Internal models in sensorimotor integration: perspectives from adaptive control theory

Neural dynamics of delayed feedback in robot teleoperation: insights from fNIRS analysis

Introduction

As robot teleoperation increasingly becomes integral in executing tasks in distant, hazardous, or inaccessible environments, operational delays remain a significant obstacle. These delays, inherent in signal transmission and processing, adversely affect operator performance, particularly in tasks requiring precision and timeliness. While current research has made strides in mitigating these delays through advanced control strategies and training methods, a crucial gap persists in understanding the neurofunctional impacts of these delays and the efficacy of countermeasures from a cognitive perspective.

Methods

This study addresses the gap by leveraging functional Near-Infrared Spectroscopy (fNIRS) to examine the neurofunctional implications of simulated haptic feedback on cognitive activity and motor coordination under delayed conditions. In a human-subject experiment (N = 41), sensory feedback was manipulated to observe its influences on various brain regions of interest (ROIs) during teleoperation tasks. The fNIRS data provided a detailed assessment of cerebral activity, particularly in ROIs implicated in time perception and the execution of precise movements.

Results

Our results reveal that the anchoring condition, which provided immediate simulated haptic feedback with a delayed visual cue, significantly optimized neural functions related to time perception and motor coordination. This condition also improved motor performance compared to the asynchronous condition, where visual and haptic feedback were misaligned.

Discussion

These findings provide empirical evidence about the neurofunctional basis of the enhanced motor performance with simulated synthetic force feedback in the presence of teleoperation delays. The study highlights the potential for immediate haptic feedback to mitigate the adverse effects of operational delays, thereby improving the efficacy of teleoperation in critical applications.

Representational horizon and visual space orientation: An investigation into the role of visual contextual cues on spatial mislocalisations

The perceived offset position of a moving target has been found to be displaced forward, in the direction of motion (Representational Momentum; RM), downward, in the direction of gravity (Representational Gravity; RG), and, recently, further displaced along the horizon implied by the visual context (Representational Horizon; RH). The latter, while still underexplored, offers the prospect to clarify the role of visual contextual cues in spatial orientation and in the perception of dynamic events. As such, the present work sets forth to ascertain the robustness of Representational Horizon across varying types of visual contexts, particularly between interior and exterior scenes, and to clarify to what degree it reflects a perceptual or response phenomenon. To that end, participants were shown targets, moving along one out of several possible trajectories, overlaid on a randomly chosen background depicting either an interior or exterior scene rotated -22.5º, 0º, or 22.5º in relation to the actual vertical. Upon the vanishing of the target, participants were required to indicate its last seen location with a computer mouse. For half the participants, the background vanished with the target while for the remaining it was kept visible until a response was provided. Spatial localisations were subjected to a discrete Fourier decomposition procedure to obtain independent estimates of RM, RG, and RH. Outcomes showed that RH's direction was biased towards the horizon implied by the visual context, but solely for exterior scenes, and irrespective of its presence or absence during the spatial localisation response, supporting its perceptual/representational nature.

An experimentally informed computational model of neurovestibular adaptation to altered gravity

Transitions to altered gravity environments result in acute sensorimotor impairment for astronauts, leading to serious mission and safety risks in the crucial first moments in a new setting. Our understanding of the time course and severity of impairment in the early stages of adaptation remains limited and confounded by unmonitored head movements, which are likely to impact the rate of adaptation. Here, we aimed to address this gap by using a human centrifuge to simulate the first hour of hypergravity (1.5g) exposure and the subsequent 1g readaptation period, with precisely controlled head tilt activity. We quantified head tilt overestimation via subjective visual vertical and found ∼30% tilt overestimation that did not decrease over the course of 1 h of exposure to the simulated gravity environment. These findings extended the floor of the vestibular adaptation window (with controlled vestibular cueing) to 1 h of exposure to altered gravity. We then used the empirical data to inform a computational model of neurovestibular adaptation to changes in the magnitude of gravity, which can offer insight into the adaptation process and, with further tuning, can be used to predict the temporal dynamics of vestibular-mediated misperceptions in altered gravity.

Modeling orientation perception adaptation to altered gravity environments with memory of past sensorimotor states

Transitioning between gravitational environments results in a central reinterpretation of sensory information, producing an adapted sensorimotor state suitable for motor actions and perceptions in the new environment. Critically, this central adaptation is not instantaneous, and complete adaptation may require weeks of prolonged exposure to novel environments. To mitigate risks associated with the lagging time course of adaptation (e.g., spatial orientation misperceptions, alterations in locomotor and postural control, and motion sickness), it is critical that we better understand sensorimotor states during adaptation. Recently, efforts have emerged to model human perception of orientation and self-motion during sensorimotor adaptation to new gravity stimuli. While these nascent computational frameworks are well suited for modeling exposure to novel gravitational stimuli, they have yet to distinguish how the central nervous system (CNS) reinterprets sensory information from familiar environmental stimuli (i.e., readaptation). Here, we present a theoretical framework and resulting computational model of vestibular adaptation to gravity transitions which captures the role of implicit memory. This advancement enables faster readaptation to familiar gravitational stimuli, which has been observed in repeat flyers, by considering vestibular signals dependent on the new gravity environment, through Bayesian inference. The evolution and weighting of hypotheses considered by the CNS is modeled via a Rao-Blackwellized particle filter algorithm. Sensorimotor adaptation learning is facilitated by retaining a memory of past harmonious states, represented by a conditional state transition probability density function, which allows the model to consider previously experienced gravity levels (while also dynamically learning new states) when formulating new alternative hypotheses of gravity. In order to demonstrate our theoretical framework and motivate future experiments, we perform a variety of simulations. These simulations demonstrate the effectiveness of this model and its potential to advance our understanding of transitory states during which central reinterpretation occurs, ultimately mitigating the risks associated with the lagging time course of adaptation to gravitational environments.

A First Principles Approach to Subjective Experience

Understanding the neural bases of subjective experience remains one of the great challenges of the natural sciences. Higher-order theories of consciousness are typically defended by assessments of neural activity in higher cortical regions during perception, often with disregard to the nature of the neural computations that these regions execute. We have sought to refocus the problem toward identification of those neural computations that are necessary for subjective experience with the goal of defining the sorts of neural architectures that can perform these operations. This approach removes reliance on behaviour and brain homologies for appraising whether non-human animals have the potential to subjectively experience sensory stimuli. Using two basic principles—first, subjective experience is dependent on complex processing executing specific neural functions and second, the structure-determines-function principle—we have reasoned that subjective experience requires a neural architecture consisting of stacked forward models that predict the output of neural processing from inputs. Given that forward models are dependent on appropriately connected processing modules that generate prediction, error detection and feedback control, we define a minimal neural architecture that is necessary (but not sufficient) for subjective experience. We refer to this framework as the hierarchical forward models algorithm. Accordingly, we postulate that any animal lacking this neural architecture will be incapable of subjective experience.

