Neural primitives for motion control

The neural control of movement requires the ability to deal with changes, both in the environment and in the parameters that characterize the mechanical structure of the organism. Here we discuss the three types of coordinate representations that sensory and motor systems use to generate and control movements, and argue that the intrinsic redundancy of the musculoeskeletal system can be exploited to implement control signals that result in successful task completion while allowing for variance in trajectory parameters not relevant to the task. We also argue that muscle synergies activated through the stimulation of specific loci along the spinal cord provide evidence for the existence of a vocabulary of motor primitives that can be combined, either simultaneously or sequentially, to generate a broad repertoire of complex movements.

[1]  E. Bizzi,et al.  Neural, mechanical, and geometric factors subserving arm posture in humans , 1985, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[2]  S. Grillner,et al.  Modeling of the Spinal Neuronal Circuitry Underlying Locomotion in a Lower Vertebratea , 1998, Annals of the New York Academy of Sciences.

[3]  B. Hochner,et al.  Patterns of Arm Muscle Activation Involved in Octopus Reaching Movements , 1998, The Journal of Neuroscience.

[4]  S. Grillner,et al.  Synaptic effects of intraspinal stretch receptor neurons mediating movement-related feedback during locomotion , 1990, Brain Research.

[5]  P. Rack,et al.  The effects of length and stimulus rate on tension in the isometric cat soleus muscle , 1969, The Journal of physiology.

[6]  P. Morasso Spatial control of arm movements , 2004, Experimental Brain Research.

[7]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[8]  D. Wolpert,et al.  Internal models in the cerebellum , 1998, Trends in Cognitive Sciences.

[9]  Daniel M. Wolpert,et al.  Forward Models for Physiological Motor Control , 1996, Neural Networks.

[10]  L. Miller,et al.  Primary motor cortical neurons encode functional muscle synergies , 2002, Experimental Brain Research.

[11]  J R Flanagan,et al.  The Role of Internal Models in Motion Planning and Control: Evidence from Grip Force Adjustments during Movements of Hand-Held Loads , 1997, The Journal of Neuroscience.

[12]  J. Hore,et al.  Finger opening in an overarm throw is not triggered by proprioceptive feedback from elbow extension or wrist flexion , 1999, Experimental Brain Research.

[13]  J.A. Walker,et al.  Structure, function, and neural control of pectoral fins in fishes , 2004, IEEE Journal of Oceanic Engineering.

[14]  G. Ermentrout,et al.  Modelling of intersegmental coordination in the lamprey central pattern generator for locomotion , 1992, Trends in Neurosciences.

[15]  Michael I. Jordan,et al.  The Role of Inertial Sensitivity in Motor Planning , 1998, The Journal of Neuroscience.

[16]  T. Brown On the nature of the fundamental activity of the nervous centres; together with an analysis of the conditioning of rhythmic activity in progression, and a theory of the evolution of function in the nervous system , 1914, The Journal of physiology.

[17]  Winfried Stefan Lohmiller,et al.  Contraction analysis of nonlinear systems , 1999 .

[18]  F A Mussa-Ivaldi,et al.  Computations underlying the execution of movement: a biological perspective. , 1991, Science.

[19]  Stefan Schaal,et al.  Robot Learning From Demonstration , 1997, ICML.

[20]  Y. Prigent [Long term depression]. , 1989, Annales medico-psychologiques.

[21]  A. Whiten Imitation of the sequential structure of actions by chimpanzees (Pan troglodytes). , 1998, Journal of comparative psychology.

[22]  Eve Marder,et al.  Modifiability of pattern generation , 1992, Current Biology.

[23]  R.R. Llinas,et al.  The olivo-cerebellar circuit as a universal motor control system , 2004, IEEE Journal of Oceanic Engineering.

[24]  Stefan Schaal,et al.  Is imitation learning the route to humanoid robots? , 1999, Trends in Cognitive Sciences.

[25]  J. V. José,et al.  Classical Dynamics: A Contemporary Approach , 1998 .

[26]  Warren M. Grill,et al.  Endpoint forces evoked by microstimulation of the cat spinal cord , 1999, Proceedings of the First Joint BMES/EMBS Conference. 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society (Cat. N.

