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In JoVE (1)
Other Publications (16)
- Biological Cybernetics
- IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
- Journal of Neural Engineering
- Experimental Brain Research. Experimentelle Hirnforschung. Expérimentation Cérébrale
- Journal of Neurophysiology
- Neural Computation
- Experimental Brain Research. Experimentelle Hirnforschung. Expérimentation Cérébrale
- Frontiers in Neuroscience
- Frontiers in Neuroscience
- PloS One
- Cognitive Neuropsychology
- Journal of Neurophysiology
- Experimental Brain Research. Experimentelle Hirnforschung. Expérimentation Cérébrale
- Journal of Integrative Neuroscience
- IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
- The Journal of Neuroscience : the Official Journal of the Society for Neuroscience
Articles by Amir Karniel in JoVE
JoVE Neuroscience
One Dimensional Turing-Like Handshake Test for Motor Intelligence
Biomedical Engineering, Ben-Gurion University
We present a Turing-like Handshake test administered through a telerobotic system in which the interrogator is holding a robotic stylus and interacting with another party (human or artificial). We use a forced choice method, and extract a measure for the similarity of the artificial model to a human handshake.
Other articles by Amir Karniel on PubMed
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.
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.
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.
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.
Explaining Patterns of Neural Activity in the Primary Motor Cortex Using Spinal Cord and Limb Biomechanics Models
What determines the specific pattern of activation of primary motor cortex (M1) neurons in the context of a given motor task? We present a systems level physiological model describing the transformation from the neural activity in M1, through the muscle control signal, into joint torques and down to endpoint forces and movements. The redundancy of the system is resolved by biologically plausible optimization criteria. The model explains neural activity at both the population, and single neuron, levels. Due to the model's relative simplicity and analytic tractability, it provides intuition as to the most salient features of the system as well as a possible causal explanation of how these determine the overall behavior. Moreover, it explains a large number of recent observations, including the temporal patterns of single-neuron and population firing rates during isometric and movement tasks, narrow tuning curves, non cosine tuning curves, changes of preferred directions during a task, and changes of preferred directions due to different experimental conditions.
Minimum Acceleration Criterion with Constraints Implies Bang-bang Control As an Underlying Principle for Optimal Trajectories of Arm Reaching Movements
Rapid arm-reaching movements serve as an excellent test bed for any theory about trajectory formation. How are these movements planned? A minimum acceleration criterion has been examined in the past, and the solution obtained, based on the Euler-Poisson equation, failed to predict that the hand would begin and end the movement at rest (i.e., with zero acceleration). Therefore, this criterion was rejected in favor of the minimum jerk, which was proved to be successful in describing many features of human movements. This letter follows an alternative approach and solves the minimum acceleration problem with constraints using Pontryagin's minimum principle. We use the minimum principle to obtain minimum acceleration trajectories and use the jerk as a control signal. In order to find a solution that does not include nonphysiological impulse functions, constraints on the maximum and minimum jerk values are assumed. The analytical solution provides a three-phase piecewise constant jerk signal (bang-bang control) where the magnitude of the jerk and the two switching times depend on the magnitude of the maximum and minimum available jerk values. This result fits the observed trajectories of reaching movements and takes into account both the extrinsic coordinates and the muscle limitations in a single framework. The minimum acceleration with constraints principle is discussed as a unifying approach for many observations about the neural control of movements.
Involvement of the Autonomic Nervous System in Motor Adaptation: Acceleration or Error Reduction?
In the last few decades motor adaptation was extensively studied observing the invariant features of reaching movements. In a parallel neurobehavioral line of research emotional learning was studied under the umbrella of the 'two-factor theory of learning'. In this study we explore the relation between motor learning and the autonomic response (heart rate, HR) of subjects performing point to point reaching movements holding a computer mouse. We consider two alternative outcomes: one is that autonomic response correlates with the learning rate and the second is that the autonomic response correlates with the residual error at the steady state. Eighteen subjects performed reaching movements under perturbed visual feedback demonstrating learning and after effects of learning. The hand movement as well as an Electrocardiogram signal were recorded throughout the training and carefully analyzed offline to extract the trial by trial error as well as the heart period. The results show clear correlation between the change in HR and the residual error but no correlation between the change in HR and the learning rate supporting the second alternative that the sensitivity to errors but not the learning rate correlates with the autonomic response. A control group of another seven subjects underwent the same experiment without the perturbed visual feedback. This control group showed no change in the HR. Further studies are required to validate this hypothesis and unravel the mechanism by which the autonomic response correlates with the residual motor error.