COMPASS: Computations for Orientation and Motion Perception in Altered Sensorimotor States

Reliable perception of self-motion and orientation requires the central nervous system (CNS) to adapt to changing environments, stimuli, and sensory organ function. The proposed computations required of neural systems for this adaptation process remain conceptual, limiting our understanding and ability to quantitatively predict adaptation and mitigate any resulting impairment prior to completing adaptation. Here, we have implemented a computational model of the internal calculations involved in the orientation perception system’s adaptation to changes in the magnitude of gravity. In summary, we propose that the CNS considers parallel, alternative hypotheses of the parameter of interest (in this case, the CNS’s internal estimate of the magnitude of gravity) and uses the associated sensory conflict signals (i.e., difference between sensory measurements and the expectation of them) to sequentially update the posterior probability of each hypothesis using Bayes rule. Over time, an updated central estimate of the internal magnitude of gravity emerges from the posterior probability distribution, which is then used to process sensory information and produce perceptions of self-motion and orientation. We have implemented these hypotheses in a computational model and performed various simulations to demonstrate quantitative model predictions of adaptation of the orientation perception system to changes in the magnitude of gravity, similar to those experienced by astronauts during space exploration missions. These model predictions serve as quantitative hypotheses to inspire future experimental assessments.

The influence of auditory rhythms on the speed of inferred motion

The present research explored the influence of isochronous auditory rhythms on the timing of movement-related prediction in two experiments. In both experiments, participants observed a moving disc that was visible for a predetermined period before disappearing behind a small, medium, or large occluded area for the remainder of its movement. In Experiment 1, the disc was visible for 1 s. During this period, participants were exposed to either a fast or slow auditory rhythm, or they heard nothing. They were instructed to press a key to indicate when they believed the moving disc had reached a specified location on the other side of the occluded area. The procedure measured the (signed) error in participants' estimate of the time it would take for a moving object to contact a stationary one. The principal results of Experiment 1 were main effects of the rate of the auditory rhythm and of the size of the occlusion on participants' judgments. In Experiment 2, the period of visibility was varied with size of the occlusion area to keep the total movement time constant for all three levels of occlusion. The results replicated the main effect of rhythm found in Experiment 1 and showed a small, significant interaction, but indicated no main effect of occlusion size. Overall, the results indicate that exposure to fast isochronous auditory rhythms during an interval of inferred motion can influence the imagined rate of such motion and suggest a possible role of an internal rhythmicity in the maintenance of temporally accurate dynamic mental representations.

Revisiting the Instrumented Romberg Test: Can Today's Technology Offer a Risk-of-Fall Screening Device for Senior Citizens? An Experience-Based Approach

Risk of fall (ROF) is a worldwide major concern for its prevalence and consequent dramatic outcomes in the elderly population. The growing age-related risk appears to be associated with increasing motor, sensory, and cognitive problems in the elderly population. There is a consensus on the need to screen for these balance dysfunctions, but the available methods are largely based on subjectively assessed performances. The instrumented Romberg test using a force plate represents a validated assessment process for the evaluation of balance performances. The purpose of this study is to propose an innovative instrumental method to identify balance deficits, assess their severity, and give an automated indication of the most likely etiology. The proposed new method was applied to the instrumented Romberg test, using force plate data recorded in a cohort of 551 females aged >65 participating in adapted physical activity courses. The method allowed us to identify 145 dysfunctional subjects and to determine the likely origin of their deficit: 21 central, 5 vestibular, 9 visual, 59 proprioceptive (musculoskeletal etiology), and 51 functional. Based on the preliminary findings of the study, this test could be an efficient and cost-effective mass screening tool for identifying subjects at risk of fall, since the procedure proves to be rapid, non-invasive, and apparently devoid of any contraindications.

Bayesian State Estimation in Sensorimotor Systems With Particle Filtering

In sensorimotor control, sensory feedback integrates with forward models to alleviate the impacts of sensory noise and delay on state estimation. The sensorimotor integration is subject to Bayesian inference and has been formulated by the Kalman filter in computational neuroscience. However, the Kalman filter, as an artificial optimal estimator to address the abstract characteristics of spatial perception, is inadequate to present the neural computation in the cerebellum. Besides, the nonlinear neuromuscular dynamics with tightly coupled state variables also substantially impedes the implementation of Kalman filter in realistic sensorimotor systems. Here we address the sensorimotor state estimate by using the particle filter, a nonlinear Bayesian estimator that can be implemented in arbitrary dynamic systems with the neurocomputational compatibility. Particle filtering is explicitly implemented in a biophysically realistic sensorimotor model of an upper limb integrating Hill-type muscles, tendons, skeleton, and primary afferents. By involving the command noises, the constructed neuromusculoskeletal model qualitatively represents the experimental variability in center-out reaching movements. Despite the initial estimation uncertainty and sensorimotor noises, the particle filter is able to approximate the actual states in forward-reaching movements. Furthermore, the simulated hand-position estimate is consistent with the experimental results, in the presence of forward model errors, neural noises, and sensory delays. The particle filter is demonstrated to effectively implement the Bayesian state estimation in biophysically realistic sensorimotor systems and provide better compatibility with neuronal computation than the Kalman filter.

Tutorial Review of Bio-Inspired Approaches to Robotic Manipulation for Space Debris Salvage

We present a comprehensive tutorial review that explores the application of bio-inspired approaches to robot control systems for grappling and manipulating a wide range of space debris targets. Current robot manipulator control systems exploit limited techniques which can be supplemented by additional bio-inspired methods to provide a robust suite of robot manipulation technologies. In doing so, we review bio-inspired control methods because this will be the key to enabling such capabilities. In particular, force feedback control may be supplemented with predictive forward models and software emulation of viscoelastic preflexive joint behaviour. This models human manipulation capabilities as implemented by the cerebellum and muscles/joints respectively. In effect, we are proposing a three-level control strategy based on biomimetic forward models for predictive estimation, traditional feedback control and biomimetic muscle-like preflexes. We place emphasis on bio-inspired forward modelling suggesting that all roads lead to this solution for robust and adaptive manipulator control. This promises robust and adaptive manipulation for complex tasks in salvaging space debris.

