A neural model of voluntary movement and proprioception is developed that offers an integrated interpretation of the functional roles of diverse cell types in movement.related areas of primate cortex. The model circuit maintains accurate proprioception while controlling voluntary reaches to 5;patial targets, exertion of force against obstacles, posture maintenance despite perturbations, compliance with an imposed movement, and static and inertial load compensations. Computer simulations show that properties of model elements correspond to the proper1ties of many known cells types in areas 4 and 5. Among these prope,rties are delay period activation, response profiles during movEiment, kinematic and kinetic sensitivities, and latency of activity onset. In particular, area 4 phasic and tonic cells, respectively, compute velocity and position commands that are capable of activating alpha and gamma motor neurons, thereby shifting the mechanical equilibrium point. Anterior area 5 cells compute the position of the limb using corollary discharges from area 4 and feedback from muscle spindles. Posterior area 5 neurons use the position perception signal and a target position signal to compute a desired movement vector. The cortical loop is closed by a volition-gated projection of this movement vector to the area 4 phasic cells. An auxiliary circuit allows phasic-tonic cells in area 4 to incorporate force command components needed to compensate for static and inertial loads. After reporting simulations of prior experimental results, predictions are made for both motor and parietal cell types under novel experimental protocols.

Trajectory and kinematics of drawing movements are mutually constrained by functional relationships that reduce the degrees of freedom of the hand-arm system. Previous investigations of these relationships are extended here by considering their development in children between 5 and 12 years of age. Performances in a simple motor task—the continuous tracing of elliptic trajectories—demonstrate that both the phenomenon of isochrony (increase of the average movement velocity with the linear extent of the trajectory) and the so-called two-thirds power law (relation between tangential velocity and curvature) are qualitatively present already at the age of 5. The quantitative aspects of these regularities evolve with age, however, and steady-state adult performance is not attained even by the oldest children. The power-law formalism developed in previous reports is generalized to encompass these developmental aspects of the control of movement. Two general frameworks are currently available to conceptualize the motor-control problem. Broadly, the two frameworks differ in the answer that they give to the question "Where do form and structure come from? " According to the

Current models of psychological development rely heavily on connectionist models that use supervised learning. These models adapt network weights when the network output does not match the target outputs computed by some agent. The authors present a model of motor learning in which the child uses exploration to discover appropriate ways of responding. The model is consistent with what is known about how neural systems evaluate behavior. The authors model the development of reaching and investigate N. Bernstein’s (1967) hypotheses about early motor learning. Simulations show the course of learning as well as model the kinematics of reaching by a dynamical arm. Almost all developmental theories assume that a child’s interaction with the environment plays an important role in development. Often this interaction leads to long-term changes in behavior that can best be described as learning. Modern theories of learning characterize the process as exploratory, as involving the variation and selection of behavior or strategies, or as the discovery of

Humans exploit dynamics---gravity, inertia, joint coupling, elasticity, and so on---as a regular part of skillful, coordinated movements. Such movements comprise everyday activities, like reaching and walking, as well as highly practiced maneuvers as used in athletics and the performing arts. Robots, especially industrial manipulators, instead use control schemes that ordinarily cancel the complex, nonlinear dynamics that humans use to their advantage. Alternative schemes from the machine learning and intelligent control communities offer a number of potential benefits, such as improved efficiency, online skill acquisition, and tracking of nonstationary environments. However, the success of such methods depends a great deal on structure in the form of simplifying assumptions, prior knowledge, solution constraints and other heuristics that bias learning. My premise

A biologically inspired two-layered neural network for trajectory formation and obstacle avoidance is presented. The two topographically ordered neural maps consist of analog neurons having continuous dynamics. The first layer, the sensory map, receives sensory information and builds up an activity pattern which contains the optimal solutions (i.e. shortest path without collisions) for any given set of current position, target positions and obstacle positions. Targets and obstacles are allowed to move, in which case the activity pattern in the sensory map will change accordingly. The time-evolution of the neural activity in the second layer, the motor map, results in a moving cluster of activity, which can be interpreted as a population vector. Through the feedforward connections between the two layers, input of the sensory map directs the movement of the cluster along the optimal path from the current position of the cluster to the target position. The smooth trajectory is the result ...

We present a general method to intuitively create a wide range of locomotion controllers for 3D legged characters. The key of our approach is the assumption that efficient locomotion can exploit the natural vibration modes of the body, where these modes are related to morphological parameters such as the shape, size, mass, and joint stiffness. The vibration modes are computed for a mechanical model of any 3D character with rigid bones, elastic joints, and additional constraints as desired. A small number of vibration modes can be selected with respect to their relevance to locomotion patterns and combined into a compact controller driven by very few parameters. We show that these controllers can be used in dynamic simulations of simple creatures, and for kinematic animations of more complex creatures of a variety of shapes and sizes. Categories and Subject Descriptors (according to ACM CCS):

With little conscious effort, the Central Nervous System (CNS) is capable of coordinating visual information from the distal environment with the complex commands required to organize action. One account of this remarkable ability is that CNS hierarchically organizes movement control, with an effector independent planner superordinate to the variable details of movement execution. This thesis is concerned with the extent to which arm movements are preplanned in extrinsic, Cartesian space. Two fundamental questions are addressed: what aspects of movement are centrally planned, and by what criteria is the plan chosen? The first part of this thesis presents two sets of visual feedback perturbation experiments supporting the notion of Cartesian planning of movement trajectories. A novel prism adaptation technique is employed to investigate the relationship between the static visuomotor map and path planning. Reorganization of the mapping from extrinsic space to intrinsic coordinates is fou...

this paper is to determine whether such aimed movements in an insect are compensated against altered loading

Abstract: The analysis of the simplest biped walking machine can provide much insight into dynamics and control of human gait. The problem, in the sagittal plane, consists to control the impulse force under the swing foot and the torque between the legs in order to reach a stable limit cycle. The main contribution of this paper concerns the hierarchical control based on the physiological Lambda model and a pattern generator working with intermittent data. The feedback is tuned in order to obtain oscillations at the walking frequency. Simulation results present stable limit cycle. Copyright © 2002 IFAC

Perception of relative phase and phase variability may play a fundamental role in interlimb coordination. This study was designed to investigate the perception of relative phase and of phase variability and the stability of perception in each case. Observers judged the relative phasing of two circles rhythmically moving on a computer display. The circles moved from side to side, simulating movement in the frontoparallel plane, or increased and decreased in size, simulating movement in depth. Under each viewing condition, participants observed the same displays but were to judge either mean relative phase or phase variability. Phase variability interfered with the mean-relative-phase judgments, in particular when the mean relative phase was 0°. Judgments of phase variability varied as a function of mean relative phase. Furthermore, the stability of the judgments followed an asymmetric inverted U-shaped relation with mean relative phase, as predicted by the Haken-Kelso-Bunz model. In the early eighties, Kugler, Turvey, and Kelso, among others, introduced the concepts of nonlinear dynamics into the study of human movement, thereby building on Bernstein's (1967) important insight that perception-action systems should be regarded as coordinative structures that are task specific and soft molded