A Simple and Accurate Onset Detection Method for a Measured Bell-shaped Speed Profile
Motor control neuroscientists measure limb trajectories and extract the onset of the movement for a variety of purposes. Such trajectories are often aligned relative to the onset of individual movement before the features of that movement are extracted and their properties are inspected. Onset detection is performed either manually or automatically, typically by selecting a velocity threshold. Here, we present a simple onset detection algorithm that is more accurate than the conventional velocity threshold technique. The proposed method is based on a simple regression and follows the minimum acceleration with constraints model, in which the initial phase of the bell-shaped movement is modeled by a cubic power of the time. We demonstrate the performance of the suggested method and compare it to the velocity threshold technique and to manual onset detection by a group of motor control experts. The database for this comparison consists of simulated minimum jerk trajectories and recorded reaching movements.
New Perspectives on the Dialogue Between Brains and Machines
Brain-machine interfaces (BMIs) are mostly investigated as a means to provide paralyzed people with new communication channels with the external world. However, the communication between brain and artificial devices also offers a unique opportunity to study the dynamical properties of neural systems. This review focuses on bidirectional interfaces, which operate in two ways by translating neural signals into input commands for the device and the output of the device into neural stimuli. We discuss how bidirectional BMIs help investigating neural information processing and how neural dynamics may participate in the control of external devices. In this respect, a bidirectional BMI can be regarded as a fancy combination of neural recording and stimulation apparatus, connected via an artificial body. The artificial body can be designed in virtually infinite ways in order to observe different aspects of neural dynamics and to approximate desired control policies.
Adaptation to Delayed Force Perturbations in Reaching Movements
Adaptation to deterministic force perturbations during reaching movements was extensively studied in the last few decades. Here, we use this methodology to explore the ability of the brain to adapt to a delayed velocity-dependent force field. Two groups of subjects preformed a standard reaching experiment under a velocity dependent force field. The force was either immediately proportional to the current velocity (Control) or lagged it by 50 ms (Test). The results demonstrate clear adaptation to the delayed force perturbations. Deviations from a straight line during catch trials were shifted in time compared to post-adaptation to a non-delayed velocity dependent field (Control), indicating expectation to the delayed force field. Adaptation to force fields is considered to be a process in which the motor system predicts the forces to be expected based on the state that a limb will assume in response to motor commands. This study demonstrates for the first time that the temporal window of this prediction needs not to be fixed. This is relevant to the ability of the adaptive mechanisms to compensate for variability in the transmission of information across the sensory-motor system.
Months in Space: Synaesthesia Modulates Attention and Action
Month-space synaesthetes experience months as sequences arranged in spatially defined configurations. While most works on synaesthesia have studied its perceptual implications, this study focuses on the synaesthetic influence on a synaesthete's action behaviour. S.M., a month-space synaesthete, and 5 matched controls performed a spatial Stroop-like task in a haptics and virtual reality combined environment, which was especially designed to simulate S.M.'s three-dimensional synaesthetic experience. In the experiment, a circle and a word were presented simultaneously. The word consisted of either a month name or a direction name and was located at the centre of the screen, while the circle was displayed in one of four peripheral positions-top, bottom, right, or left. When S.M. was asked to ignore the word and reach for the circle, no effects were found. In contrast, when she was asked to ignore the circle and reach for a location indicated by the word, a congruency effect was found for both months and direction words. Crucially, these effects were evident in all measurements of reaching performance (i.e., path, velocity, and trajectory of movement). Our findings revealed that for month-space synaesthetes, months trigger spatial shifts of attention in a similar manner as directions do. Moreover, these shifts of attention affected not only latent cognitive processes (i.e., reaction time) but also overt behaviour (i.e., entire hand movements).
Proximodistal Gradient in the Perception of Delayed Stiffness
Proximal and distal muscles are different in size, maximum force, mechanical action, and neuromuscular control. In the current study we explore the perception of delayed stiffness when probing is executed using movement of different joints. We found a proximodistal gradient in the amount of underestimation of delayed stiffness in the transition between probing with shoulder, elbow, and wrist joints. Moreover, there was a similar gradient in the optimal weighting between estimation of stiffness and the inverse of estimation of compliance that predicted the perception of the subjects. These gradients could not be ascribed to differences in movement amplitude, duration, velocity, and force amplitude because these variables were not significantly modulated by the joint used for probing. Mean force did not follow a similar gradient either. Therefore we suggest that the observed gradient in perception reveals a proximodistal gradient in control, such that proximal joints are dominated by force control, whereas distal joints are dominated by position control.
