Motor learning through the combination of primitives. Mussa-Ivaldi, F. & Bizzi, E Philos Trans R Soc Lond B Biol Sci, 355(1404):1755-69, 2000.
abstract   bibtex   
In this paper we discuss a new perspective on how the central nervous system (CNS) represents and solves some of the most fundamental computational problems of motor control. In particular, we consider the task of transforming a planned limb movement into an adequate set of motor commands. To carry out this task the CNS must solve a complex inverse dynamic problem. This problem involves the transformation from a desired motion to the forces that are needed to drive the limb. The inverse dynamic problem is a hard computational challenge because of the need to coordinate multiple limb segments and because of the continuous changes in the mechanical properties of the limbs and of the environment with which they come in contact. A number of studies of motor learning have provided support for the idea that the CNS creates, updates and exploits internal representations of limb dynamics in order to deal with the complexity of inverse dynamics. Here we discuss how such internal representations are likely to be built by combining the modular primitives in the spinal cord as well as other building blocks found in higher brain structures. Experimental studies on spinalized frogs and rats have led to the conclusion that the premotor circuits within the spinal cord are organized into a set of discrete modules. Each module, when activated, induces a specific force field and the simultaneous activation of multiple modules leads to the vectorial combination of the corresponding fields. We regard these force fields as computational primitives that are used by the CNS for generating a rich grammar of motor behaviours.
@Article{Mussa-Ivaldi2000,
  author   = {FA Mussa-Ivaldi and E Bizzi},
  journal  = {Philos Trans R Soc Lond B Biol Sci},
  title    = {Motor learning through the combination of primitives.},
  year     = {2000},
  number   = {1404},
  pages    = {1755-69},
  volume   = {355},
  abstract = {In this paper we discuss a new perspective on how the central nervous
	system (CNS) represents and solves some of the most fundamental computational
	problems of motor control. In particular, we consider the task of
	transforming a planned limb movement into an adequate set of motor
	commands. To carry out this task the CNS must solve a complex inverse
	dynamic problem. This problem involves the transformation from a
	desired motion to the forces that are needed to drive the limb. The
	inverse dynamic problem is a hard computational challenge because
	of the need to coordinate multiple limb segments and because of the
	continuous changes in the mechanical properties of the limbs and
	of the environment with which they come in contact. A number of studies
	of motor learning have provided support for the idea that the CNS
	creates, updates and exploits internal representations of limb dynamics
	in order to deal with the complexity of inverse dynamics. Here we
	discuss how such internal representations are likely to be built
	by combining the modular primitives in the spinal cord as well as
	other building blocks found in higher brain structures. Experimental
	studies on spinalized frogs and rats have led to the conclusion that
	the premotor circuits within the spinal cord are organized into a
	set of discrete modules. Each module, when activated, induces a specific
	force field and the simultaneous activation of multiple modules leads
	to the vectorial combination of the corresponding fields. We regard
	these force fields as computational primitives that are used by the
	CNS for generating a rich grammar of motor behaviours.},
  keywords = {Computing Methodologies, Human, Language, Learning, Mental Processes, Models, Theoretical, Stochastic Processes, Support, U.S. Gov't, Non-P.H.S., Cognition, Linguistics, Neural Networks (Computer), Practice (Psychology), Non-U.S. Gov't, Memory, Psychological, Task Performance and Analysis, Time Factors, Visual Perception, Adult, Attention, Discrimination Learning, Female, Male, Short-Term, Mental Recall, Orientation, Pattern Recognition, Visual, Perceptual Masking, Reading, Concept Formation, Form Perception, Animals, Corpus Striatum, Shrews, P.H.S., Visual Cortex, Visual Pathways, Acoustic Stimulation, Auditory Cortex, Auditory Perception, Cochlea, Ear, Gerbillinae, Glycine, Hearing, Neurons, Space Perception, Strychnine, Adolescent, Decision Making, Reaction Time, Astrocytoma, Brain Mapping, Brain Neoplasms, Cerebral Cortex, Electric Stimulation, Electrophysiology, Epilepsy, Temporal Lobe, Evoked Potentials, Frontal Lobe, Noise, Parietal Lobe, Scalp, Child, Language Development, Psycholinguistics, Brain, Perception, Speech, Vocalization, Animal, Discrimination (Psychology), Hippocampus, Rats, Calcium, Chelating Agents, Excitatory Postsynaptic Potentials, Glutamic Acid, Guanosine Diphosphate, In Vitro, Neuronal Plasticity, Pyramidal Cells, Receptors, AMPA, Metabotropic Glutamate, N-Methyl-D-Aspartate, Somatosensory Cortex, Synapses, Synaptic Transmission, Thionucleotides, Action Potentials, Calcium Channels, L-Type, Electric Conductivity, Entorhinal Cortex, Neurological, Long-Evans, Infant, Mathematics, Statistics, Probability Learning, Problem Solving, Psychophysics, Association Learning, Child Psychology, Habituation (Psychophysiology), Probability Theory, Analysis of Variance, Semantics, Symbolism, Behavior, Eye Movements, Macaca mulatta, Prefrontal Cortex, Cats, Dogs, Haplorhini, Photic Stimulation, Electroencephalography, Nervous System Physiology, Darkness, Grasshoppers, Light, Membrane Potentials, Neural Inhibition, Afferent, Picrotoxin, Vision, Deoxyglucose, Injections, Microspheres, Neural Pathways, Rhodamines, Choice Behavior, Speech Perception, Verbal Learning, Dominance, Cerebral, Fixation, Ocular, Language Tests, Random Allocation, Comparative Study, Saguinus, Sound Spectrography, Species Specificity, Audiometry, Auditory Threshold, Calibration, Data Interpretation, Statistical, Anesthesia, General, Electrodes, Implanted, Pitch Perception, Sound Localization, Paired-Associate Learning, Serial Learning, Auditory, Age Factors, Motion Perception, Brain Injuries, Computer Simulation, Blindness, Psychomotor Performance, Color Perception, Signal Detection (Psychology), Judgment, ROC Curve, Regression Analysis, Music, Probability, Arm, Cerebrovascular Disorders, Hemiplegia, Movement, Muscle, Skeletal, Myoclonus, Robotics, Magnetoencephalography, Phonetics, Software, Speech Production Measurement, Epilepsies, Partial, Laterality, Stereotaxic Techniques, Germany, Speech Acoustics, Verbal Behavior, Child Development, Instinct, Brain Stem, Coma, Diagnosis, Differential, Hearing Disorders, Hearing Loss, Central, Neuroma, Acoustic, Dendrites, Down-Regulation, Patch-Clamp Techniques, Wistar, Up-Regulation, Aged, Aphasia, Middle Aged, Cones (Retina), Primates, Retina, Retinal Ganglion Cells, Tympanic Membrane, Cell Communication, Extremities, Biological, Motor Activity, Rana catesbeiana, Spinal Cord, Central Nervous System, Motion, Motor Cortex, 11205339},
}
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