A Computational Model of Adaptation to Novel Stable and Unstable Dynamics David W. Franklin1,2, Rieko Osu1, Etienne Burdet3, Mitsuo Kawato1 and Theodore E. Milner2 1 ATR- HIS Laboratories, Kyoto, Japan 2 School of Kinesiology, Simon Fraser University, Burnaby, Canada 3 Dept. of Mech Eng and Division of Bioeng, National University of Singapore

Introduction Humans have exceptional abilities to produce movements and interact with objects in the environment. When faced with novel tasks, they adapt to environmental disturbances in a way that indicates they are forming an internal representation of the external mechanics or an internal model. Adaptation under mechanically stable conditions appears to involve the acquisition of an inverse dynamics model through feedback error learning 4. However, many tasks that humans perform, particularly those involving tool use, are inherently unstable and the mechanism of adaptation to these environments is not well understood. One form of adaptation to instability, which we have been investigating, is the selective control of endpoint stiffness 1. Our recent research suggests that inverse dynamics models and impedance control are combined during motor learning. An inverse dynamics model is a controller which computes feedforward commands of the net joint torques for movement, based on the estimated effects of internal and external dynamics. An impedance controller, in contrast, modifies the impedance of the limb by co-contraction of agonist and antagonist muscles without changing net joint torque. Recent experiments have provided evidence that inverse dynamics models and impedance control may function independently 6;7. In our most recent work we have investigated the adaptation to various stable and unstable environments which has led to a proposed computational mechanism whereby such adaptation can be realized by the central nervous system (CNS).

Methods Subjects sat in a chair and moved a 2D robotic manipulator (PFM) in a series of forward reaching movements performed in the horizontal plane. Trajectory, joint torque and EMG changes were investigated during and following adaptation to a stable velocity dependent (VF) and an unstable position dependent (DF) force field and were compared to performance in a null force field (NF). Subjects initially performed movements in the NF. After 50 to 70 trials, one of the novel force fields was unexpectedly substituted for the NF. Subjects proceeded to adapt to this force field over the subsequent 100 to 200 trials. Both after effect and before effect trials were recorded to check that subjects had adapted to the force field and to aid in interpreting adaptive changes in EMG.

Results Initially subjects’ trajectories were disturbed by the force fields, however after training they tended to produce straight movements to the target. Two parallel processes were identified from the evolution of the muscle activation patterns. One was an activation process involved in increasing the endpoint stiffness of the arm by means of muscle co-contraction during the early stages of learning. The other was a deactivation process, which led to gradual reduction in muscle activity as learning progressed. Adaptation to the VF was characterized by fast increases in muscle activity contributing to the inverse dynamics model and a slower increase in generalized co-contraction. Kinematic error was reduced with a time course similar to the increase in co-contraction but adaptation of the joint torque (formation of inverse dynamics model) took longer. Later, unnecessary co-contraction was reduced. The change between the initial and final patterns of muscle activation was similar to the reflex responses observed during

before effects, suggesting that the reflex patterns provided a template for the feedforward command. Learning in the DF was characterized by initial slow increase in activation of all muscles (co-contraction) which occurred at a similar rate to the decrease in kinematic error. After adaptation to the force field, unneeded cocontraction was gradually reduced. Kinematic data for the first few trials in the DF suggest that the CNS attempts to learn the DF using an inverse dynamics model. Therefore, it appears that the CNS utilizes both an inverse dynamics model and impedance control in adapting to any novel environment.

Discussion of Computational Model

Osu et al. 5 showed that the classical feedback-errorlearning algorithm 3 applied to joint torque can explain inverse dynamics model learning in the VF, but cannot explain impedance learning in the DF. The following natural extension of feedback error learning could coherently unify the two learning processes, and at least qualitatively reproduce the current results, as well as other recent data on motor learning 1;2;6. First, centrally generated feedforward motor commands would comprise both a reciprocal component for agonist and antagonist muscle pairs (difference in muscle activation similar to net joint torque) and a co-activation component (summation of agonist and antagonist muscle activation similar to joint stiffness). Since these two components sum at the muscle level it is not necessary that they be separately represented in the brain. Second, the feedforward co-activation signal to antagonists should increase on trials following perturbation of the hand path during early learning even when only agonist muscles are stretched. Third, the feedforward co-activation signal decays with a large time constant as manifested by the deactivation time constants of all muscles. Synaptic plasticity equations can be constructed by extending the feedback-errorlearning algorithm based on the proposed theory. Such a model could explain both the development of net joint torque to compensate for external forces and the selective adaptation of endpoint impedance to environmental instability. Computer simulations of this computational model qualitatively reproduce the experimental results and tentatively confirm this conceptual model.

Acknowledgements This research was supported by the Telecommunications Advancement Organization of Japan; the Natural Sciences and Engineering Research Council of Canada; and the Human Frontier Science Program.

References 1. 2.

