Internal models and contextual cues: encoding serial order and direction of movement.

During reaching, the brain may rely on internal models to transform desired sensory outcomes into motor commands. This transformation depends on both the state of the limb and the cues that can identify the context of the movement. How are contextual cues and information about state of the limb combined in the computations of internal models? We considered a reaching task where forces on the hand depended on both the direction of movement (state of the limb) and order of that movement in a predefined sequence (contextual cue). When the cue was available, the motor system formed an internal model that used both serial order and target direction to program motor commands. Assuming that the internal model was formed by a population code through a combination of unknown basis elements, the sensitivity of the bases with respect to state of the limb and contextual cue should dictate how error in one type of movement affected all other movement types. Using a state-space theory, we estimated this generalization function and identified the adaptive system from trial-by-trial changes in performance. The results implied that the basis elements were tuned to direction of movement but output of each basis at its preferred direction was multiplicatively modulated by a weak tuning with respect to the contextual cue. Activity fields that multiplicatively encode diverse sources of information may serve as a general mechanism for a single network to produce context-dependent motor output.

[1]  H. Akaike A new look at the statistical model identification , 1974 .

[2]  A P Georgopoulos,et al.  On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex , 1982, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[3]  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.

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

[5]  S. Glantz Primer of applied regression and analysis of variance / Stanton A. Glantz, Bryan K. Slinker , 1990 .

[6]  S. Glantz,et al.  Primer of Applied Regression & Analysis of Variance , 1990 .

[7]  T Poggio,et al.  Fast perceptual learning in visual hyperacuity. , 1991, Science.

[8]  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.

[9]  R. Poppele,et al.  Representation of passive hindlimb postures in cat spinocerebellar activity. , 1996, Journal of neurophysiology.

[10]  T. Sejnowski,et al.  Spatial Transformations in the Parietal Cortex Using Basis Functions , 1997, Journal of Cognitive Neuroscience.

[11]  Zoubin Ghahramani,et al.  Modular decomposition in visuomotor learning , 1997, Nature.

[12]  Daniel M. Wolpert,et al.  Signal-dependent noise determines motor planning , 1998, Nature.

[13]  G. E. Alexander,et al.  Movement sequence-related activity reflecting numerical order of components in supplementary and presupplementary motor areas. , 1998, Journal of neurophysiology.

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

[15]  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.

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

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

[18]  A. Georgopoulos,et al.  Motor cortical encoding of serial order in a context-recall task. , 1999, Science.

[19]  R. Zemel,et al.  Information processing with population codes , 2000, Nature Reviews Neuroscience.

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

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

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

[23]  Reza Shadmehr,et al.  Learning the dynamics of reaching movements results in the modification of arm impedance and long-latency perturbation responses , 2001, Biological Cybernetics.

[24]  Rieko Osu,et al.  The central nervous system stabilizes unstable dynamics by learning optimal impedance , 2001, Nature.

[25]  F. A. Mussa-Ivaldi,et al.  Does the motor control system use multiple models and context switching to cope with a variable environment? , 2002, Experimental Brain Research.

[26]  David J Ostry,et al.  Transfer of Motor Learning across Arm Configurations , 2002, The Journal of Neuroscience.

[27]  Ferdinando A. Mussa-Ivaldi,et al.  Sequence, time, or state representation: how does the motor control system adapt to variable environments? , 2003, Biological Cybernetics.

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

[29]  A. Graybiel,et al.  Representation of Action Sequence Boundaries by Macaque Prefrontal Cortical Neurons , 2003, Science.

[30]  M. Kawato,et al.  Adaptation to Stable and Unstable Dynamics Achieved By Combined Impedance Control and Inverse Dynamics Model , 2003 .

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

[32]  R. Shadmehr,et al.  Supplementary Information: Quantifying Generalization from Trial-by-Trial behavior of Adaptive Systems that Learn with Basis Functions , 2003 .

[33]  J. Kalaska,et al.  Context-dependent anticipation of different task dynamics: rapid recall of appropriate motor skills using visual cues. , 2003, Journal of neurophysiology.

[34]  M. Kawato,et al.  Acquisition and contextual switching of multiple internal models for different viscous force fields , 2003, Neuroscience Research.

[35]  M. Kawato,et al.  Random presentation enables subjects to adapt to two opposing forces on the hand , 2004, Nature Neuroscience.

[36]  Emilio Salinas,et al.  Fast Remapping of Sensory Stimuli onto Motor Actions on the Basis of Contextual Modulation , 2004, The Journal of Neuroscience.

[37]  A. Georgopoulos,et al.  Static spatial effects in motor cortex and area 5: Quantitative relations in a two-dimensional space , 1984, Experimental Brain Research.