[27]  Jorge V. José,et al.  Classical Dynamics: Contents , 1998 .

[28]  M Kawato,et al.  Internal models for motor control. , 2007, Novartis Foundation symposium.

[29]  K.M. Lynch,et al.  Mechanics and control of swimming: a review , 2004, IEEE Journal of Oceanic Engineering.

[30]  J. Donoghue,et al.  Learning-induced LTP in neocortex. , 2000, Science.

[31]  Jean-Paul Laumond,et al.  Robot Motion Planning and Control , 1998 .

[32]  Carson C. Chow,et al.  Synchronization and Oscillatory Dynamics in Heterogeneous, Mutually Inhibited Neurons , 1998, Journal of Computational Neuroscience.

[33]  T. Flash,et al.  The coordination of arm movements: an experimentally confirmed mathematical model , 1985, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[34]  Predrag Cvitanovic Chaotic field theory: a sketch , 2000 .

[35]  William H. Nedderman,et al.  Low-Speed Maneuvering Hydrodynamics of Fish and Small Underwater Vehicles , 1997 .

[36]  E. Marder,et al.  Principles of rhythmic motor pattern generation. , 1996, Physiological reviews.

[37]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

[38]  P. Matthews Relationship of firing intervals of human motor units to the trajectory of post‐spike after‐hyperpolarization and synaptic noise. , 1996, The Journal of physiology.

[39]  D. Munoz,et al.  t Immediate Neural Plasticity Shapes Motor Performance , 2000, The Journal of Neuroscience.

[40]  S. Grillner,et al.  The edge cell, a possible intraspinal mechanoreceptor. , 1984, Science.

[41]  R. Byrne,et al.  Priming primates: Human and otherwise , 1998, Behavioral and Brain Sciences.

[42]  E. Bizzi,et al.  Responses to spinal microstimulation in the chronically spinalized rat and their relationship to spinal systems activated by low threshold cutaneous stimulation , 1999, Experimental Brain Research.

[43]  S. Grillner Control of Locomotion in Bipeds, Tetrapods, and Fish , 1981 .

[44]  Neville Hogan,et al.  Impedance Control: An Approach to Manipulation , 1984, 1984 American Control Conference.

[45]  I. Prigogine Exploring Complexity , 2017 .

[46]  Ferdinando A. Mussa-Ivaldi,et al.  Nonlinear force fields: a distributed system of control primitives for representing and learning movements , 1997, Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation'.

[47]  D. Marr A theory of cerebellar cortex , 1969, The Journal of physiology.

[48]  Neville Hogan,et al.  The mechanics of multi-joint posture and movement control , 1985, Biological Cybernetics.

[49]  F. A. Mussa-lvaldi,et al.  Convergent force fields organized in the frog's spinal cord , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[50]  Michael I. Jordan,et al.  Optimal feedback control as a theory of motor coordination , 2002, Nature Neuroscience.

[51]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[52]  A. Hill The heat of shortening and the dynamic constants of muscle , 1938 .

[53]  Michael I. Jordan,et al.  Sensorimotor adaptation in speech production. , 1998, Science.

[54]  Daniel M Wolpert,et al.  Role of uncertainty in sensorimotor control. , 2002, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[55]  T. Nichols,et al.  The contributions of muscles and reflexes to the regulation of joint and limb mechanics. , 2002, Clinical orthopaedics and related research.

[56]  D. Long I of the Vortex: From Neurons to Self , 2002 .

[57]  E Bizzi,et al.  Motor learning through the combination of primitives. , 2000, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[58]  F A Mussa-Ivaldi,et al.  Central representation of time during motor learning. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[59]  E. Bizzi,et al.  Controlling multijoint motor behavior. , 1987, Exercise and sport sciences reviews.

[60]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[61]  F A Mussa-Ivaldi,et al.  Adaptive representation of dynamics during learning of a motor task , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[62]  J. W. Humberston Classical mechanics , 1980, Nature.

[63]  Bruno Siciliano,et al.  Modeling and Control of Robot Manipulators , 1995 .

[64]  Daniel M. Wolpert,et al.  Making smooth moves , 2022 .

[65]  N. Hogan,et al.  Impedance Control:An Approach to Manipulation,Parts I,II,III , 1985 .