The Neural Engine: A Reprogrammable Low Power Platform for Closed-Loop Optogenetics

Brain-machine Interfaces (BMI) hold great potential for treating neurological disorders such as epilepsy. Technological progress is allowing for a shift from open-loop, pacemaker-class, intervention towards fully closed-loop neural control systems. Low power programmable processing systems are therefore required which can operate within the thermal window of 2° C for medical implants and maintain long battery life. In this work, we have developed a low power neural engine with an optimized set of algorithms which can operate under a power cycling domain. We have integrated our system with a custom-designed brain implant chip and demonstrated the operational applicability to the closed-loop modulating neural activities in in-vitro and in-vivo brain tissues: the local field potentials can be modulated at required central frequency ranges. Also, both a freely-moving non-human primate (24-hour) and a rodent (1-hour) in-vivo experiments were performed to show system reliable recording performance. The overall system consumes only 2.93 mA during operation with a biological recording frequency 50 Hz sampling rate (the lifespan is approximately 56 hours). A library of algorithms has been implemented in terms of detection, suppression and optical intervention to allow for exploratory applications in different neurological disorders. Thermal experiments demonstrated that operation creates minimal heating as well as battery performance exceeding 24 hours on a freely moving rodent. Therefore, this technology shows great capabilities for both neuroscience in-vitro/in-vivo applications and medical implantable processing units.

Sensorimotor impairment from a new analog of spaceflight-altered neurovestibular cues

Exposure to microgravity during spaceflight causes central reinterpretations of orientation sensory cues in astronauts, leading to sensorimotor impairment upon return to Earth. Currently there is no ground-based analog for the neurovestibular system relevant to spaceflight. We propose such an analog, which we term the "wheelchair head-immobilization paradigm" (WHIP). Subjects lie on their side on a bed fixed to a modified electric wheelchair, with their head restrained by a custom facemask. WHIP prevents any head tilt relative to gravity, which normally produces coupled stimulation to the otoliths and semicircular canals, but does not occur in microgravity. Decoupled stimulation is produced through translation and rotation on the wheelchair by the subject using a joystick. Following 12 h of WHIP exposure, subjects systematically felt illusory sensations of self-motion when making head tilts and had significant decrements in balance and locomotion function using tasks similar to those assessed in astronauts postspaceflight. These effects were not observed in our control groups without head restraint, suggesting the altered neurovestibular stimulation patterns experienced in WHIP lead to relevant central reinterpretations. We conclude by discussing the findings in light of postspaceflight sensorimotor impairment, WHIP's uses beyond a spaceflight analog, limitations, and future work.NEW & NOTEWORTHY We propose, implement, and demonstrate the feasibility of a new analog for spaceflight-altered neurovestibular stimulation. Following extended exposure to the analog, we found subjects reported illusory self-motion perception. Furthermore, they demonstrated decrements in balance and locomotion, using tasks similar to those used to assess astronaut sensorimotor performance postspaceflight.

Mathematical models for dynamic, multisensory spatial orientation perception

Mathematical models have been proposed for how the brain interprets sensory information to produce estimates of self-orientation and self-motion. This process, spatial orientation perception, requires dynamically integrating multiple sensory modalities, including visual, vestibular, and somatosensory cues. Here, we review the progress in mathematical modeling of spatial orientation perception, focusing on dynamic multisensory models, and the experimental paradigms in which they have been validated. These models are primarily "black box" or "as if" models for how the brain processes spatial orientation cues. Yet, they have been effective scientifically, in making quantitative hypotheses that can be empirically assessed, and operationally, in investigating aircraft pilot disorientation, for example. The primary family of models considered, the observer model, implements estimation theory approaches, hypothesizing that internal models (i.e., neural systems replicating the behavior/dynamics of physical systems) are used to produce expected sensory measurements. Expected signals are then compared to actual sensory afference, yielding sensory conflict, which is weighted to drive central perceptions of gravity, angular velocity, and translation. This approach effectively predicts a wide range of experimental scenarios using a small set of fixed free parameters. We conclude with limitations and applications of existing mathematical models and important areas of future work.

Ultra-Low-Level Laser Therapy and Acupuncture Libralux: What Is so Special?

Background: Contrary to the most credited theories on laser therapy that see power/energy as the major factors to its effectiveness, a technique using an extremely low power/energy laser stimulation to treat musculoskeletal pain and dysfunction is proposed. The stimulus consists of a 20 s train of modulated pulses with an average power below 0.02 mW and is applied on sequences of acupuncture points selected according to the impaired segment of the patient’s body. Methods: Modifications on the extracellular soft tissue matrix and on the “fascia” were sonographically demonstrated. Laboratory and clinical tests confirmed the effectiveness. Results: Responses similar to those experienced in acupuncture were observed. The device—a CE Class IIa certified medical device named Libralux—affords a clinically proven effectiveness exceeding 80% in the treatment of musculoskeletal conditions and associated motor dysfunctions. An average of just three application sessions was generally sufficient to overcome the dysfunction. Conclusions: The development of the method is supported by over 20 years of R&D activities, with a range of experiments discussed in several papers published in indexed peer-reviewed journals. A few considerations regarding the possible physiological action mechanisms involved are proposed in this paper.

Cortico-cerebellar network involved in saccade adaptation

Saccade adaptation is the learning process that ensures that vision and saccades remain calibrated. The central nervous system network involved in these adaptive processes remains unclear because of difficulties in isolating the learning process from the correlated visual and motor processes. Here we imaged the human brain during a novel saccade adaptation paradigm that allowed us to isolate neural signals involved in learning independent of the changes in the amplitude of corrective saccades usually correlated with adaptation. We show that the changes in activation in the ipsiversive cerebellar vermis that track adaptation are not driven by the changes in corrective saccades and thus provide critical supporting evidence for previous findings. Similarly, we find that activation in the dorsomedial wall of the contraversive precuneus mirrors the pattern found in the cerebellum. Finally, we identify dorsolateral and dorsomedial cortical areas in the frontal and parietal lobes that encode the retinal errors following inaccurate saccades used to drive recalibration. Together, these data identify a distributed network of cerebellar and cortical areas and their specific roles in oculomotor learning. NEW & NOTEWORTHY The central nervous system constantly learns from errors and adapts to keep visual targets and saccades in registration. We imaged the human brain while the gain of saccades adapted to a visual target that was displaced while the eye was in motion, inducing retinal error. Activity in the cerebellum and precuneus tracked learning, whereas parts of the dorsolateral and dorsomedial frontal and parietal cortex encoded the retinal error used to drive learning.

