Biomedical subjects

F A Mussa-Ivaldi

Publications and source records attributed to F A Mussa-Ivaldi.

At least 19 recordsLinked to original sources

PubMed2002

Does the motor control system use multiple models and context switching to cope with a variable environment?

Studies of arm movements have shown that subjects learn to compensate predictable mechanical perturbations by developing a representation of the relation between the state of motion of the arm and the perturbing forces. Here, we tested the hypothesis that subjects construct internal representations of two different force fields and switch between them when presented with an alternating sequence of these fields. Our results do not support this hypothesis. Subjects performed reaching movements in four sessions over 4 days. On the 1st day the robotic manipulandum perturbed the movement by perpendicular force that alternated its direction after each movement. Subjects were unable to construct the two underlying models and switch between them. On the 2nd day only one field was applied and well learned. On the 3rd day only the other field was applied and well learned. Then the experiment of the 1st day was repeated on the 4th day. Even after this extensive training subjects showed no signs of improved performance with alternating fields. This result combined with previous studies suggests that the central nervous system has a strong tendency to employ a single internal model when dealing with a sequence of perturbations.

Adaptation, Psychological↗

PubMed2001

Learning to move amid uncertainty.

We studied how subjects learned to make movements against unpredictable perturbations. Twelve healthy human subjects made goal-directed reaching movements in the horizontal plane while holding the handle of a two-joint robotic manipulator. The robot generated viscous force fields that perturbed the limb perpendicular to the desired direction of movement. The amplitude (but not the direction) of the viscous field varied randomly from trial to trial. Systems identification techniques were employed to characterize how subjects adapted to these random perturbations. Subject performance was quantified primarily using the peak deviation from a straight-line hand path. Subjects adapted their arm movements to the sequence of random force-field amplitudes. This adaptive response compensated for the approximate mean from the random sequence of perturbations and did not depend on the statistical distribution of that sequence. Subjects did not adapt by directly counteracting the mean field strength itself on each trial but rather by using information about perturbations and movement errors from a limited number of previous trials to adjust motor commands on subsequent trials. This strategy permitted subjects to achieve near-optimal performance (defined as minimizing movement errors in a least-squares sense) while maintaining computational efficiency. A simple model using information about movement errors and perturbation amplitudes from a single previous trial predicted subject performance in stochastic environments with a high degree of fidelity and further predicted key performance features observed in nonstochastic environments. This suggests that the neural structures modified during motor adaptation require only short-term memory. Explicit representations regarding movements made more than a few trials in the past are not used in generating optimal motor responses on any given trial.

Adaptation, Physiological↗

PubMed2000

Motor learning through the combination of primitives.

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.

Animals↗

PubMed2000

Haptic interaction with virtual objects. Spatial perception and motor control.

This paper considers interaction of the human arm with "virtual" objects simulated mechanically by a planar robot. Haptic perception of spatial properties of objects is distorted. It is reasonable to expect that it may be distorted in a geometrically consistent way. Three experiments were performed to quantify perceptual distortion of length, angle and orientation. We found that spatial perception is geometrically inconsistent across these perceptual tasks. Given that spatial perception is distorted, it is plausible that motor behavior may be distorted in a way consistent with perceptual distortion. In a fourth experiment, subjects were asked to draw circles. The results were geometrically inconsistent with those of the length perception experiment. Interestingly, although the results were inconsistent (statistically different), this difference was not strong (the relative distortion between the observed distributions was small). Some computational implications of this research for haptic perception and motor planning are discussed.

Distance Perception↗

PubMed2000

Persistence of motor adaptation during constrained, multi-joint, arm movements.

We studied the stability of changes in motor performance associated with adaptation to a novel dynamic environment during goal-directed movements of the dominant arm. Eleven normal, human subjects made targeted reaching movements in the horizontal plane while holding the handle of a two-joint robotic manipulator. This robot was programmed to generate a novel viscous force field that perturbed the limb perpendicular to the desired direction of movement. Following adaptation to this force field, we sought to determine the relative role of kinematic errors and dynamic criteria in promoting recovery from the adapted state. In particular, we compared kinematic and dynamic measures of performance when kinematic errors were allowed to occur after removal of the viscous fields, or prevented by imposing a simulated, mechanical "channel" on movements. Hand forces recorded at the handle revealed that when kinematic errors were prevented from occurring by the application of the channel, recovery from adaptation to the novel field was much slower compared with when kinematic aftereffects were allowed to take place. In particular, when kinematic errors were prevented, subjects persisted in generating large forces that were unnecessary to generate an accurate reach. The magnitude of these forces decreased slowly over time, at a much slower rate than when subjects were allowed to make kinematic errors. This finding provides strong experimental evidence that both kinematic and dynamic criteria influence motor adaptation, and that kinematic-dependent factors play a dominant role in the rapid loss of adaptation after restoring the original dynamics.

