Trial-by-trial motor adaptation: a window into elemental neural computation.

How does the brain compute? To address this question, mathematical modelers, neurophysiologists, and psychophysicists have sought behaviors that provide evidence of specific neural computations. Human motor behavior consists of several such computations [Shadmehr, R., Wise, S.P. (2005). MIT Press: Cambridge, MA], such as the transformation of a sensory input to a motor output. The motor system is also capable of learning new transformations to produce novel outputs; humans have the remarkable ability to alter their motor output to adapt to changes in their own bodies and the environment [Wolpert, D.M., Ghahramani, Z. (2000). Nat. Neurosci., 3: 1212-1217]. These changes can be long term, through growth and changing body proportions, or short term, through changes in the external environment. Here we focus on trial-by-trial adaptation, the transformation of individually sensed movements into incremental updates of adaptive control. These investigations have the promise of revealing important basic principles of motor control and ultimately guiding a new understanding of the neuronal correlates of motor behaviors.

[1]  Daniel M Wolpert,et al.  Kinematics and Dynamics Are Not Represented Independently in Motor Working Memory: Evidence from an Interference Study , 2002, The Journal of Neuroscience.

[2]  Kurt A. Thoroughman,et al.  Trial-by-trial transformation of error into sensorimotor adaptation changes with environmental dynamics. , 2007, Journal of neurophysiology.

[3]  Reza Shadmehr,et al.  Learned dynamics of reaching movements generalize from dominant to nondominant arm. , 2003, Journal of neurophysiology.

[4]  M. Kawato,et al.  Temporal firing patterns of Purkinje cells in the cerebellar ventral paraflocculus during ocular following responses in monkeys II. Complex spikes. , 1998, Journal of neurophysiology.

[5]  Daniel W Moran,et al.  Computational model of a primate arm: from hand position to joint angles, joint torques and muscle forces , 2006, Journal of neural engineering.

[6]  D. Wolpert,et al.  Temporal and amplitude generalization in motor learning. , 1998, Journal of neurophysiology.

[7]  Tatsuya Kimura,et al.  Cerebellar complex spikes encode both destinations and errors in arm movements , 1998, Nature.

[8]  R A Scheidt,et al.  Learning to move amid uncertainty. , 2001, Journal of neurophysiology.

[9]  M. Gazzaniga.  The cognitive neurosciences, 3rd edition , 2004 .

[10]  A. P. Georgopoulos,et al.  Neuronal population coding of movement direction. , 1986, Science.

[11]  C. Atkeson,et al.  Learning arm kinematics and dynamics. , 1989, Annual review of neuroscience.

[12]  E. Bizzi,et al.  The Cognitive Neurosciences , 1996 .

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

[14]  T. Poggio.  A theory of how the brain might work. , 1990, Cold Spring Harbor symposia on quantitative biology.

[15]  Tamar Flash,et al.  Computational approaches to motor control , 2001, Current Opinion in Neurobiology.

[16]  E. Bizzi,et al.  Neuronal Correlates of Motor Performance and Motor Learning in the Primary Motor Cortex of Monkeys Adapting to an External Force Field , 2001, Neuron.

[17]  Reza Shadmehr,et al.  Learning of action through adaptive combination of motor primitives , 2000, Nature.

[18]  J. Lackner,et al.  Rapid adaptation to Coriolis force perturbations of arm trajectory. , 1994, Journal of neurophysiology.

[19]  J. Kalaska,et al.  Motor cortex neural correlates of output kinematics and kinetics during isometric-force and arm-reaching tasks. , 2005, Journal of neurophysiology.

[20]  T. Brashers-Krug,et al.  Functional Stages in the Formation of Human Long-Term Motor Memory , 1997, The Journal of Neuroscience.

[21]  W. T. Thach.  Correlation of neural discharge with pattern and force of muscular activity, joint position, and direction of intended next movement in motor cortex and cerebellum. , 1978, Journal of neurophysiology.

[22]  M. Kawato,et al.  A hierarchical neural-network model for control and learning of voluntary movement , 2004, Biological Cybernetics.

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

[24]  Kurt A. Thoroughman,et al.  Motor adaptation to single force pulses: sensitive to direction but insensitive to within-movement pulse placement and magnitude. , 2006, Journal of neurophysiology.

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

[26]  R Shadmehr,et al.  Spatial Generalization from Learning Dynamics of Reaching Movements , 2000, The Journal of Neuroscience.

[27]  D. Wolpert,et al.  Failure to Consolidate the Consolidation Theory of Learning for Sensorimotor Adaptation Tasks , 2004, The Journal of Neuroscience.

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

[29]  E Bizzi,et al.  Motor learning by field approximation. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[30]  M. Kawato,et al.  The cerebellum and VOR/OKR learning models , 1992, Trends in Neurosciences.

[31]  Kurt A. Thoroughman,et al.  Rapid Reshaping of Human Motor Generalization , 2005, The Journal of Neuroscience.

[32]  K. Kawano,et al.  Role of Purkinje cells in the ventral paraflocculus in short-latency ocular following responses , 2004, Experimental Brain Research.

[33]  Reza Shadmehr,et al.  Quantifying Generalization from Trial-by-Trial Behavior of Adaptive Systems that Learn with Basis Functions: Theory and Experiments in Human Motor Control , 2003, The Journal of Neuroscience.

[34]  R. Shadmehr,et al.  A Gain-Field Encoding of Limb Position and Velocity in the Internal Model of Arm Dynamics , 2003, PLoS biology.

[35]  A B Schwartz,et al.  Motor cortical representation of speed and direction during reaching. , 1999, Journal of neurophysiology.

[36]  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.

[37]  Zoubin Ghahramani,et al.  Computational motor control , 2004 .

[38]  M. Kawato,et al.  Temporal firing patterns of Purkinje cells in the cerebellar ventral paraflocculus during ocular following responses in monkeys I. Simple spikes. , 1998, Journal of neurophysiology.

[39]  Alexandre Pouget,et al.  Computational approaches to sensorimotor transformations , 2000, Nature Neuroscience.

[40]  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.

[41]  W. T. Thach,et al.  Purkinje cell activity during motor learning , 1977, Brain Research.

[42]  T. Ebner,et al.  Cerebellar Purkinje Cell Simple Spike Discharge Encodes Movement Velocity in Primates during Visuomotor Arm Tracking , 1999, The Journal of Neuroscience.

[43]  S. Scott,et al.  Reaching movements with similar hand paths but different arm orientations. I. Activity of individual cells in motor cortex. , 1997, Journal of neurophysiology.