Visuomotor coordination and cortical connectivity of modular motor learning

The ability to transfer sensorimotor skill components to new actions and the capacity to use skill components from whole actions are characteristic of the adaptability of the human sensorimotor system. However, behavioral evidence suggests complex limitations for transfer after combined or modular learning of motor adaptations. Also, to date, only behavioral analysis of the consequences of the modular learning has been reported, with little understanding of the sensorimotor mechanisms of control and the interaction between cortical areas. We programmed a video game with distorted kinematic and dynamic features to test the ability to combine sensorimotor skill components learned modularly (composition) and the capacity to use separate sensorimotor skill components learned in combination (decomposition). We examined motor performance, eye-hand coordination, and EEG connectivity. When tested for integrated learning, we found that combined practice initially performed better than separated practice, but differences disappeared after integrated practice. Separate learning promotes fewer anticipatory control mechanisms (depending more on feedback control), evidenced in a lower gaze leading behavior and in higher connectivity between visual and premotor domains, in comparison with the combined practice. The sensorimotor system can acquire motor modules in a separated or integrated manner. However, the system appears to require integrated practice to coordinate the adaptations with the skill learning and the networks involved in the integrated behavior. This integration seems to be related to the acquisition of anticipatory mechanism of control and with the decrement of feedback control.

An Inverse Optimal Control Approach to Explain Human Arm Reaching Control Based on Multiple Internal Models

Human motor control is highly efficient in generating accurate and appropriate motor behavior for a multitude of tasks. This paper examines how kinematic and dynamic properties of the musculoskeletal system are controlled to achieve such efficiency. Even though recent studies have shown that the human motor control relies on multiple models, how the central nervous system (CNS) controls this combination is not fully addressed. In this study, we utilize an Inverse Optimal Control (IOC) framework in order to find the combination of those internal models and how this combination changes for different reaching tasks. We conducted an experiment where participants executed a comprehensive set of free-space reaching motions. The results show that there is a trade-off between kinematics and dynamics based controllers depending on the reaching task. In addition, this trade-off depends on the initial and final arm configurations, which in turn affect the musculoskeletal load to be controlled. Given this insight, we further provide a discomfort metric to demonstrate its influence on the contribution of different inverse internal models. This formulation together with our analysis not only support the multiple internal models (MIMs) hypothesis but also suggest a hierarchical framework for the control of human reaching motions by the CNS.

Synapse fits neuron: joint reduction by model inversion

In this paper, we introduce a novel simplification method for dealing with physical systems that can be thought to consist of two subsystems connected in series, such as a neuron and a synapse. The aim of our method is to help find a simple, yet convincing model of the full cascade-connected system, assuming that a satisfactory model of one of the subsystems, e.g., the neuron, is already given. Our method allows us to validate a candidate model of the full cascade against data at a finer scale. In our main example, we apply our method to part of the squid's giant fiber system. We first postulate a simple, hypothetical model of cell-to-cell signaling based on the squid's escape response. Then, given a FitzHugh-type neuron model, we derive the verifiable model of the squid giant synapse that this hypothesis implies. We show that the derived synapse model accurately reproduces synaptic recordings, hence lending support to the postulated, simple model of cell-to-cell signaling, which thus, in turn, can be used as a basic building block for network models.

Vestibular stimulation interferes with the dynamics of an internal representation of gravity

The remembered vanishing location of a moving target has been found to be displaced downward in the direction of gravity (representational gravity) and more so with increasing retention intervals, suggesting that the visual spatial updating recruits an internal model of gravity. Despite being consistently linked with gravity, few inquiries have been made about the role of vestibular information in these trends. Previous experiments with static tilting of observers' bodies suggest that under conflicting cues between the idiotropic vector and vestibular signals, the dynamic drift in memory is reduced to a constant displacement along the body's main axis. The present experiment aims to replicate and extend these outcomes while keeping the observers' bodies unchanged in relation to physical gravity by varying the gravito-inertial acceleration using a short-radius centrifuge. Observers were shown, while accelerated to varying degrees, targets moving along several directions and were required to indicate the perceived vanishing location after a variable interval. Increases of the gravito-inertial force (up to 1.4G), orthogonal to the idiotropic vector, did not affect the direction of representational gravity, but significantly disrupted its time course. The role and functioning of an internal model of gravity for spatial perception and orientation are discussed in light of the results.

The Contribution of the Cerebellum in the Hierarchial Development of the Self

What distinguishes human beings from other living organisms is that a human perceives himself as a "self". The self is developed hierarchially in a multi-layered process, which is based on the evolutionary maturation of the nervous system and patterns according to the rules and demands of the external world. Many researchers have attempted to explain the different aspects of the self, as well as the related neural substrates. In this paper, we first review the previously proposed ideas regarding the neurobiology of the self. We then suggest a new hypothesis regarding the hierarchial self, which proposes that the self is developed at three stages: subjective, objective, and reflective selves. In the second part, we attempt to answer the question "Why do we need a self?" We therefore explain that different parts of the self developed in an effort to identify stability in space, stability against constantly changing objects, and stability against changing cognitions. Finally, we discuss the role of the cerebellum as the neural substrate for the self.

Adaptive Tracking Control for Robots with Unknown Kinematic and Dynamic Properties

It has been almost two decades since the first globally tracking convergent adaptive controllers were derived for robot with dynamic uncertainties. However, the problem of concurrent adaptation to both kinematic and dynamic uncertainties has never been systematically solved. This is the subject of this paper. We derive a new adaptive Jacobian controller for trajectory tracking of robot with uncertain kinematics and dynamics. It is shown that the robot endeffector is able to converge to a desired trajectory with the uncertain kinematics and dynamics parameters being updated online by parameter update laws. The algorithm requires only to measure the end-effector position, besides the robot's joint angles and joint velocities. The proposed controller can also be extended to adaptive visual tracking control with uncertain camera parameters, taking into consideration the uncertainties of the nonlinear robot kinematics and dynamics. Experimental results are presented to illustrate the performance of the proposed controllers. In the experiments, we demonstrate that the robot's shadow can be used to control the robot.

