Biomedical subjects

Miriam Zacksenhouse

Publications and source records attributed to Miriam Zacksenhouse.

3 recordsLinked to original sources

PubMed2006

Coordinative structure of manipulative hand-movements facilitates their recognition.

Manipulative hand movements involve coordinated movements of the fingers to manipulate an object within the hand, and are classified as either simultaneous or sequential. Simultaneous hand movements are characterized by single coordinated patterns of digit movements, while sequential hand movements involve sequences of such patterns. Here, we investigate the extent of the coordination among 15 hand-joints during simultaneous hand movements, and demonstrate that it leads to a concise representation that facilitates movement recognition. Principal component analysis (PCA), performed in the 15-dimensional (15-D) joint-space, indicates that the first principal-component captures more than 98% of the variability in individual hand movements. Consequently, the first principal direction provides a 15-D feature-vector that describes the underlying-coordination and can be used for automatic recognition. We evaluated this recognition strategy on a set of nine simultaneous hand-movements using a database of six users, each performing six sessions. A dedicated classifier for each user resulted in recognition rates of 97.0 +/- 4.7% during testing, while a single generic classifier achieved 95.2 +/- 2.5% recognition rates. We conclude that the suggested feature-vector captures the invariant structure of simultaneous hand-movements, facilitates their recognition, and may provide insight into motor planning.

Artificial Intelligence↗

PubMed2006

Temporal decoding by phase-locked loops: unique features of circuit-level implementations and their significance for vibrissal information processing.

Rhythmic active touch, such as whisking, evokes a periodic reference spike train along which the timing of a novel stimulus, induced, for example, when the whiskers hit an external object, can be interpreted. Previous work supports the hypothesis that the whisking-induced spike train entrains a neural implementation of a phase-locked loop (NPLL) in the vibrissal system. Here we extend this work and explore how the entrained NPLL decodes the delay of the novel, contact-induced stimulus and facilitates object localization. We consider two implementations of NPLLs, which are based on a single neuron or a neural circuit, respectively, and evaluate the resulting temporal decoding capabilities. Depending on the structure of the NPLL, it can lock in either a phase- or co-phase-sensitive mode, which is sensitive to the timing of the input with respect to the beginning of either the current or the next cycle, respectively. The co-phase-sensitive mode is shown to be unique to circuit-based NPLLs. Concentrating on temporal decoding in the vibrissal system of rats, we conclude that both the nature of the information processing task and the response characteristics suggest that the computation is sensitive to the co-phase. Consequently, we suggest that the underlying thalamocortical loop should implement a circuit-based NPLL.

Animals↗

PubMed2005

Cortical ensemble adaptation to represent velocity of an artificial actuator controlled by a brain-machine interface.

Monkeys can learn to directly control the movements of an artificial actuator by using a brain-machine interface (BMI) driven by the activity of a sample of cortical neurons. Eventually, they can do so without moving their limbs. Neuronal adaptations underlying the transition from control of the limb to control of the actuator are poorly understood. Here, we show that rapid modifications in neuronal representation of velocity of the hand and actuator occur in multiple cortical areas during the operation of a BMI. Initially, monkeys controlled the actuator by moving a hand-held pole. During this period, the BMI was trained to predict the actuator velocity. As the monkeys started using their cortical activity to control the actuator, the activity of individual neurons and neuronal populations became less representative of the animal's hand movements while representing the movements of the actuator. As a result of this adaptation, the animals could eventually stop moving their hands yet continue to control the actuator. These results show that, during BMI control, cortical ensembles represent behaviorally significant motor parameters, even if these are not associated with movements of the animal's own limb.

Adaptation, Physiological↗