[66]  J. Albus A Theory of Cerebellar Function , 1971 .

[67]  E. Bizzi,et al.  Consolidation in human motor memory , 1996, Nature.

[68]  Neville Hogan,et al.  Impedance Control: An Approach to Manipulation: Part I—Theory , 1985 .

[69]  E. Bizzi,et al.  Motor-space coding in the central nervous system. , 1990, Cold Spring Harbor symposia on quantitative biology.

[70]  E. Marder,et al.  Central pattern generators and the control of rhythmic movements , 2001, Current Biology.

[71]  J McIntyre,et al.  Reference frames and internal models for visuo-manual coordination: what can we learn from microgravity experiments? , 1998, Brain Research Reviews.

[72]  E. Bizzi,et al.  Linear combinations of primitives in vertebrate motor control. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[73]  Suzanne Daneau,et al.  Action , 2020, Remaking the Real Economy.

[74]  Reza Shadmehr,et al.  Computational nature of human adaptive control during learning of reaching movements in force fields , 1999, Biological Cybernetics.

[75]  Neville Hogan,et al.  Integrable Solutions of Kinematic Redundancy via Impedance Control , 1991, Int. J. Robotics Res..

[76]  Jean-Jacques E. Slotine,et al.  On Contraction Analysis for Non-linear Systems , 1998, Autom..

[77]  Karl F. Leeser,et al.  Exploitation of Kinematic Redundancy in Torque-Controllable Manipulators via Multiple-Jacobian Superposition , 1988 .

[78]  T. Bliss,et al.  Long‐lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path , 1973, The Journal of physiology.

[79]  R. Llinás I of the Vortex: From Neurons to Self , 2000 .

[80]  C. H. Jordan Awareness for action. , 1977, AORN journal.

[81]  T. Bliss,et al.  A synaptic model of memory: long-term potentiation in the hippocampus , 1993, Nature.

[82]  F. Mussa-Ivaldi,et al.  The motor system does not learn the dynamics of the arm by rote memorization of past experience. , 1997, Journal of neurophysiology.

[83]  T G Deliagina,et al.  Modifications of vestibular responses of individual reticulospinal neurons in lamprey caused by unilateral labyrinthectomy. , 2002, Journal of neurophysiology.

[84]  J. Feldman,et al.  PreBötzinger complex and pacemaker neurons: hypothesized site and kernel for respiratory rhythm generation. , 1998, Annual review of physiology.

[85]  David Tomko,et al.  The New York Academy of Sciences , 1881, Cellular and Molecular Neurobiology.

[86]  D M Wolpert,et al.  Multiple paired forward and inverse models for motor control , 1998, Neural Networks.

[87]  O. Kiehn,et al.  Functional role of plateau potentials in vertebrate motor neurons , 1998, Current Opinion in Neurobiology.

[88]  Jorge V. José,et al.  Classical Dynamics: List of Worked Examples , 1998 .

[89]  Michael I. Jordan,et al.  Forward Models: Supervised Learning with a Distal Teacher , 1992, Cogn. Sci..

[90]  H. C. Corben,et al.  Classical Mechanics (2nd ed.) , 1961 .

[91]  Xiao-Jing Wang,et al.  Alternating and Synchronous Rhythms in Reciprocally Inhibitory Model Neurons , 1992, Neural Computation.

[92]  R. Dubuc,et al.  Long-term potentiation of glutamatergic pathways in the lamprey brainstem , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[93]  A. Huxley Muscle structure and theories of contraction. , 1957, Progress in biophysics and biophysical chemistry.

[94]  Ferdinando A. Mussa-Ivaldi Geometrical principles in motor control , 1998 .

[95]  N. A. Bernshteĭn The co-ordination and regulation of movements , 1967 .

[96]  Daniel J. Amit,et al.  Modeling brain function: the world of attractor neural networks, 1st Edition , 1989 .

[97]  L. Jami Golgi tendon organs in mammalian skeletal muscle: functional properties and central actions. , 1992, Physiological reviews.

[98]  S Grillner,et al.  Neuronal mechanisms of synaptic and network plasticity in the lamprey spinal cord. , 2000, Progress in brain research.