patterns account for muscle activity during human locomotion. Journal of Physiology, 556 (Pt 1), 267–282. https://doi.org/10.1113/jphysiol.2003.057174Google Scholar
Jaeger, H., & Haas, H. (2004). Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science, 304 (5667), 78–80. https://doi.org/10.1126/science.1091277Google Scholar
Kadmon Harpaz, N., Flash, T., & Dinstein, I. (2014). Scale-invariant movement encoding in the human motor system. Neuron, 81 (2), 452–462. https://doi.org/10.1016/j.neuron.2013.10.058Google Scholar
Kalaska, J. F., Cohen, D. A., Hyde, M. L., & Prud’homme, M. (1989). A comparison of movement direction-related versus load direction-related activity in primate motor cortex, using a two-dimensional reaching task. Journal of Neuroscience, 9 (6), 2080–2102. www.ncbi.nlm.nih.gov/pubmed/2723767Google Scholar
Kaufman, M. T., Churchland, M. M., Ryu, S. I., & Shenoy, K. V. (2014). Cortical activity in the null space: permitting preparation without movement. Nature Neuroscience, 17 (3), 440–448. https://doi.org/10.1038/nn.3643Google Scholar
Kaufman, M. T., Churchland, M. M., & Shenoy, K. V. (2013). The roles of monkey M1 neuron classes in movement preparation and execution. Journal of Neurophysiology, 110 (4), 817–825. https://doi.org/10.1152/jn.00892.2011CrossRefGoogle ScholarPubMed
Kelso, J. S. (1995). Dynamic Patterns: The Self-Organization of Brain and Behavior. Cambridge, MA: MIT Press.Google Scholar
Kim, T., Hamade, K. C., Todorov, D., et al. (2017). Reward-based motor adaptation mediated by basal ganglia. Frontiers in Computational Neuroscience, 11. https://doi.org/10.3389/fncom.2017.00019Google Scholar
Kober, J., & Peters, J. (2011). Policy search for motor primitives in robotics. Machine Learning, 84 (1–2), 171–203.Google Scholar
Kuo, L. C., Chen, S. W., Lin, C. J., Lin, W. J., Lin, S. C., & Su, F. C. (2013). The force synergy of human digits in static and dynamic cylindrical grasps. PLoS One, 8 (3), e60509. https://doi.org/10.1371/journal.pone.0060509Google Scholar
Lacquaniti, F., Terzuolo, C., & Viviani, P. (1983). The law relating the kinematic and figural aspects of drawing movements. Acta Psychologica (Amst), 54 (1–3), 115–130. https://doi.org/10.1016/0001-6918(83)90027-6Google Scholar
Maass, W., Natschlager, T., & Markram, H. (2002). Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Computation, 14 (11), 2531–2560. https://doi.org/10.1162/089976602760407955Google Scholar
Maoz, U., Portugaly, E., Flash, T., & Weiss, Y. (2006). Noise and the two-thirds power law. In Advances in Neural Information Processing Systems, Vancouver, British Columbia, Canada.Google Scholar
McCrea, D. A., & Rybak, I. A. (2008). Organization of mammalian locomotor rhythm and pattern generation. Brain Research Reviews, 57 (1), 134–146. https://doi.org/10.1016/j.brainresrev.2007.08.006Google Scholar
Meirovitch, Y. (2014). Movement decomposition and compositionality based on geometric and kinematic principles. Ph.D. dissertation, Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.Google Scholar
Meirovitch, Y., Harris, H., Dayan, E., Arieli, A., & Flash, T. (2015). Alpha and beta band event-related desynchronization reflects kinematic regularities. Journal of Neuroscience, 35 (4), 1627–1637.Google Scholar
Merel, J., Botvinick, M., & Wayne, G. (2019). Hierarchical motor control in mammals and machines. Nature Communication, 10 (1), 5489. https://doi.org/10.1038/s41467-019-13239-6Google Scholar
Merkle, L. A., Layne, C. S., Bloomberg, J. J., & Zhang, J. J. (1998). Using factor analysis to identify neuromuscular synergies during treadmill walking. Journal of Neuroscience Methods, 82 (2), 207–214. https://doi.org/10.1016/s0165-0270(98)00054-5Google Scholar
Moran, D. W., & Schwartz, A. B. (1999). Motor cortical representation of speed and direction during reaching. Journal of Neurophysiology, 82 (5), 2676–2692. https://doi.org/10.1152/jn.1999.82.5.2676Google Scholar