Adaptation, Physiological↗

PubMed2000

Output units of motor behavior: an experimental and modeling study.

Cognitive approaches to motor control typically concern sequences of discrete actions without taking into account the stunning complexity of the geometry and dynamics of the muscles. This begs the question: Does the brain convert the intricate, continuous-time dynamics of the muscles into simpler discrete units of actions, and if so, how? One way for the brain to form discrete units of behavior from muscles is through the synergistic co-activation of muscles. While this possibility has long been known, the composition of potential muscle synergies has remained elusive. In this paper, we have focused on a method that allowed us to examine and compare the limb stabilization properties of all possible muscle combinations. We found that a small set (as few as 23 out of 65,536) of all possible combinations of 16 limb muscles are robust with respect to activation noise: these muscle combinations could stabilize the limb at predictable, restricted portions of the workspace in spite of broad variations in the force output of their component muscles. The locations at which the robust synergies stabilize the limb are not uniformly distributed throughout the leg's workspace, but rather, they cluster at four workspace areas. The simulated robust synergies are similar to the actual synergies we have previously found to be generated by activation of the spinal cord. Thus, we have developed a new analytical method that enabled us to select a few muscle synergies with interesting properties out of the set of possible muscle combinations. Beyond this, the identification of robustness as a common property of the synergies in simple motor behaviors will open the way to the study of dynamic stability, which is an important and distinct property of the vertebrate motor-control system.

Animals↗

PubMed2000

Connecting brains to robots: an artificial body for studying the computational properties of neural tissues.

We have created a hybrid neuro-robotic system that establishes two-way communication between the brain of a lamprey and a small mobile robot. The purpose of this system is to offer a new paradigm for investigating the behavioral, computational, and neurobiological mechanisms of sensory-motor learning in a unified context. The mobile robot acts as an artificial body that delivers sensory information to the neural tissue and receives command signals from it. The sensory information encodes the intensity of light generated by a fixed source. The closed-loop interaction between brain and robot generates autonomous behaviors whose features are strictly related to the structure and operation of the neural preparation. We provide a detailed description of the hybrid system, and we present experimental findings on its performance. In particular, we found (a) that the hybrid system generates stable behaviors, (b) that different preparations display different but systematic responses to the presentation of an optical stimulus, and (c) that alteration of the sensory input leads to short- and long-term adaptive changes in the robot responses. The comparison of the behaviors generated by the lamprey's brain stem with the behaviors generated by network models of the same neural system provides us with a new tool for investigating the computational properties of synaptic plasticity.

Animals↗

PubMed1999

Central representation of time during motor learning.

This study stemmed from the observation that the brain of human as well as nonhuman primates is capable of forming and memorizing remarkably accurate internal representations of the dynamics of the arm. These dynamics establish a functional relation between applied force and ensuing arm motion, a relation that generally is quite complex and nonlinear. Current evidence shows that the motor control system is capable of adapting to perturbing forces that depend on motion variables such as position, velocity, and acceleration. The experiments we report here were aimed at establishing whether or not the motor system also may adapt to forces that depend explicitly on time rather than on motion variables. Surprisingly, the experiments suggest a negative answer. When asked to compensate for a predictable and repeated time-varying pattern of disturbing forces, subjects learned to counteract the disturbance by producing forces that did not depend on time but on the velocity and the position of the arm. We conclude from this evidence that time and time-dependent dynamics are not explicitly represented within the neural structures that are responsible for motor adaptation. Although our findings are not sufficient to rule out the presence of a timing structure within the central nervous system, they are consistent with other investigations that conspicuously failed to find evidence for such a central clock.

Adaptation, Physiological↗

PubMed1999

Emergence of symmetric, modular, and reciprocal connections in recurrent networks with Hebbian learning.

While learning and development are well characterized in feedforward networks, these features are more difficult to analyze in recurrent networks due to the increased complexity of dual dynamics - the rapid dynamics arising from activation states and the slow dynamics arising from learning or developmental plasticity. We present analytical and numerical results that consider dual dynamics in a recurrent network undergoing Hebbian learning with either constant weight decay or weight normalization. Starting from initially random connections, the recurrent network develops symmetric or near-symmetric connections through Hebbian learning. Reciprocity and modularity arise naturally through correlations in the activation states. Additionally, weight normalization may be better than constant weight decay for the development of multiple attractor states that allow a diverse representation of the inputs. These results suggest a natural mechanism by which synaptic plasticity in recurrent networks such as cortical and brainstem premotor circuits could enhance neural computation and the generation of motor programs.

Animals↗

PubMed1999

Modular features of motor control and learning.

The study of complex motor behaviours has highlighted the role of modular representations both in the planning and in the execution of actions. Recent findings suggest the presence of functional modules within a variety of neural structures. Computational investigations are now addressing the issue of how these modules may act concurrently to generate a wide repertoire of behaviours.

