Miriam Zacksenhouse

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 animals 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 animals own limb.

Chapter 6 Temporal and spatial coding in the rat vibrissal system

Publisher Summary This chapter discusses the temporal and spatial coding in the rat vibrissal system. Spatial encoding of the world is accomplished by arrays of receptors, which are spatially organized across the sensory organs. Each receptor is sensitive to a specific and limited range within the sensation spectrum. Temporal encoding is attained by the temporal pattern of receptor firing, which is not limited to the dynamic aspects of the stimulus, and can also be used for encoding stationary aspects. Primates explore the texture of objects by moving their fingers across it, and rodents scan their environment by moving their whiskers. Tactile acquisition by the whiskers is a motor sensory active process. Whiskers are moved by the motor system to acquire information, which is then analyzed by the sensory system. Changes in pressure, caused by the presence of an object, or by the ridges and grooves across its surface, are detected by the mechanoreceptors. The rat vibrissal system provides a clear example of dissociated coding schemes.

Cortical modulations increase in early sessions with brain-machine interface.

Background During planning and execution of reaching movements, the activity of cortical motor neurons is modulated by a diversity of motor, sensory, and cognitive signals. Brain-machine interfaces (BMIs) extract part of these modulations to directly control artificial actuators. However, cortical modulations that emerge in the novel context of operating the BMI are poorly understood. Methodology/Principal Findings Here we analyzed the changes in neuronal modulations that occurred in different cortical motor areas as monkeys learned to use a BMI to control reaching movements. Using spike-train analysis methods we demonstrate that the modulations of the firing-rates of cortical neurons increased abruptly after the monkeys started operating the BMI. Regression analysis revealed that these enhanced modulations were not correlated with the kinematics of the movement. The initial enhancement in firing rate modulations declined gradually with subsequent training in parallel with the improvement in behavioral performance. Conclusions/Significance We conclude that the enhanced modulations are related to computational tasks that are significant especially in novel motor contexts. Although the function and neuronal mechanism of the enhanced cortical modulations are open for further inquiries, we discuss their potential role in processing execution errors and representing corrective or explorative activity. These representations are expected to contribute to the formation of internal models of the external actuator and their decoding may facilitate BMI improvement.

Oscillatory neural networks for robotic yo-yo control

Different networks of coupled oscillators were developed for open-loop control of periodic motion. However, some tasks, like yo-yo playing, are open-loop unstable and require proper phase locking to stabilize. Given the phase-locking property of coupled oscillators, we investigate their application to closed-loop control of open-loop unstable systems, concentrating on the challenging task of yo-yo control. In particular, we focus on pulse-coupling, where the yo-yo sends a feedback upon reaching the bottom of the string and the onset of the oscillatory cycle is used to trigger the movement. Four networks involving either a stand-alone or a circuit level oscillator with either excitatory or inhibitory couplings are considered. Working curve analysis indicates that three of the networks cannot stabilize the yo-yo. The fourth network, which is based on a circuit-level oscillator, is analyzed using the return map and the region of stability is determined and verified by simulations. The resulting pulse-coupled oscillatory control provides a model-free control strategy that operates with an easy-to-measure low-rate feedback.

Accuracy/robustness dilemma in impedance control

Impedance control facilitates the execution of tasks that involve contact with the environment. However task performance depends on the accuracy at which the desired impedance is attained. This paper focuses on feedback methods for implementing impedance control and reveals the underlying conflict between impedance accuracy and robustness to uncertainties. Furthermore, we propose a novel yet practical method that facilitates robustness while maintaining accurate impedance tracking. Eigenvalue analysis and simulation results are presented to demonstrate the accuracy/robustness dilemma and the relative merits of the different methods.

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 thewhiskers 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. Hereweextend thiswork and explorehowthe 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.

Robotic yoyo playing with visual feedback

Robotic yoyo playing is a challenging, open-loop unstable game, which requires dynamic dexterity to stabilize. The switching control strategy proposed here stabilizes the yoyo by determining when to start its activation. A straightforward implementation is presented and demonstrated, using continuous state estimation provided by visual feedback and the previously developed dynamic model. The discrete return map associated with the original continuous system is analyzed and shown to possess a single physically relevant fixed point. Stability is guaranteed as long as the control parameter, which determines the magnitude of the acceleration, is above a certain level. The stabilizing power of the proposed control algorithm is successfully demonstrated on a real-time robotic yoyo playing system. Theoretical predictions regarding the fixed point of the return map are confirmed experimentally, providing further support for our modeling and analysis.

Sensitivity of basic oscillatory mechanisms for pattern generation and detection

Abstract.u2002Intrinsic oscillators are the basic building blocks of central pattern generators, which model the neural circuits underlying pattern generation. Coupled intrinsic oscillators have been shown to synchronize their oscillatory frequencies and to maintain a characteristic pattern of phase relationships. Recently, oscillatory neurons have also been identified in sensory systems that are involved in decoding phase information. It has been hypothesized that the neural oscillators are part of neural circuits that implement phase-locked loops (PLLs), which are well-known electrical circuits for temporal decoding. Thus, there is evidence that intrinsic neural oscillators participate in both temporal pattern generation and temporal pattern decoding. The present paper investigates the dynamics underlying forced oscillators and forced PLLs, using a single framework, and compares both their stability and sensitivity characteristics. In particular, a method for assessing whether an oscillatory neuron is forced directly or indirectly, as part of a PLL, is developed and applied to published data.

Do we know how to set decision thresholds for diabetes

The diagnosis of diabetes, based on measured fasting plasma glucose level, depends on choosing a threshold level for which the probability of failing to detect disease (missed diagnosis), as well as the probability of falsely diagnosing disease (false alarm), are both small. The Bayesian risk provides a tool for aggregating and evaluating the risks of missed diagnosis and false alarm. However, the underlying probability distributions are uncertain, which makes the choice of the decision threshold difficult. We discuss an hypothesis for choosing the threshold that can robustly achieve acceptable risk. Our analysis is based on info-gap decision theory, which is a non-probabilistic methodology for modelling and managing uncertainty. Our hypothesis is that the non-probabilistic method of info-gap robust decision making is able to select decision thresholds according to their probability of success. This hypothesis is motivated by the relationship between info-gap robustness and the probability of success, which has been observed in other disciplines (biology and economics). If true, it provides a valuable clinical tool, enabling the clinician to make reliable diagnostic decisions in the absence of extensive probabilistic information. Specifically, the hypothesis asserts that the physician is able to choose a diagnostic threshold that maximizes the probability of acceptably small Bayesian risk, without requiring accurate knowledge of the underlying probability distributions. The actual value of the Bayesian risk remains uncertain.

Instantaneous model impedance control for robots

We propose a new method for implementing impedance control, which is designed to take advantage of the error-correction capabilities of position controllers, while maintaining good impedance tracking performance. The proposed instantaneous model impedance control re-initializes the impedance model to the current position of the robot so the model does not accumulate position errors. The novelty of the method is in using the position feedback both in the outer loop, to track the desired impedance, and in the inner loop to improve robustness. The proposed method includes the dynamic-based and the position-based methods as specific cases, and can be tuned to trade between their corresponding merits. Using simulations, we demonstrate the performance of the new method in comparison with the previous methods. We consider two scenarios: stiff environment, which challenges the position based approach, and load uncertainties, which challenges the dynamic based approach.