Mukovskiy, A., Slotine, J. J. E., & Giese, M. A. (2013). Dynamically stable control of articulated crowds. Journal of Computer Science, 4, 304–310.Google Scholar
Mukovskiy, A., Vassallo, C., Naveau, M., Stasse, O., Souères, P. E., & Giese, M. A. (2017). Adaptive synthesis of dynamically feasible full-body movements for the humanoid robot HRP-2 by flexible combination of learned dynamic movement primitives. Robotics and Autonomous Systems, 91(C), 270–283. https://doi.org/10.1016/j.robot.2017.01.010Google Scholar
Mussa-Ivaldi, F. A., Giszter, S. F., & Bizzi, E. (1994). Linear combinations of primitives in vertebrate motor control. Proceedings of the National Academy of Sciences , 91 (16), 7534–7538. https://doi.org/10.1073/pnas.91.16.7534Google Scholar
Omlor, L., & Giese, M. A. (2011). Anechoic blind source separation using Wigner marginals. Journal of Machine Learning Research, 12, 1111–1148.Google Scholar
Overduin, S. A., d’Avella, A., Roh, J., Carmena, J. M., & Bizzi, E. (2015). Representation of muscle synergies in the primate brain. Journal of Neuroscience, 35 (37), 12615–12624. https://doi.org/10.1523/JNEUROSCI.4302-14.2015Google Scholar
Pandarinath, C., O’Shea, D. J., Collins, J., et al. (2018). Inferring single-trial neural population dynamics using sequential auto-encoders. Nature Methods, 15 (10), 805–815. https://doi.org/10.1038/s41592-018-0109-9Google Scholar
Paraschos, A., Daniel, C., Peters, J., & Neumann, G. (2018). Using probabilistic movement primitives in robotics. Autonomous Robots, 42, 529–551.Google Scholar
Poggio, T., & Reichardt, W. (1976). Visual control of orientation behaviour in the fly. Part II. Towards the underlying neural interactions. Quarterly Reviews of Biophysics, 9 (3), 377–438. https://doi.org/10.1017/s0033583500002535Google Scholar
Pollick, F. E., Maoz, U., Handzel, A. A., Giblin, P. J., Sapiro, G., & Flash, T. (2009). Three-dimensional arm movements at constant equi-affine speed. Cortex, 45 (3), 325–339. https://doi.org/10.1016/j.cortex.2008.03.010Google Scholar
Pollick, F. E., & Sapiro, G. (1997). Constant affine velocity predicts the 1/3 power law of planar motion perception and generation. Vision Research, 37 (3), 347–353. https://doi.org/10.1016/s0042-6989(96)00116-2Google Scholar
Richardson, M. J., & Flash, T. (2002). Comparing smooth arm movements with the two-thirds power law and the related segmented-control hypothesis. Journal of Neuroscience, 22 (18), 8201–8211. www.ncbi.nlm.nih.gov/pubmed/12223574Google Scholar
Rückert, E., & d’Avella, A. (2013). Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems. Frontiers in Computational Neuroscience, 7, 138. https://doi.org/10.3389/fncom.2013.00138Google Scholar
Russo, M., D’Andola, M., Portone, A., Lacquaniti, F., & d’Avella, A. (2014). Dimensionality of joint torques and muscle patterns for reaching. Frontiers in Computational Neuroscience, 8, 24. https://doi.org/10.3389/fncom.2014.00024Google Scholar
Santello, M., Flanders, M., & Soechting, J. F. (1998). Postural hand synergies for tool use. Journal of Neuroscience, 18 (23), 10105–10115. www.ncbi.nlm.nih.gov/pubmed/9822764Google Scholar
Saxena, S., & Cunningham, J. P. (2019). Towards the neural population doctrine. Current Opinion in Neurobiology, 55, 103–111. https://doi.org/10.1016/j.conb.2019.02.002Google Scholar
Schaal, S. (2006). Dynamic movement primitives: a framework for motor control in humans and humanoid robotics. In Kimura, H., Tsuchiya, K., Ishiguro, A., & Witte, H. (Eds.), Adaptive Motion of Animals and Machines (pp. 261–280). London: Springer.Google Scholar
Schaal, S., Kotosaka, S., & Sternad, D. (2000). Nonlinear dynamical systems as movement primitives. Paper presented at the Humanoids2000, First IEEE-RAS International Conference on Humanoid Robots, Cambridge, MA.Google Scholar