3. 4. 5. 6. 7.

E. Burdet, R. Osu, D. W. Franklin, T. E. Milner, M. Kawato, Nature 414, 446-9 (2001). D. W. Franklin, E. Burdet, R. Osu, M. Kawato, T. E. Milner, Functional significance of stiffness in adaptation of multijoint arm movements to stable and unstable environments (submitted). M. Kawato, Curr Opin Neurobiol 9, 718-27 (1999). M. Kawato, K. Furukawa, R. Suzuki, Biol Cybern 57, 169-85 (1987). Osu, R., Burdet, E., Franklin, D. W., Milner, T. E., and Kawato, M. Internal model learning is not used when adapting to unstable dynamics. (submitted). R. Osu et al., J Neurophysiol 88, 991-1004 (2002). C. D. Takahashi, R. A. Scheidt, D. J. Reinkensmeyer, J Neurophysiol 86, 1047-51 (2001).

A Computational Model of Adaptation to Novel Stable ...

effect and before effect trials were recorded to check that subjects had adapted to ... signal decays with a large time constant as manifested by the deactivation ...

63KB Sizes 1 Downloads 228 Views

Recommend Documents

A Computational Model of Adaptation to Novel Stable ...
effect and before effect trials were recorded to check that subjects ... Japan; the Natural Sciences and Engineering Research Council of Canada; and the.

Computational chemistry comparison of stable ...
Apr 20, 2007 - Some of most used connectivity indices include first (M1) and second (M2) Zagreb ... First, the data is split at random in two parts, a training series. (t) used for model ..... CambridgeSoft Corporation. Chem3D Ultra software.

A DISCRIMINATIVE DOMAIN ADAPTATION MODEL ...
domain data, our proposed model aims at transferring source domain labeled .... Suppose that there exist n instances in a d-dimensional space, we have the ...

A Computational Model of Muscle Recruitment for Wrist ...
tor system must resolve prior to making a movement. Hoffman and Strick ... poster, we present an abstract model of wrist muscle recruitment that selects muscles ...

756 A Computational Model of Infant Speech ...
computer model which has no requirement for acoustic matching on the part of .... enables him to associate an adult speech sound to his gestural formulation. .... because the optimization then had a larger number of degrees of freedom from .... ”Tw

756 A Computational Model of Infant Speech ...
targets, by interpolating between them using critically damped trajectories, as adopted my ..... Westermann, G. and Miranda, E. (2004) A new model of sensorimotor ... In: Schaffer, H. R. (ed) Studies in Mother- ... Connection Science 14 (4), 245-.

Designing a Computational Model of Learning
how intelligence can be represented in software agents. .... A good candidate for a complementary model is ...... in tracking, analyzing, and reporting on. They.

A computational model of reach decisions in the ...
Paul Cisek. Department of physiology, University of Montreal ... Reference List. Cisek, P. (2002) “Think ... Neuroscience. Orlando, FL, November 2nd, 2002.

Designing a Computational Model of Learning
and how students develop new knowledge through modeling and ... or simulation” is a computer code or application that embodies .... development of intelligence (Pfeifer & Bongard,. 2007). ...... New York: Teacher College Press. Cosmides, L.

A computational model of risk, conflict, and ... - Semantic Scholar
Available online 26 July 2007. The error likelihood effect ..... results of our follow-up study which revealed a high degree of individual ..... and Braver, 2005) as a value that best simulated the timecourse of .... Adaptive coding of reward value b

A Computational Model of Muscle Recruitment for Wrist
Oct 10, 2002 - Thus for a given wrist configuration () and muscle activation vector (a), the endpoint of movement (x, a two element vector) can be described as ...

LANGUAGE MODEL ADAPTATION USING RANDOM ...
Broadcast News LM to MIT computer science lecture data. There is a ... If wi is the word we want to predict, then the general question takes the following form:.

Read PDF Kindred: A Graphic Novel Adaptation
We provide excellent essay writing service 24 7 Enjoy proficient essay writing and custom writing services provided by professional ... Instant #1 New York Times.

A biomimetic, force-field based computational model ... - Springer Link
Aug 11, 2009 - a further development of what was proposed by Tsuji et al. (1995) and Morasso et al. (1997). .... level software development by facilitating modularity, sup- port for simultaneous ...... Adaptive representation of dynamics during ...

A trajectory-based computational model for optical flow ...
and Dubois utilized the concepts of data conservation and spatial smoothness in ...... D. Marr, Vision. I. L. Barron, D. J. Fleet, S. S. Beauchemin, and T. A. Burkitt,.

Development of Novel In Silico Model to Predict Corneal Permeability ...
Oct 26, 2010 - drug companies. 2.2. Preparation of .... software in training sets of 32 diverse noncongeneric com- pounds including ... 6.1.12.33, Japan), ACD/Chemsketch (Freeware version 10), ..... database of structures that avoids the need to calc