Biomedical subjects

Pierre Baraduc

Publications and source records attributed to Pierre Baraduc.

8 recordsLinked to original sources

PubMed2006

Computational motor control: redundancy and invariance.

The nervous system controls the behavior of complex kinematically redundant biomechanical systems. How it computes appropriate commands to generate movements is unknown. Here we propose a model based on the assumption that the nervous system: 1) processes static (e.g., gravitational) and dynamic (e.g., inertial) forces separately; 2) calculates appropriate dynamic controls to master the dynamic forces and progress toward the goal according to principles of optimal feedback control; 3) uses the size of the dynamic commands (effort) as an optimality criterion; and 4) can specify movement duration from a given level of effort. The model was used to control kinematic chains with 2, 4, and 7 degrees of freedom [planar shoulder/elbow, three-dimensional (3D) shoulder/elbow, 3D shoulder/elbow/wrist] actuated by pairs of antagonist muscles. The muscles were modeled as second-order nonlinear filters and received the dynamics commands as inputs. Simulations showed that the model can quantitatively reproduce characteristic features of pointing and grasping movements in 3D space, i.e., trajectory, velocity profile, and final posture. Furthermore, it accounted for amplitude/duration scaling and kinematic invariance for distance and load. These results suggest that motor control could be explained in terms of a limited set of computational principles.

Algorithms↗

PubMed2006

"Paradoxical kinesis" is not a hallmark of Parkinson's disease but a general property of the motor system.

Although slowness of movement is a typical feature of Parkinson's disease (PD), it has been suggested that severely disabled patients remained able to produce normal motor responses in the context of urgent or externally driven situations. To investigate this phenomenon (often designated "paradoxical kinesis"), we required PD patients and healthy subjects to press a large switch under three main conditions: Self Generated, produce the fastest possible movement; External Cue, same as Self Generated but in response to an acoustic cue; Urgent External Cue, same as External Cue with the switch controlling an electromagnet that prevented a ball falling at the bottom of a tilted ramp. Task difficulty was equalized for the two experimental groups. Results showed that external cues and urgent conditions decreased movement duration (Urgent External Cue < External Cue < Self Generated) and reaction time (Urgent External Cue < External Cue). The amount of reduction was identical in PD patients and healthy subjects. These observations show that paradoxical kineses are not a hallmark of PD or a byproduct of basal ganglia dysfunctions, but a general property of the motor system.

Aged↗

PubMed2004

Consolidation of dynamic motor learning is not disrupted by rTMS of primary motor cortex.

Motor skills, once learned, are often retained over a long period of time. However, such learning first undergoes a period of consolidation after practice. During this time, the motor memory is susceptible to being disrupted by the performance of another motor-learning task. Recently, it was shown that repetitive transcranial magnetic stimulation (rTMS) over the primary motor cortex could disrupt the retention of a newly learned ballistic task in which subjects had to oppose their index finger and thumb as rapidly as possible. Here we investigate whether the motor cortex is similarly involved during the consolidation that follows learning novel dynamics. We applied rTMS to primary motor cortex shortly after subjects had either learned to compensate for a dynamic force field applied to their index finger or learned a ballistic finger abduction task. rTMS severely degraded the retention of the learning for the ballistic task but had no effect on retention of the dynamic force-field learning. This suggests that, unlike learning of simple ballistic skills, learning of dynamics may be stored in a more distributed manner, possibly outside the primary motor cortex.

Adult↗

PubMed2002

Role of uncertainty in sensorimotor control.

Neural signals are corrupted by noise and this places limits on information processing. We review the processes involved in goal-directed movements and how neural noise and uncertainty determine aspects of our behaviour. First, noise in sensory signals limits perception. We show that, when localizing our hand, the central nervous system (CNS) integrates visual and proprioceptive information, each with different noise properties, in a way that minimizes the uncertainty in the overall estimate. Second, noise in motor commands leads to inaccurate movements. We review an optimal-control framework, known as 'task optimization in the presence of signal-dependent noise', which assumes that movements are planned so as to minimize the deleterious consequences of noise and thereby minimize inaccuracy. Third, during movement, sensory and motor signals have to be integrated to allow estimation of the body's state. Models are presented that show how these signals are optimally combined. Finally, we review how the CNS deals with noise at the neural and network levels. In all of these processes, the CNS carries out the tasks in such a way that the detrimental effects of noise are minimized. This shows that it is important to consider effects at the neural level in order to understand performance at the behavioural level.

Humans↗

PubMed2002

Neurophysiology: cerebral carbon copies.

Predicting the consequences of our actions is essential for sensorimotor control. A candidate neural pathway underlying the prediction of eye position during saccades has been reported.

Animals↗

PubMed2002

A neural model of perceptual-motor alignment.

Sensorimotor systems face complex and frequent discrepancies among spatial modalities, for example, growth, optical distortion, and telemanipulation. Adaptive mechanisms must act continuously to restore perceptual-motor alignments necessary for perception of a coherent world. Experimental manipulations that exposed participants to localized discrepancies showed that adaptation is revealed by the acquisition of a constrained relation between entire modalities rather than associations between individual exemplars within these modalities. The computational problem faced by the human nervous system can thus be conceived as having to induce constrained relations between continuous stimulus and response dimensions from ambiguous or incomplete training sets, that is, performing interpolation and extrapolation. How biological neuronal networks solve this problem is unknown. Here we show that neural processing based on linear collective computation and least-square (LS) error learning in populations of frequency-coded neurons (i.e., whose discharge varies in a monotonic fashion with a parameter) has built-in interpolation and extrapolation capacities. This model can account for the properties of perceptual-motor adaptations in sensorimotor systems.

Adaptation, Physiological↗

PubMed2002

Adaptation to a visuomotor shift depends on the starting posture.

Previous studies have shown that human subjects can adapt to a new visuomotor relationship that depends on the trajectory of the arm. However, these studies have not distinguished between hand- and joint-based learning models. We have examined whether different endpoint kinematics are necessary to obtain a differential visuomotor shift. The joint trajectory was varied by changing the initial posture, while maintaining a similar finger trajectory. After learning, maximum after-effects were found when movement began with the posture used during exposure to the visuomotor shift and decreased with the difference between initial and trained posture. This was shown to be independent of the final posture attained. Our results show that adaptation to a visual remapping cannot be due to the recoding of a desired final posture and depends on the arm trajectory in joint space.

Adult↗

PubMed2002

Population computation of vectorial transformations.

Many neurons of the central nervous system are broadly tuned to some sensory or motor variables. This property allows one to assign to each neuron a preferred attribute (PA). The width of tuning curves and the distribution of PAs in a population of neurons tuned to a given variable define the collective behavior of the population. In this article, we study the relationship of the nature of the tuning curves, the distribution of PAs, and computational properties of linear neuronal populations. We show that noise-resistant distributed linear algebraic processing and learning can be implemented by a population of cosine tuned neurons assuming a nonuniform but regular distribution of PAs. We extend these results analytically to the noncosine tuning and uniform distribution case and show with a numerical simulation that the results remain valid for a nonuniform regular distribution of PAs for broad noncosine tuning curves. These observations provide a theoretical basis for modeling general nonlinear sensorimotor transformations as sets of local linearized representations.

Algorithms↗