Evidence for Predictive Control in Lifting Series of Virtual Objects
The human motor control system gracefully behaves in a dynamic and time varying environment. Here, we explored the predictive capabilities of the motor system in a simple motor task of lifting a series of virtual objects. When a subject lifts an object, she/he uses an expectation of the weight of the object to generate a motor command. All models of motor learning employ learning algorithms that essentially expect the future to be similar to the previously experienced environment. In this study, we asked subjects to lift a series of increasing weights and determined whether they extrapolated from past experience and predicted the next weight in the series even though that weight had never been experienced. The grip force at the beginning of the lifting task is a clean indication of the motor expectation. In contrast to the motor learning literature asserting adaptation by means of expecting a weighted average based on past experience, our results suggest that the motor system is able to predict the subsequent weight that follows a series of increasing weights.
Open Questions in Computational Motor Control
Computational motor control covers all applications of quantitative tools for the study of the biological movement control system. This paper provides a review of this field in the form of a list of open questions. After an introduction in which we define computational motor control, we describe: a Turing-like test for motor intelligence; internal models, inverse model, forward model, feedback error learning and distal teacher; time representation, and adaptation to delay; intermittence control strategies; equilibrium hypotheses and threshold control; the spatiotemporal hierarchy of wide sense adaptation, i.e., feedback, learning, adaptation, and evolution; optimization based models for trajectory formation and optimal feedback control; motor memory, the past and the future; and conclude with the virtue of redundancy. Each section in this paper starts with a review of the relevant literature and a few more specific studies addressing the open question, and ends with speculations about the possible answer and its implications to motor neuroscience. This review is aimed at concisely covering the topic from the author's perspective with emphasis on learning mechanisms and the various structures and limitations of internal models.
Lack of Predictive Control in Lifting Series of Virtual Objects by Individuals with Diplegic Cerebral Palsy
To date, research on the motor control of hand function in cerebral palsy has focused on children with hemiplegia, although many persons with diplegic cerebral palsy (dCP) have asymmetrically decreased hand function. We explored the predictive capabilities of the motor system in a simple motor task of lifting a series of virtual objects for five persons with spastic dCP and five age-matched controls. When a person lifts an object, s/he uses an expectation of the weight of the object to generate a motor command. We asked the study subjects to lift a series of increasing weights and determined whether they extrapolated from past experience to predict the next weight in the series, even though that weight had never been experienced. Planning of precision grasp was assessed by measurement of the grip force at the beginning of the lifting task and by estimating the motor command. Execution of precision grasp was assessed by measurement of the time interval between the onset of grip and the onset of movement. We found that persons with dCP demonstrated a lack of predictive feed-forward control in their lifting movements: they exhibited a significantly longer time between onset of grip and onset of movement than the control subjects and they did not predict the weight of the next object in the lifting task. In addition, for subjects with dCP, the time between the onset of grip and the onset of movement of the dominant hand correlated strongly with the outcome of a hand function test. We postulate that a higher-order motor planning deficit in addition to execution deficit are evident in the subjects with spastic diplegic.
How Soft is That Pillow? The Perceptual Localization of the Hand and the Haptic Assessment of Contact Rigidity
A new haptic illusion is described, in which the location of the mobile object affects the perception of its rigidity. There is theoretical and experimental support for the notion that limb position sense results from the brain combining ongoing sensory information with expectations arising from prior experience. How does this probabilistic state information affect one's tactile perception of the environment mechanics? In a simple estimation process, human subjects were asked to report the relative rigidity of two simulated virtual objects. One of the objects remained fixed in space and had various coefficients of stiffness. The other virtual object had constant stiffness but moved with respect to the subjects. Earlier work suggested that the perception of an object's rigidity is consistent with a process of regression between the contact force and the perceived amount of penetration inside the object's boundary. The amount of penetration perceived by the subject was affected by varying the position of the object. This, in turn, had a predictable effect on the perceived rigidity of the contact. Subjects' reports on the relative rigidity of the object are best accounted for by a probabilistic model in which the perceived boundary of the object is estimated based on its current location and on past observations. Therefore, the perception of contact rigidity is accounted for by a stochastic process of state estimation underlying proprioceptive localization of the hand.