The visual representations of motion and of gravity are functionally independent: Evidence of a differential effect of smooth pursuit eye movements

The memory for the final position of a moving object which suddenly disappears has been found to be displaced forward, in the direction of motion, and downwards, in the direction of gravity. These phenomena were coined, respectively, Representational Momentum and Representational Gravity. Although both these and similar effects have been systematically linked with the functioning of internal representations of physical variables (e.g. momentum and gravity), serious doubts have been raised for a cognitively based interpretation, favouring instead a major role of oculomotor and perceptual factors which, more often than not, were left uncontrolled and even ignored. The present work aims to determine the degree to which Representational Momentum and Representational Gravity are epiphenomenal to smooth pursuit eye movements. Observers were required to indicate the offset locations of targets moving along systematically varied directions after a variable imposed retention interval. Each participant completed the task twice, varying the eye movements' instructions: gaze was either constrained or left free to track the targets. A Fourier decomposition analysis of the localization responses was used to disentangle both phenomena. The results show unambiguously that constraining eye movements significantly eliminates the harmonic components which index Representational Momentum, but have no effect on Representational Gravity or its time course. The found outcomes offer promising prospects for the study of the visual representation of gravity and its neurological substrates.

Type III-IV muscle afferents are not required for steady-state exercise hyperpnea in healthy subjects and patients with COPD or heart failure

Blockade of group III-IV muscle afferents by intrathecal injection of the μ-opioid agonist fentanyl (IF) in humans has been variously reported to depress exercise hyperpnea in some studies but not others. A key unanswered question is whether such an effect is transient or persists in the steady state. Here we show that in healthy subjects undergoing constant-load cycling exercise IF significantly slows the transient exercise ventilatory kinetics but has no discernible effect on the ventilatory response when exercise is sufficiently prolonged. Thus, the ventilatory response to group III-IV muscle afferents input in healthy subjects is not a simple reflex but acts like a high-pass filter with maximum sensitivity during early-phase exercise and is reset in the late phase. In patients with chronic heart failure (CHF) IF causes sustained CO2 retention not only during exercise but also in the resting state, where muscle afferents feedback is minimal. In patients with chronic obstructive pulmonary disease (COPD), IF also elicits sustained decreases in the exercise ventilatory response but with little or no resultant CO2 retention due to concomitant decreases in physiological VD/VT (dead space-to-ventilation ratio). These results support the proposition that optimal long-term regulation of exercise hyperpnea in health and in disease is determined centrally by the respiratory controller through the continuing adaptation of an internal model which dynamically tracks the metabolic CO2 load and the ventilatory inefficiency 1/1-VD/VT that must be overcome for the maintenance of arterial PCO2 homeostasis, rather than being reflexively driven by group III-IV muscle afferents feedback per se.

Sensory-motor integration in focal dystonia

Traditional definitions of focal dystonia point to its motor component, mainly affecting planning and execution of voluntary movements. However, focal dystonia is tightly linked also to sensory dysfunction. Accurate motor control requires an optimal processing of afferent inputs from different sensory systems, in particular visual and somatosensory (e.g., touch and proprioception). Several experimental studies indicate that sensory-motor integration - the process through which sensory information is used to plan, execute, and monitor movements - is impaired in focal dystonia. The neural degenerations associated with these alterations affect not only the basal ganglia-thalamic-frontal cortex loop, but also the parietal cortex and cerebellum. The present review outlines the experimental studies describing impaired sensory-motor integration in focal dystonia, establishes their relationship with changes in specific neural mechanisms, and provides new insight towards the implementation of novel intervention protocols. Based on the reviewed state-of-the-art evidence, the theoretical framework summarized in the present article will not only result in a better understanding of the pathophysiology of dystonia, but it will also lead to the development of new rehabilitation strategies.

Recasting brain-machine interface design from a physical control system perspective

With the goal of improving the quality of life for people suffering from various motor control disorders, brain-machine interfaces provide direct neural control of prosthetic devices by translating neural signals into control signals. These systems act by reading motor intent signals directly from the brain and using them to control, for example, the movement of a cursor on a computer screen. Over the past two decades, much attention has been devoted to the decoding problem: how should recorded neural activity be translated into the movement of the cursor? Most approaches have focused on this problem from an estimation standpoint, i.e., decoders are designed to return the best estimate of motor intent possible, under various sets of assumptions about how the recorded neural signals represent motor intent. Here we recast the decoder design problem from a physical control system perspective, and investigate how various classes of decoders lead to different types of physical systems for the subject to control. This framework leads to new interpretations of why certain types of decoders have been shown to perform better than others. These results have implications for understanding how motor neurons are recruited to perform various tasks, and may lend insight into the brain's ability to conceptualize artificial systems.

The cortical computations underlying feedback control in vocal production

Recent neurophysiological studies of speaking are beginning to elucidate the neural mechanisms underlying auditory feedback processing during vocalizations. Here we review how research findings impact our state feedback control (SFC) model of speech motor control. We will discuss the evidence for cortical computations that compare incoming feedback with predictions derived from motor efference copy. We will also review observations from auditory feedback perturbation studies that demonstrate clear evidence for a state estimate correction process, which drives compensatory motor behavioral responses. While there is compelling support for cortical computations in the SFC model, there are still several outstanding questions that await resolution by future neural investigations.

Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation

Restorative brain-computer interfaces (BCI) are increasingly used to provide feedback of neuronal states in a bid to normalize pathological brain activity and achieve behavioral gains. However, patients and healthy subjects alike often show a large variability, or even inability, of brain self-regulation for BCI control, known as BCI illiteracy. Although current co-adaptive algorithms are powerful for assistive BCIs, their inherent class switching clashes with the operant conditioning goal of restorative BCIs. Moreover, due to the treatment rationale, the classifier of restorative BCIs usually has a constrained feature space, thus limiting the possibility of classifier adaptation. In this context, we applied a Bayesian model of neurofeedback and reinforcement learning for different threshold selection strategies to study the impact of threshold adaptation of a linear classifier on optimizing restorative BCIs. For each feedback iteration, we first determined the thresholds that result in minimal action entropy and maximal instructional efficiency. We then used the resulting vector for the simulation of continuous threshold adaptation. We could thus show that threshold adaptation can improve reinforcement learning, particularly in cases of BCI illiteracy. Finally, on the basis of information-theory, we provided an explanation for the achieved benefits of adaptive threshold setting.

Long-latency reflexes account for limb biomechanics through several supraspinal pathways

Accurate control of body posture is enforced by a multitude of corrective actions operating over a range of time scales. The earliest correction is the short-latency reflex (SLR) which occurs between 20–45 ms following a sudden displacement of the limb and is generated entirely by spinal circuits. In contrast, voluntary reactions are generated by a highly distributed network but at a significantly longer delay after stimulus onset (greater than 100 ms). Between these two epochs is the long-latency reflex (LLR) (around 50–100 ms) which acts more rapidly than voluntary reactions but shares some supraspinal pathways and functional capabilities. In particular, the LLR accounts for the arm’s biomechanical properties rather than only responding to local muscle stretch like the SLR. This paper will review how the LLR accounts for the arm’s biomechanical properties and the supraspinal pathways supporting this ability. Relevant experimental paradigms include clinical studies, non-invasive brain stimulation, neural recordings in monkeys, and human behavioral studies. The sum of this effort indicates that primary motor cortex and reticular formation (RF) contribute to the LLR either by generating or scaling its structured response appropriate for the arm’s biomechanics whereas the cerebellum scales the magnitude of the feedback response. Additional putative pathways are discussed as well as potential research lines.