Schaal, S., Peters, J., Nakanishi, J., & Ijspeert, A. (2005). Learning movement primitives. Paper presented at the Robotics Research, The Eleventh International Symposium.Google Scholar
Schaal, S., & Sternad, D. (2001). Origins and violations of the 2/3 power law in rhythmic three-dimensional arm movements. Experimental Brain Research, 136 (1), 60–72. https://doi.org/10.1007/s002210000505Google Scholar
Schaal, S., Sternad, D., Osu, R., & Kawato, M. (2004). Rhythmic arm movement is not discrete. Nature Neuroscience, 7 (10), 1136–1143. https://doi.org/10.1038/nn1322CrossRefGoogle Scholar
Scholz, J. P., & Schöner, G. (1999). The uncontrolled manifold concept: identifying control variables for a functional task. Experimental Brain Research, 126 (3), 289–306. https://doi.org/10.1007/s002210050738Google Scholar
Schöner, G. (1990). A dynamic theory of coordination of discrete movement. Biological Cybernetics, 63 (4), 257–270. https://doi.org/10.1007/BF00203449Google Scholar
Sergio, L. E., & Kalaska, J. F. (1998). Changes in the temporal pattern of primary motor cortex activity in a directional isometric force versus limb movement task. Journal of Neurophysiology, 80 (3), 1577–1583. https://doi.org/10.1152/jn.1998.80.3.1577Google Scholar
Singh, R. E., Iqbal, K., White, G., & Hutchinson, T. E. (2018). A systematic review on muscle synergies: from building blocks of motor behavior to a neurorehabilitation tool. Applied Bionics and Biomechanics, 2018, 3615368. https://doi.org/10.1155/2018/3615368Google Scholar
Sreenivasa, M., Ayusawa, K., & Nakamura, Y. (2016). Modeling and identification of a realistic spiking neural network and musculoskeletal model of the human arm, and an application to the stretch reflex. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24 (5), 591–602. https://doi.org/10.1109/TNSRE.2015.2478858Google Scholar
Sussillo, D., Jozefowicz, R., Abbott, L. F., & Pandarinath, C. (2016). LFADS: latent factor analysis via dynamical systems. arXiv, 1608.06315.Google Scholar
Taborri, J., Agostini, V., Artemiadis, P. K., et al. (2018). Feasibility of muscle synergy outcomes in clinics, robotics, and sports: a systematic review. Applied Bionics and Biomechanics, 2018, 3934698. https://doi.org/10.1155/2018/3934698Google Scholar
Takei, T., Confais, J., Tomatsu, S., Oya, T., & Seki, K. (2017). Neural basis for hand muscle synergies in the primate spinal cord. Proceedings of the National Academy of Sciences, 114 (32), 8643–8648. https://doi.org/10.1073/pnas.1704328114Google Scholar
Tanaka, H. (2016). Modeling the motor cortex: optimality, recurrent neural networks, and spatial dynamics. Neuroscience Research, 104, 64–71. https://doi.org/10.1016/j.neures.2015.10.012Google Scholar
Tanneberg, D., Paraschos, A., Peters, J., & Rueckert, E. (2016). Deep spiking networks for model-based planning in humanoids. Paper presented at the International Conference on Humanoid Robots (HUMANOIDS).Google Scholar
Taubert, N., Christensen, A., Endres, D., & Giese, M. A. (2012). Online simulation of emotional interactive behaviors with hierarchical Gaussian process dynamical models. In Proceedings of the ACM Symposium on Applied Perception, Los Angeles, California.Google Scholar
Teka, W. W., Hamade, K. C., Barnett, W. H., et al. (2017). From the motor cortex to the movement and back again. PLoS One, 12 (6), e0179288.Google Scholar
Tesio, L., Rota, V., & Perucca, L. (2011). The 3D trajectory of the body centre of mass during adult human walking: evidence for a speed-curvature power law. Journal of Biomechanics, 44 (4), 732–740. https://doi.org/10.1016/j.jbiomech.2010.10.035Google Scholar
Thoroughman, K. A., & Shadmehr, R. (2000). Learning of action through adaptive combination of motor primitives. Nature, 407 (6805), 742–747. https://doi.org/10.1038/35037588Google Scholar
