Luc P. J. Selen

Noise in the nervous system.

Noise — random disturbances of signals — poses a fundamental problem for information processing and affects all aspects of nervous-system function. However, the nature, amount and impact of noise in the nervous system have only recently been addressed in a quantitative manner. Experimental and computational methods have shown that multiple noise sources contribute to cellular and behavioural trial-to-trial variability. We review the sources of noise in the nervous system, from the molecular to the behavioural level, and show how noise contributes to trial-to-trial variability. We highlight how noise affects neuronal networks and the principles the nervous system applies to counter detrimental effects of noise, and briefly discuss noises potential benefits.

Trunk muscle activation in low-back pain patients, an analysis of the literature

This paper provides an analysis of the literature on trunk muscle recruitment in low-back pain patients. Two models proposed in the literature, the pain-spasm-pain model and the pain adaptation model, yield conflicting predictions on how low- back pain would affect trunk muscle recruitment in various activities. The two models are outlined and evidence for the two from neurophsysiological studies is reviewed. Subsequently, specific predictions with respect to changes in activation of the lumbar extensor musculature are derived from both models. These predictions are compared to the results from 30 clinical studies and three induced pain studies retrieved in a comprehensive literature search. Neither of the two models is unequivocally supported by the literature. These data and further data on timing of muscle activity and load sharing between muscles suggest an alternative model to explain the alterations of trunk muscle recruitment due to low-back pain. It is proposed that motor control changes in patients are functional in that they enhance spinal stability.

Impedance Control Reduces Instability That Arises from Motor Noise

There is ample evidence that humans are able to control the endpoint impedance of their arms in response to active destabilizing force fields. However, such fields are uncommon in daily life. Here, we examine whether the CNS selectively controls the endpoint impedance of the arm in the absence of active force fields but in the presence of instability arising from task geometry and signal-dependent noise (SDN) in the neuromuscular system. Subjects were required to generate forces, in two orthogonal directions, onto four differently curved rigid objects simulated by a robotic manipulandum. The endpoint stiffness of the limb was estimated for each object curvature. With increasing curvature, the endpoint stiffness increased mainly parallel to the object surface and to a lesser extent in the orthogonal direction. Therefore, the orientation of the stiffness ellipses did not orient to the direction of instability. Simulations showed that the observed stiffness geometries and their pattern of change with instability are the result of a tradeoff between maximizing the mechanical stability and minimizing the destabilizing effects of SDN. Therefore, it would have been suboptimal to align the stiffness ellipse in the direction of instability. The time course of the changes in stiffness geometry suggests that modulation takes place both within and across trials. Our results show that an increase in stiffness relative to the increase in noise can be sufficient to reduce kinematic variability, thereby allowing stiffness control to improve stability in natural tasks.

Deliberation in the Motor System: Reflex Gains Track Evolving Evidence Leading to a Decision

Both decision making and sensorimotor control require real-time processing of noisy information streams. Historically these processes were thought to operate sequentially: cognitive processing leads to a decision, and the outcome is passed to the motor system to be converted into action. Recently, it has been suggested that the decision process may provide a continuous flow of information to the motor system, allowing it to prepare in a graded fashion for the probable outcome. Such continuous flow is supported by electrophysiology in nonhuman primates. Here we provide direct evidence for the continuous flow of an evolving decision variable to the motor system in humans. Subjects viewed a dynamic random dot display and were asked to indicate their decision about direction by moving a handle to one of two targets. We probed the state of the motor system by perturbing the arm at random times during decision formation. Reflex gains were modulated by the strength and duration of motion, reflecting the accumulated evidence in support of the evolving decision. The magnitude and variance of these gains tracked a decision variable that explained the subjects decision accuracy. The findings support a continuous process linking the evolving computations associated with decision making and sensorimotor control.

Can co-activation reduce kinematic variability? A simulation study

Impedance modulation has been suggested as a means to suppress the effects of internal ‘noise’ on movement kinematics. We investigated this hypothesis in a neuro-musculo-skeletal model. A prerequisite is that the muscle model produces realistic force variability. We found that standard Hill-type models do not predict realistic force variability in response to variability in stimulation. In contrast, a combined motor-unit pool model and a pool of parallel Hill-type motor units did produce realistic force variability as a function of target force, largely independent of how the force was transduced to the tendon. To test the main hypothesis, two versions of the latter model were simulated as an antagonistic muscle pair, controlling the position of a frictionless hinge joint, with a distal segment having realistic inertia relative to the muscle strength. Increasing the impedance through co-activation resulted in less kinematic variability, except for the lowest levels of co-activation. Model behavior in this region was affected by the noise amplitude and the inertial properties of the model. Our simulations support the idea that muscular co-activation is in principle an effective strategy to meet accuracy demands.

