Biomedical subjects

Amir Karniel

Publications and source records attributed to Amir Karniel.

4 recordsLinked to original sources

PubMed2005

Bimanual adaptation: internal representations of bimanual rhythmic movements.

From tying your shoes and clipping your tie to the claps at the end of a fine seminar, bimanual coordination plays a major role in our daily activities. An important phenomenon in bimanual coordination is the predisposition toward mirror symmetry in the performance of bimanual rhythmic movements. Although learning and adaptation in bimanual coordination are phenomena that have been observed, they have not been studied in the context of adaptive control and internal representations-approaches that were successfully employed in the arena of reaching movements and adaptation to force perturbations. In this paper we examine the dynamics of the learning mechanisms involved when subjects are trained to perform a bimanual non-harmonic polyrhythm in a bimanual index finger tapping task. Subjects are trained in this task implicitly, using altered visual feedback, while their performance is continuously monitored throughout the experiment. Our experimental results indicate the existence of significant (p<<0.01) learning curves (i.e., error plots with significantly negative slopes) during training and aftereffects with a washout period after the visual feedback ceases to be altered. These results confirm the formation of internal representations in bimanual motor control. We present a simple, physiologically plausible, neural model that combines feedback and adaptation in the control process and which is able to reproduce key phenomena of bimanual coordination and adaptation.

Adaptation, Physiological↗

PubMed2005

Computational analysis in vitro: dynamics and plasticity of a neuro-robotic system.

When the brain interacts with the environment it constantly adapts by representing the environment in a form that is called an internal model. The neurobiological basis for internal models is provided by the connectivity and the dynamical properties of neurons. Thus, the interactions between neural tissues and external devices provide a fundamental means for investigating the connectivity and dynamical properties of neural populations. We developed this idea, suggested in the 1980s by Valentino Braitenberg, for investigating and representing the dynamical behavior of neuronal populations in the brainstem of the lamprey. The brainstem was maintained in vitro and connected in a closed loop with two types of artificial device: (a) a simulated dynamical system and (b) a small mobile robot. In both cases, the device was controlled by recorded extracellular signals and its output was translated into electrical stimuli delivered to the neural system. The goal of the first study was to estimate the dynamical dimension of neural preparation in a single-input/single-output configuration. The dynamical dimension is the number of state variables that together with the applied input determine the output of a system. The results indicate that while this neural system has significant dynamical properties, its effective complexity, as established by the dynamical dimension, is rather moderate. In the second study, we considered a more specific situation, in which the same portion of the nervous system controls a robotic device in a two-input/two-output configuration. We fitted the input-output data from the neuro-robotic preparation to neural network models having different internal dynamics and we observed the generalization error of each model. Consistent with the first study, this second experiment showed that a simple recurrent dynamical model was able to capture the behavior of the hybrid system. This experimental and computational framework provides the means for investigating neural plasticity and internal representations in the context of brain-machine interfaces.

Animals↗

PubMed2003

Sequence, time, or state representation: how does the motor control system adapt to variable environments?

Does the observation of well-timed movements imply the existence of some internal representation of time, such as a hypothetical neural clock? Here we report the results of experiments designed to investigate whether subjects form a correct adaptive representation of mechanical environments that change in a very predictable manner. In these experiments, subjects were asked to execute arm movements over a two-dimensional workspace while experiencing time-dependent disturbing forces. We provide a formal definition for time representation and conclude that our subjects didn't use time representation for motor adaptation under the tested conditions. Subjects performed arm-reaching movements in the following experiments: (1) six experiments in a sinusoidal time-varying force field; (2) six experiments in a simple sequence of alternating viscous force fields, in which the number of targets allowed for the approximation of the force by a complex state-dependent force field; and (3) six experiments in the same simple sequence of alternating viscous force fields, in which no state-dependent force field approximation was possible. We found that the subjects did not adapt to the time-varying force field and were unable to form an adequate representation of the simple sequence of force fields. In the latter case, whenever possible, they adapted to a single state-dependent field that produced forces similar to the two alternating fields. This state-dependent field produced the same forces as the applied sequence of fields only over the trajectories that subjects executed during the training phase. However, the state-dependent field was inadequate to produce the correct forces generated by the field sequence over a new set of trajectories.These results are not consistent with the hypothesis that subjects would develop a correct representation of time-dependent forces, at least under the tested circumstances. We speculate that the system responsible for adaptation of movements to external forces may be unable to employ temporal representation. While it is possible that such a representation may emerge in a more prolonged and/or intense training, our findings indicate a preference by the adaptive system to generalize based on representing dependence of external forces upon state rather than upon time.

Adaptation, Physiological↗

PubMed2003

Dynamical dimension of a hybrid neurorobotic system.

The goal of this work is to understand how neural tissue can be programed to execute predetermined functions. We developed a research tool that includes the brainstem of a lamprey and a two-wheeled robot interconnected in a closed loop. We report here the development of a framework for studying the dynamics of the neural tissue based on the interaction of this tissue with the robot.

Animals↗