Ting, L. H., & Macpherson, J. M. (2005). A limited set of muscle synergies for force control during a postural task. Journal of Neurophysiology, 93 (1), 609–613. https://doi.org/10.1152/jn.00681.2004Google Scholar
Todorov, E., & Jordan, M. I. (1998). Smoothness maximization along a predefined path accurately predicts the speed profiles of complex arm movements. Journal of Neurophysiology, 80 (2), 696–714. https://doi.org/10.1152/jn.1998.80.2.696Google Scholar
Todorov, E., & Jordan, M. I. (2002). Optimal feedback control as a theory of motor coordination. Nature Neuroscience, 5 (11), 1226–1235. https://doi.org/10.1038/nn963Google Scholar
Tresch, M. C., & Bizzi, E. (1999). Responses to spinal microstimulation in the chronically spinalized rat and their relationship to spinal systems activated by low threshold cutaneous stimulation. Experimental Brain Research, 129 (3), 401–416. https://doi.org/10.1007/s002210050908Google Scholar
Tresch, M. C., Cheung, V. C., & d’Avella, A. (2006). Matrix factorization algorithms for the identification of muscle synergies: evaluation on simulated and experimental data sets. Journal of Neurophysiology, 95 (4), 2199–2212. https://doi.org/10.1152/jn.00222.2005Google Scholar
Tresch, M. C., & Jarc, A. (2009). The case for and against muscle synergies. Current Opinion in Neurobiology, 19 (6), 601–607. https://doi.org/10.1016/j.conb.2009.09.002Google Scholar
Umilta, M. A., Escola, L., Intskirveli, I., et al. (2008). When pliers become fingers in the monkey motor system. Proceedings of the National Academy of Sciences, 105 (6), 2209–2213. https://doi.org/10.1073/pnas.0705985105Google Scholar
Uno, Y., Kawato, M., & Suzuki, R. (1989). Formation and control of optimal trajectory in human multijoint arm movement. Minimum torque-change model. Biological Cybernetics, 61 (2), 89–101. https://doi.org/10.1007/BF00204593Google Scholar
Viviani, P., & Cenzato, M. (1985). Segmentation and coupling in complex movements. Journal of Experimental Psychology: Human Perception and Performance, 11 (6), 828–845. https://doi.org/10.1037//0096-1523.11.6.828Google Scholar
Viviani, P., & Flash, T. (1995). Minimum-jerk, two-thirds power law, and isochrony: converging approaches to movement planning. Journal of Experimental Psychology: Human Perception and Performance, 21 (1), 32–53. https://doi.org/10.1037//0096-1523.21.1.32Google Scholar
Viviani, P., & McCollum, G. (1983). The relation between linear extent and velocity in drawing movements. Neuroscience, 10 (1), 211–218. https://doi.org/10.1016/0306-4522(83)90094-5Google Scholar
Viviani, P., & Schneider, R. (1991). A developmental study of the relationship between geometry and kinematics in drawing movements. Journal of Experimental Psychology: Human Perception and Performance, 17 (1), 198–218. https://doi.org/10.1037//0096-1523.17.1.198Google Scholar
Vyas, S., Golub, M. D., Sussillo, D., & Shenoy, K. V. (2020). Computation through neural population dynamics. Annual Review of Neuroscience, 43, 249–275. https://doi.org/10.1146/annurev-neuro-092619-094115Google Scholar
Wensing, P., & Slotine, J. J. S. (2016). Sparse control for dynamic movement primitives. arXiv, CoRR, abs/1611.05066.Google Scholar
Wojtara, T., Alnajjar, F., Shimoda, S., & Kimura, H. (2014). Muscle synergy stability and human balance maintenance. Journal of NeuroEngineering and Rehabilitation, 11, 129. https://doi.org/10.1186/1743-0003-11-129Google Scholar
Yanai, Y., Adamit, N., Harel, R., Israel, Z., & Prut, Y. (2007). Connected corticospinal sites show enhanced tuning similarity at the onset of voluntary action. Journal of Neuroscience, 27 (45), 12349–12357. https://doi.org/10.1523/JNEUROSCI.3127-07.2007Google Scholar

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