Fourier decomposition of spatial localization errors reveals an idiotropic dominance of an internal model of gravity

Given its conspicuous nature, gravity has been acknowledged by several research lines as a prime factor in structuring the spatial perception of one's environment. One such line of enquiry has focused on errors in spatial localization aimed at the vanishing location of moving objects - it has been systematically reported that humans mislocalize spatial positions forward, in the direction of motion (representational momentum) and downward in the direction of gravity (representational gravity). Moreover, spatial localization errors were found to evolve dynamically with time in a pattern congruent with an anticipated trajectory (representational trajectory). The present study attempts to ascertain the degree to which vestibular information plays a role in these phenomena. Human observers performed a spatial localization task while tilted to varying degrees and referring to the vanishing locations of targets moving along several directions. A Fourier decomposition of the obtained spatial localization errors revealed that although spatial errors were increased "downward" mainly along the body's longitudinal axis (idiotropic dominance), the degree of misalignment between the latter and physical gravity modulated the time course of the localization responses. This pattern is surmised to reflect increased uncertainty about the internal model when faced with conflicting cues regarding the perceived "downward" direction.

Fast feedback control involves two independent processes utilizing knowledge of limb dynamics

Corrective muscle responses occurring 50-100 ms after a mechanical perturbation are tailored to the mechanical features of the limb and its environment. For example, the evoked response by the shoulder's extensor muscle counters an imposed shoulder torque, rather than local shoulder motion, by integrating motion information from the shoulder and elbow appropriate for their dynamic interaction. Previous studies suggest that arm muscle activity within this epoch, alternately termed the R2/3 response, or long-latency reflex, reflects the summed result of two distinct components: an activity-dependent component which scales with the background muscle activity, and a task-dependent component which scales with the required vigor of the behavioral task. Here we examine how the knowledge of limb dynamics expressed during the shoulder muscle's R2/3 epoch is related to these two functional components. Subjects countered torque steps applied to their shoulder and/or elbow under different conditions of background torque and target size to recruit the activity-dependent and task-dependent component in varying degrees. Experiment 1 involved four torque perturbations, two levels of background torques and two target sizes; this design revealed that both background torque and target size impacted the shoulder's R2/3 activity, indicative of knowledge of limb dynamics. Experiment 2 involved two perturbation torques, five levels of background torque and two target sizes; this design demonstrated that the two factors had an independent impact on the R2/3 activity indicative of knowledge of limb dynamics. We conclude that a sophisticated feature of upper limb feedback control reflects the summation of two processes having a common capability.

Can representational trajectory reveal the nature of an internal model of gravity?

The memory for the vanishing location of a horizontally moving target is usually displaced forward in the direction of motion (representational momentum) and downward in the direction of gravity (representational gravity). Moreover, this downward displacement has been shown to increase with time (representational trajectory). However, the degree to which different kinematic events change the temporal profile of these displacements remains to be determined. The present article attempts to fill this gap. In the first experiment, we replicate the finding that representational momentum for downward-moving targets is bigger than for upward motions, showing, moreover, that it increases rapidly during the first 300 ms, stabilizing afterward. This temporal profile, but not the increased error for descending targets, is shown to be disrupted when eye movements are not allowed. In the second experiment, we show that the downward drift with time emerges even for static targets. Finally, in the third experiment, we report an increased error for upward-moving targets, as compared with downward movements, when the display is compatible with a downward ego-motion by including vection cues. Thus, the errors in the direction of gravity are compatible with the perceived event and do not merely reflect a retinotopic bias. Overall, these results provide further evidence for an internal model of gravity in the visual representational system.

Mechanism of augmented exercise hyperpnea in chronic heart failure and dead space loading

Patients with chronic heart failure (CHF) suffer increased alveolar VD/VT (dead-space-to-tidal-volume ratio), yet they demonstrate augmented pulmonary ventilation such that arterial [Formula: see text] ( [Formula: see text] ) remains remarkably normal from rest to moderate exercise. This paradoxical effect suggests that the control law governing exercise hyperpnea is not merely determined by metabolic CO2 production ( [Formula: see text] ) per se but is responsive to an apparent (real-feel) metabolic CO2 load ( [Formula: see text] ) that also incorporates the adverse effect of physiological VD/VT on pulmonary CO2 elimination. By contrast, healthy individuals subjected to dead space loading also experience augmented ventilation at rest and during exercise as with increased alveolar VD/VT in CHF, but the resultant response is hypercapnic instead of eucapnic, as with CO2 breathing. The ventilatory effects of dead space loading are therefore similar to those of increased alveolar VD/VT and CO2 breathing combined. These observations are consistent with the hypothesis that the increased series VD/VT in dead space loading adds to [Formula: see text] as with increased alveolar VD/VT in CHF, but this is through rebreathing of CO2 in dead space gas thus creating a virtual (illusory) airway CO2 load within each inspiration, as opposed to a true airway CO2 load during CO2 breathing that clogs the mechanism for CO2 elimination through pulmonary ventilation. Thus, the chemosensing mechanism at the respiratory controller may be responsive to putative drive signals mediated by within-breath [Formula: see text] oscillations independent of breath-to-breath fluctuations of the mean [Formula: see text] level. Skeletal muscle afferents feedback, while important for early-phase exercise cardioventilatory dynamics, appears inconsequential for late-phase exercise hyperpnea.