Animals↗

PubMed1997

The motor system does not learn the dynamics of the arm by rote memorization of past experience.

The purpose of this study was to investigate the learning mechanisms underlying motor adaptation of arm movements to externally applied perturbing forces. We considered two alternative hypotheses. According to one, adaptation occurs through the learning of a mapping between the states (positions and velocities) visited by the arm and the forces experienced at those states. The alternative hypothesis is that adaptation occurs through the memorization of the temporal sequence of forces experienced along specific trajectories. The first mechanism corresponds to developing a model of the dynamics of the environment, whereas the second is a form of "rote learning." Both types of learning would lead to the recovery of the unperturbed performance. We have tested these hypotheses by examining how adaptation is transferred across different types of movements. Our results indicate that 1) adaptation to an externally applied force field occurs with different classes of movements including but not limited to reaching movements and 2) adaptation generalizes across different movements that visit the same regions of the external field. These findings are not compatible with the hypothesis of rote learning. Instead, they are consistent with the hypothesis that adaptation to changes in movement dynamics is achieved by a module that learns to reproduce the structure of the environmental field as an association between visited states and experienced forces, independent of the kinematics of the movements made during adaptation.

Adaptation, Psychological↗

PubMed1996

Motor learning by field approximation.

We investigated how human subjects adapt to forces perturbing the motion of their ams. We found that this kind of learning is based on the capacity of the central nervous system (CNS) to predict and therefore to cancel externally applied perturbing forces. Our experimental results indicate: (i) that the ability of the CNS to compensate for the perturbing forces is restricted to those spatial locations where the perturbations have been experienced by the moving arm. The subjects also are able to compensate for forces experienced at neighboring workspace locations. However, adaptation decays smoothly and quickly with distance from the locations where disturbances had been sensed by the moving limb. (ii) Our experiments also how that the CNS builds an internal model of the external perturbing forces in intrinsic (muscles and / or joints) coordinates.

Adult↗

PubMed1996

The control of stable postures in the multijoint arm.

The stiffness that is measured at the hand of a multijoint arm emerges from the combined effects of the elastic properties of the muscles and joints, the geometry of the linkages and muscle attachments, and the neural control circuits that act on the arm. The effective stiffness of a nonlinear linkage such as a two-joint arm depends on the force acting on the system as well as the intrinsic stiffness of the actuators. This paper presents an analysis of the factors that affect limb stiffness, including the effects of external forces. Three potential strategies for controlling the stability of the limb are proposed and demonstrated by computer simulations. The predictions from the simulations are then compared experimentally with measured stiffness values for human subjects working against an external force. These experiments were directed toward understanding what strategies are used by the CNS to control limb stiffness and stability. The experimental evidence showed that human subjects must increase the stiffness at the joints in order to maintain limb stability in the presence of applied external forces at the hand. In the process we identified a precise role for muscles which span two or more joints in the control of over all limb stiffness. A local strategy may be used to achieve limb stability, in which the muscle stiffness increases with muscle force. Multijoint muscles are shown to provide mechanical couplings which are necessary for the maintenance of stability. By utilizing these muscles, the neuro-musculo-skeletal system can control a global property of the system (stability) with a passive local strategy.

Adult↗

PubMed1995

Modular organization of motor behavior in the frog's spinal cord.

The complex issue of translating the planning of arm movements into muscle forces is discussed in relation to the recent discovery of structures in the spinal cord. These structures contain circuitry that, when activated, produce precisely balanced contractions in groups of muscles. These synergistic contractions generate forces that direct the limb toward an equilibrium point in space. Remarkably, the force outputs, produced by activating different spinal-cord structures, sum vectorially. This vectorial combination of motor outputs might be a mechanism for producing a vast repertoire of motor behaviors in a simple manner.

Animals↗

PubMed1994

Linear combinations of primitives in vertebrate motor control.

Recent investigations on the spinalized frog have provided evidence suggesting that the neural circuits in the spinal cord are organized into a number of distinct functional modules. We have investigated the rule that governs the coactivation of two such modules. To this end, we have developed an experimental paradigm that involves the simultaneous stimulation of two sites in the frog's spinal cord and the quantitative comparison of the resulting mechanical response with the summation of the responses obtained from the stimulation of each site. We found that the simultaneous stimulation of two sites leads to the vector summation of the endpoint forces generated by each site separately. This linear behavior is quite remarkable and provides strong support to the view that the central nervous system may generate a wide repertoire of motor behaviors through the vectorial superposition of a few motor primitives stored within the neural circuits in the spinal cord.

Animals↗

PubMed1994

Adaptive representation of dynamics during learning of a motor task.

