Functional reorganization of upper-body movement after spinal cord injury.

Survivors of spinal cord injury need to reorganize their residual body movements for interacting with assistive devices and performing activities that used to be easy and natural. To investigate movement reorganization, we asked subjects with high-level spinal cord injury (SCI) and unimpaired subjects to control a cursor on a screen by performing upper-body motions. While this task would be normally accomplished by operating a computer mouse, here shoulder motions were mapped into the cursor position.

Functional reorganization of upper-body movements for wheelchair control.

In general, survivors of neuromotor disorders and injuries need to reorganize their body movements in order to achieve goals that used to be easy and natural. Often, disabled people are offered the option to control assistive devices that will facilitate the recovery of independence and capability in their daily lives. The knowledge acquired during the last few years in the motor control field can be used to study and enhance this learning process.

Learning algorithms for human-machine interfaces.

The goal of this study is to create and examine machine learning algorithms that adapt in a controlled and cadenced way to foster a harmonious learning environment between the user and the controlled device. To evaluate these algorithms, we have developed a simple experimental framework. Subjects wear an instrumented data glove that records finger motions.

Adapting human-machine interfaces to user performance.

The goal of this study was to create and examine machine learning algorithms that adapt in a controlled and cadenced way to foster a harmonious learning environment between the user of a human-machine interface and the controlled device. In this experiment, subjects' high-dimensional finger motions remotely controlled the joint angles of a simulated planar 2-link arm, which was used to hit targets on a computer screen. Subjects were required to move the cursor at the endpoint of the simulated arm.

Seeing versus believing: conflicting immediate and predicted feedback lead to suboptimal motor performance.

Reaching hand movements tend to follow straight paths. Previous work has suggested that when visual feedback is perturbed such that straight hand motions are seen as curved motions, the motor system adapts to restore straight visual motion. We show that under a nonlinear visuomotor transformation, one that maps straight hand motions to high-curvature motions of a visual cursor, reaching movements do not converge with practice toward a straight path of either the hand or the cursor.

Deciding when and how to correct a movement: discrete submovements as a decision making process.

Rapid reaching movements of human and non-human primates are often characterized by irregular multi-peaked velocity profiles. How to interpret these irregularities is still under debate. While some reports assert that these irregularities are the result of a continuous controller interacting with the environment, we and others hold that the velocity irregularities are evidence for a controller that produces discrete movement corrections.

Kinematic properties of on-line error corrections in the monkey.

Despite the abundant experimental evidence for the irregular, multipeaked velocity profiles that often characterize rapid human limb movements, there is currently little agreement on how to interpret these phenomena. While in some studies these irregularities have been interpreted as reflecting a continuous control process, in others the irregularities are considered to be evidence for the existence of discrete movement primitives that are initiated by an intermittent controller. Here we introduce a novel "soft symmetry" method for analyzing irregular movements and decomposing them into their discrete movement primitives.

Primary auditory cortex of cats: feature detection or something else?

Neurons in sensory cortices are often assumed to be "feature detectors", computing simple and then successively more complex features out of the incoming sensory stream. These features are somehow integrated into percepts. Despite many years of research, a convincing candidate for such a feature in primary auditory cortex has not been found.

Neural model for physiological responses to frequency and amplitude transitions uncovers topographical order in the auditory cortex.

We characterize primary auditory cortex (AI) units using a neural model for the detection of frequency and amplitude transitions. The model is a generalization of a model for the detection of amplitude transition. A set of neurons, tuned in the spectrotemporal domain, is created by means of neural delays and frequency filtering.