From sensorimotor inhibition to freudian repression: insights from psychosis applied to neurosis

First, three case studies are presented of psychotic patients having in common an inability to hold something down or out. In line with other theories on psychosis, we propose that a key change is at the efference copy system. Going back to Freud's mental apparatus, we propose that the messages of discharge of the motor neurons, mobilized to direct perception, also called "indications of reality," are equivalent to the modern efference copies. With this key, the reading of the cases is coherent with the psychodynamic understanding of psychosis, being a downplay of secondary processes, and consequently, a dominance of primary processes. Moreover, putting together the sensorimotor idea of a failure of efference copy-mediated inhibition with the psychoanalytic idea of a failing repression in psychosis, the hypothesis emerges that the attenuation enabled by the efference copy dynamics is, in some instances, the physiological instantiation of repression. Second, we applied this idea to the mental organization in neurosis. Indeed, the efference copy-mediated attenuation is thought to be the mechanism through which sustained activation of an intention, without reaching it - i.e., inhibition of an action - gives rise to mental imagery. Therefore, as inhibition is needed for any targeted action or for normal language understanding, acting in the world, or processing language, structurally induces mental imagery, constituting a subjective unconscious mental reality. Repression is a special instance of inhibition for emotionally threatening stimuli. These stimuli require stronger inhibition, leaving (the attenuation of) the motor intentions totally unanswered, in order to radically prevent execution which would lead to development of excess affect. This inhibition, then, yields a specific type of motor imagery, called "phantoms," which induce mental preoccupation, as well as symptoms which, especially through their form, refer to the repressed motor fragments.

A sensorimotor paradigm for Bayesian model selection

Sensorimotor control is thought to rely on predictive internal models in order to cope efficiently with uncertain environments. Recently, it has been shown that humans not only learn different internal models for different tasks, but that they also extract common structure between tasks. This raises the question of how the motor system selects between different structures or models, when each model can be associated with a range of different task-specific parameters. Here we design a sensorimotor task that requires subjects to compensate visuomotor shifts in a three-dimensional virtual reality setup, where one of the dimensions can be mapped to a model variable and the other dimension to the parameter variable. By introducing probe trials that are neutral in the parameter dimension, we can directly test for model selection. We found that model selection procedures based on Bayesian statistics provided a better explanation for subjects' choice behavior than simple non-probabilistic heuristics. Our experimental design lends itself to the general study of model selection in a sensorimotor context as it allows to separately query model and parameter variables from subjects.

Risk-sensitivity in Bayesian sensorimotor integration

Information processing in the nervous system during sensorimotor tasks with inherent uncertainty has been shown to be consistent with Bayesian integration. Bayes optimal decision-makers are, however, risk-neutral in the sense that they weigh all possibilities based on prior expectation and sensory evidence when they choose the action with highest expected value. In contrast, risk-sensitive decision-makers are sensitive to model uncertainty and bias their decision-making processes when they do inference over unobserved variables. In particular, they allow deviations from their probabilistic model in cases where this model makes imprecise predictions. Here we test for risk-sensitivity in a sensorimotor integration task where subjects exhibit Bayesian information integration when they infer the position of a target from noisy sensory feedback. When introducing a cost associated with subjects' response, we found that subjects exhibited a characteristic bias towards low cost responses when their uncertainty was high. This result is in accordance with risk-sensitive decision-making processes that allow for deviations from Bayes optimal decision-making in the face of uncertainty. Our results suggest that both Bayesian integration and risk-sensitivity are important factors to understand sensorimotor integration in a quantitative fashion.

Probabilistic models of state estimation predict visuomotor transformations during prism adaptation

This study aims to create a prediction model for state-space estimation and to elucidate the required information processing for identifying an external space in prism adaptation. Subjects were 57 healthy students. The subjects were instructed to rapidly perform reaching movements to one of the randomly illuminating light-emitting diode lights. Their movements were measured while wearing prism glasses and after removing that. We provided the following four conditions and control. In target condition, reaching error distance was visually fed back to the subject. In trajectory condition, the trajectory of fingertip movement could be seen, and the final reaching error was not fed back. Two restricted visual feedback conditions were prepared based on a different presentation timing (on-time and late-time conditions). We set up a linear parametric model and an estimation model using Kalman filtering. The goodness of fit between the estimated and observed values in each model was examined using Akaike information criterion (AIC). AIC would be one way to evaluate two models with different number of parameters. In the control, the value of AIC was 179.0 and 154.0 for the linear model and Kalman filtering, respectively, while these values were 173.6 and 161.1 for the target condition, 202.8 and 159.7 for the trajectory condition, 192.7 and 180.8 for the on-time condition, and 206.9 and 174.0 for the late-time condition. Kalman gain in the control was 0.07-0.26. Kalman gain relies on the prior estimation distribution when its value is below 0.5. Kalman gain in the trajectory and late-time conditions was 0.03-0.60 and 0.08-0.95, respectively. The Kalman filter, a state estimation model based on Bayesian theory, expressed the dynamics of the internal model under uncertain feedback information better than the linear parametric model. The probabilistic estimation model can clearly simulate state estimation according to the reliability of the visual feedback.

Speech production as state feedback control

Spoken language exists because of a remarkable neural process. Inside a speaker's brain, an intended message gives rise to neural signals activating the muscles of the vocal tract. The process is remarkable because these muscles are activated in just the right way that the vocal tract produces sounds a listener understands as the intended message. What is the best approach to understanding the neural substrate of this crucial motor control process? One of the key recent modeling developments in neuroscience has been the use of state feedback control (SFC) theory to explain the role of the CNS in motor control. SFC postulates that the CNS controls motor output by (1) estimating the current dynamic state of the thing (e.g., arm) being controlled, and (2) generating controls based on this estimated state. SFC has successfully predicted a great range of non-speech motor phenomena, but as yet has not received attention in the speech motor control community. Here, we review some of the key characteristics of speech motor control and what they say about the role of the CNS in the process. We then discuss prior efforts to model the role of CNS in speech motor control, and argue that these models have inherent limitations – limitations that are overcome by an SFC model of speech motor control which we describe. We conclude by discussing a plausible neural substrate of our model.

Online adaptation and over-trial learning in macaque visuomotor control

When faced with unpredictable environments, the human motor system has been shown to develop optimized adaptation strategies that allow for online adaptation during the control process. Such online adaptation is to be contrasted to slower over-trial learning that corresponds to a trial-by-trial update of the movement plan. Here we investigate the interplay of both processes, i.e., online adaptation and over-trial learning, in a visuomotor experiment performed by macaques. We show that simple non-adaptive control schemes fail to perform in this task, but that a previously suggested adaptive optimal feedback control model can explain the observed behavior. We also show that over-trial learning as seen in learning and aftereffect curves can be explained by learning in a radial basis function network. Our results suggest that both the process of over-trial learning and the process of online adaptation are crucial to understand visuomotor learning.

Optimal estimator models for spatial orientation and vestibular nystagmus

Mathematical models have played an important role in research on the vestibular system over the past century, from the torsion pendulum analogies of the semicircular canal to the optimal estimator "observer" models of multisensory interaction and adaptation. This short review is limited to our own contributions in bringing the technology of feedback control theory to bear on the understanding of human spatial orientation, eye movements, and nystagmus, both on Earth and in space. It points to the importance of the "internal model" concept for treatment of the manner in which the brain constantly makes predictions about future sensory feedback, adjusts the weightings of sensors according to their signal-to-noise ratios, and adapts control according to the motion environment, and availability of sensory cues.