Impedance is modulated to meet accuracy demands during goal-directed arm movements

The neuromuscular system is inherently noisy and joint impedance may serve to filter this noise. In the present experiment, we investigated whether individuals modulate joint impedance to meet spatial accuracy demands. Twelve subjects were instructed to make rapid, time constrained, elbow extensions to three differently sized targets. Some trials (20 out of 140 for each target, randomly assigned) were perturbed mechanically at 75% of movement amplitude. Inertia, damping and stiffness were estimated from the torque and angle deviation signal using a forward simulation and optimization routine. Increases in endpoint accuracy were not always reflected in a decrease in trajectory variability. Only in the final quarter of the trajectory the variability decreased as target width decreased. Stiffness estimates increased significantly with accuracy constraints. Damping estimates only increased for perturbations that were initially directed against the movement direction. We concluded that joint impedance modulation is one of the strategies used by the neuromuscular system to generate accurate movements, at least during the final part of the movement.

Multisensory Processing in Spatial Orientation: An Inverse Probabilistic Approach

Most evidence that the brain uses Bayesian inference to integrate noisy sensory signals optimally has been obtained by showing that the noise levels in each modality separately can predict performance in combined conditions. Such a forward approach is difficult to implement when the various signals cannot be measured in isolation, as in spatial orientation, which involves the processing of visual, somatosensory, and vestibular cues. Instead, we applied an inverse probabilistic approach, based on optimal observer theory. Our goal was to investigate whether the perceptual differences found when probing two different states—body-in-space and head-in-space orientation—can be reconciled by a shared scheme using all available sensory signals. Using a psychometric approach, seven human subjects were tested on two orientation estimates at tilts <120°: perception of body tilt [subjective body tilt (SBT)] and perception of visual vertical [subjective visual vertical (SVV)]. In all subjects, the SBT was more accurate than the SVV, which showed substantial systematic errors for tilt angles beyond 60°. Variability increased with tilt angle in both tasks, but was consistently lower in the SVV. The sensory integration model fitted both datasets very nicely. A further experiment, in which supine subjects judged their head orientation relative to the body, independently confirmed the predicted head-on-body noise by the model. Model predictions based on the derived noise properties from the various modalities were also consistent with previously published deficits in vestibular and somatosensory patients. We conclude that Bayesian computations can account for the typical differences in spatial orientation judgments associated with different task requirements.

Fatigue-induced changes of impedance and performance in target tracking

Kinematic variability is caused, in part, by force fluctuations. It has been shown empirically and numerically that the effects of force fluctuations on kinematics can be suppressed by increasing joint impedance. Given that force variability increases with muscular fatigue, we hypothesized that joint impedance would increase with fatigue to retain a prescribed accuracy level. To test this hypothesis, subjects tracked a target by elbow flexion and extension both with fatigued and unfatigued elbow flexor and extensor muscles. Joint impedance was estimated from controlled perturbations to the elbow. Contrary to the hypothesis, elbow impedance decreased, whereas performance, expressed as the time-on-target, was unaffected by fatigue. Further analysis of the data revealed that subjects changed their control strategy with increasing fatigue. Although their overall kinematic variability increased, task performance was retained by staying closer to the center of the target when fatigued. In conclusion, the present study reveals a limitation of impedance modulation in the control of movement variability.

Visual stability across combined eye and body motion

In order to maintain visual stability during self-motion, the brain needs to update any egocentric spatial representations of the environment. Here, we use a novel psychophysical approach to investigate how and to what extent the brain integrates visual, extraocular, and vestibular signals pertaining to this spatial update. Participants were oscillated sideways at a frequency of 0.63 Hz while keeping gaze fixed on a stationary light. When the motion direction changed, a reference target was shown either in front of or behind the fixation point. At the next reversal, half a cycle later, we tested updating of this reference location by asking participants to judge whether a briefly flashed probe was shown to the left or right of the memorized target. We show that updating is not only biased, but that the direction and magnitude of this bias depend on both gaze and object location, implying that a gaze-centered reference frame is involved. Using geometric modeling, we further show that the gaze-dependent errors can be caused by an underestimation of translation amplitude, by a bias of visually perceived objects towards the fovea (i.e., a foveal bias), or by a combination of both.

Generalization and transfer of contextual cues in motor learning

We continuously adapt our movements in daily life, forming new internal models whenever necessary and updating existing ones. Recent work has suggested that this flexibility is enabled via sensorimotor cues, serving to access the correct internal model whenever necessary and keeping new models apart from previous ones. While research to date has mainly focused on identifying the nature of such cue representations, here we investigated whether and how these cue representations generalize, interfere, and transfer within and across effector systems. Subjects were trained to make two-stage reaching movements: a premovement that served as a cue, followed by a targeted movement that was perturbed by one of two opposite curl force fields. The direction of the premovement was uniquely coupled to the direction of the ensuing force field, enabling simultaneous learning of the two respective internal models. After training, generalization of the two premovement cues representations was tested at untrained premovement directions, within both the trained and untrained hand. We show that the individual premovement representations generalize in a Gaussian-like pattern around the trained premovement direction. When the force fields are of unequal strengths, the cue-dependent generalization skews toward the strongest field. Furthermore, generalization patterns transfer to the nontrained hand, in an extrinsic reference frame. We conclude that contextual cues do not serve as discrete switches between multiple internal models. Instead, their generalization suggests a weighted contribution of the associated internal models based on the angular separation from the trained cues to the net motor output.