We investigated how the CNS learns to control movements in different dynamical conditions, and how this learned behavior is represented. In particular, we considered the task of making reaching movements in the presence of externally imposed forces from a mechanical environment. This environment was a force field produced by a robot manipulandum, and the subjects made reaching movements while holding the end-effector of this manipulandum. Since the force field significantly changed the dynamics of the task, subjects' initial movements in the force field were grossly distorted compared to their movements in free space. However, with practice, hand trajectories in the force field converged to a path very similar to that observed in free space. This indicated that for reaching movements, there was a kinematic plan independent of dynamical conditions. The recovery of performance within the changed mechanical environment is motor adaptation. In order to investigate the mechanism underlying this adaptation, we considered the response to the sudden removal of the field after a training phase. The resulting trajectories, named aftereffects, were approximately mirror images of those that were observed when the subjects were initially exposed to the field. This suggested that the motor controller was gradually composing a model of the force field, a model that the nervous system used to predict and compensate for the forces imposed by the environment. In order to explore the structure of the model, we investigated whether adaptation to a force field, as presented in a small region, led to aftereffects in other regions of the workspace. We found that indeed there were aftereffects in workspace regions where no exposure to the field had taken place; that is, there was transfer beyond the boundary of the training data. This observation rules out the hypothesis that the subject's model of the force field was constructed as a narrow association between visited states and experienced forces; that is, adaptation was not via composition of a look-up table. In contrast, subjects modeled the force field by a combination of computational elements whose output was broadly tuned across the motor state space. These elements formed a model that extrapolated to outside the training region in a coordinate system similar to that of the joints and muscles rather than end-point forces. This geometric property suggests that the elements of the adaptive process represent dynamics of a motor task in terms of the intrinsic coordinate system of the sensors and actuators.

Adaptation, Physiological↗

PubMed1993

Postural force fields of the human arm and their role in generating multijoint movements.

When a perturbation displaces the human hand from equilibrium, arm muscles respond by producing restoring forces. When a set of displacements are given at various directions from the same equilibrium position, the resulting restoring forces form a "postural force field." It is not known whether these postural forces are related to those generated when a reaching movement is executed. However, if a movement is a consequence of a shift of the equilibrium position of the hand toward the target, then, from the postural force field, predictions can be made regarding the nature of the elastic forces acting on the hand during the movement. We have taken the first steps in testing this hypothesis by measuring the postural force field of a subject's arm over relatively large distances, and comparing these forces with the static forces generated at the hand while the subject attempted a reaching movement. Using a robot manipulandum, the hand was displaced at various directions from an equilibrium position. The measured restoring forces were fitted to a nonlinear model to define a postural force field for that equilibrium position. This field was used to predict elastic forces generated when the subject attempted to move the manipulandum from a point on the circumference of a circle to a target at its center--the center corresponded to the equilibrium position at which the postural field was measured. In some of the movement trials, the manipulandum was locked during approximately the first 120 msec of the program for motion and the resulting static "evoked" forces measured. We found that (1) the evoked forces did not point to the target, but were a function of the configuration of the arm and rotated with the shoulder joint, and (2) the magnitude of the evoked forces varied systematically, even though the movements were of the same magnitude. These patterns were remarkably similar to those observed in the postural forces. Our results provide experimental evidence linking maintenance of posture in a multijoint system to that of generating a movement. The evidence is consistent with the hypothesis that the CNS programs a reaching movement by shifting the equilibrium position of the hand toward the target.

Adult↗

PubMed1993

Convergent force fields organized in the frog's spinal cord.

Microstimulation of the gray matter of the frog's spinal cord was used to elicit motor responses. Force responses were recorded with the frog's ankle clamped while EMG activity was monitored. The collections of force patterns elicited at different leg configurations were summarized as force fields. These force fields showed convergence to an equilibrium point. The equilibrium paths were calculated from the force fields with the leg clamped. These paths predicted free limb motion in 75% of trials. The force fields were separated into active and prestimulation resting responses. The active force field responses had a fixed position equilibrium. These active force fields were modulated in amplitude over time, although the balance and orientations of forces in the pattern remained fixed. The active fields grouped into a few classes. These included both convergent and parallel fields. The convergent force fields (CFFS) could be observed in deafferented preparations. Motoneuron (MN) activity underlying the force fields was marked using sulforhodamine. The marked activity covered several segments. Several simulations and MN stimulations show that topography, limb geometry, and random activation could not account for the results. It is likely that propriospinal interneurons distribute the activity that underlies the responses observed here. Experiments showed that CFFs that resemble those elicited by microstimulation also underlie natural behaviors. The full variety of fields revealed by microstimulation was larger than the repertoire elicited by cutaneous stimulation. It was concluded that fixed-pattern force fields elicited in the spinal cord may be viewed as movement primitives. These force fields could form building blocks for more complex behaviors.

Animals↗