Early switching between movement types: indication of predictive control?

In everyday life, we frequently alternate between performing discrete and rhythmic movements. When performing a periodic movement, two distinct movement types can be distinguished: highly harmonic vs. discrete-like. The harmonicity of the movement is used to classify it as one or the other. We asked: (1) whether the frequency at which a periodic movement is performed affects the harmonicity of the resultant movement; and (2) what underlies switching between these movement types. To answer these questions, we studied horizontal flexion/extension forearm movements in 13 young adults over a wide range of frequencies. Movements were performed either at a fixed frequency, or at gradually increasing or decreasing target frequencies. We found movement harmonicity to depend on the frequency of the movement. Furthermore, we found a reverse hysteresis behavior, where participants switched movement type in anticipation of the future-required frequency. These findings suggest that predictive control is employed in switching between movement types.

Schema generation in recurrent neural nets for intercepting a moving target

The grasping of a moving object requires the development of a motor strategy to anticipate the trajectory of the target and to compute an optimal course of interception. During the performance of perception-action cycles, a preprogrammed prototypical movement trajectory, a motor schema, may highly reduce the control load. Subjects were asked to hit a target that was moving along a circular path by means of a cursor. Randomized initial target positions and velocities were detected in the periphery of the eyes, resulting in a saccade toward the target. Even when the target disappeared, the eyes followed the target's anticipated course. The Gestalt of the trajectories was dependent on target velocity. The prediction capability of the motor schema was investigated by varying the visibility range of cursor and target. Motor schemata were determined to be of limited precision, and therefore visual feedback was continuously required to intercept the moving target. To intercept a target, the motor schema caused the hand to aim ahead and to adapt to the target trajectory. The control of cursor velocity determined the point of interception. From a modeling point of view, a neural network was developed that allowed the implementation of a motor schema interacting with feedback control in an iterative manner. The neural net of the Wilson type consists of an excitation-diffusion layer allowing the generation of a moving bubble. This activation bubble runs down an eye-centered motor schema and causes a planar arm model to move toward the target. A bubble provides local integration and straightening of the trajectory during repetitive moves. The schema adapts to task demands by learning and serves as forward controller. On the basis of these model considerations the principal problem of embedding motor schemata in generalized control strategies is discussed.

Compensations in response to real-time formant perturbations of different magnitudes

Previous auditory perturbation studies have demonstrated that talkers spontaneously compensate for real-time formant-shifts by altering formant production in a manner opposite to the perturbation. Here, two experiments were conducted to examine the effect of amplitude of perturbation on the compensatory behavior for the vowel /epsilon/. In the first experiment, 20 male talkers received three step-changes in acoustic feedback: F1 was increased by 50, 100, and 200 Hz, while F2 was simultaneously decreased by 75, 125, and 250 Hz. In the second experiment, 21 male talkers received acoustic feedback in which the shifts in F1 and F2 were incremented by +4 and -5 Hz on each utterance to a maximum of +350 and -450 Hz, respectively. In both experiments, talkers altered production of F1 and F2 in a manner opposite to that of the formant-shift perturbation. Compensation was approximately 25%-30% of the perturbation magnitude for shifts in F1 and F2 up to 200 and 250 Hz, respectively. As larger shifts were applied, compensation reached a plateau and then decreased. The similarity of results across experiments suggests that the compensatory response is dependent on the perturbation magnitude but not on the rate at which the perturbation is introduced.

Optimal interaction of respiratory and thermal regulation at rest and during exercise: role of a serotonin-gated spinoparabrachial thermoafferent pathway

Recent evidence indicates that the lateral parabrachial nucleus (LPBN) in dorsolateral pons is pivotal in mediating the feedback control of inspiratory drive by central chemoreceptor input and feedforward control of body temperature by cutaneous thermoreceptor input. The latter is subject to descending serotonergic inhibition which gates the transmission of ascending thermoafferent information from spinal dorsal horn to the LPBN. Here, a model is proposed which suggests that the LPBN may be important in balancing respiratory and thermal homeostasis, two conflicting goals that are heightened by environmental heat/cold stress or exercise where the effects of respiratory thermolysis become prominent. This optimization model of respiratory-thermoregulatory interaction is supported by a host of recent studies which demonstrate that animals with serotonin (5-HT) dysfunction at the spinal dorsal horn--due to 5-HT antagonism, genetic 5-HT defects or spinal cord injury--all display similar respiratory abnormalities that are consistent with hyperactivity of the spinoparabrachial thermoafferent (and pain) pathway.

Biologically-inspired humanoid postural control

This article presents a biologically-inspired framework for humanoid postural control. It complies with the main features of human postural control that are extracted from recent studies. In this article, the human body is abstracted as a single-inverted pendulum jointed with a foot that rests freely on a supporting surface. In particular, disturbances affecting posture are addressed and accommodated within the proposed framework. Among these are external forces and motion of support surface on which the body stands. The main components of this framework are: 1. A state-feedback mechanism for stabilizing the unstable dynamics of the body. 2. A tracking loop for robustly achieving desired voluntary orientations. 3. A feed-forward control primarily for improving the response to voluntary motions. 4. A stand-alone vestibular sensory fusion algorithm for estimating body orientation. 5. An external-disturbance estimator and a corresponding compensation for minimizing the effect of external disturbances. These components are interconnected in a way that qualifies this framework to modularly address the multi-segment body postural control problem. Although no postural stability measure is explicitly incorporated, experiments run on a special-purpose humanoid demonstrate the stability and the performance merits of the presented framework.

Alzheimer's disease and implicit memory

Specific neuropsychiatric disorders, such as Alzheimer's disease (AD) affect some forms of memory while leaving others relatively intact. In this review, we investigate particularities of the relationship between explicit and implicit memories in AD. It was found that implicit memory is preserved in AD, irrespective of the task used; in other words, there was not interference from explicit memory. In addition, it was verified that is possible through implicit memory compensatory strategies such as, activities of daily living (ADL) to compensate for the explicit memory deficits. In this sense, cognitive rehabilitation (CR) demonstrates reasonable results in the process of compensation of explicit memory deficits. Concluding, the decline in explicit memory suggests that both systems are functionally independent even if the other is compromised. We expect that when explicit memory system is not involved in competition with the implicit system, the final effect of learning is better, because all of the implicit memory capacity is engaged in learning and not in competition with the explicit system.