Sensory Feedback Signals Research Articles

Terra incognita of the uncontrolled manifold.

The review addresses the central concept of the uncontrolled manifold (UCM) hypothesis, which has become a major framework for analysis of performance-stabilizing motor synergies. The major goals are to summarize the status quo in the field and to ask new questions stimulating new studies. We focus on a few main questions: What is the UCM? What are the likely neural origins of the UCM? How is the UCM reflected in movement patterns? Are properties of the UCM similar in all directions? We contrast experience-based features of movements seen very soon after the movement initiation versus those based on on-line sensory feedback signals. Furthermore, we address a number of poorly explored issues such as the differences in characteristic times of processes within the UCM and orthogonal to the UCM space, the interplay between movement stability and optimality, the origin of preferred sharing patterns of performance variables across abundant sets of elements and of their intertrial variability, problems with the UCM-based analysis in different spaces, and likely neurophysiological mechanisms contributing to the UCM formation. In particular, we focus on the UCM in spaces of hypothetical neural control variables, which we associate with the reciprocal and coactivation commands to the effectors. Analysis of performance-stabilizing synergies within the UCM framework in abundant spaces of kinetic, kinematic and electromyographic variables at the selected level of analysis may be practically useful. However, mapping findings in such studies onto neural control mechanisms has been challenging.

Young adults use whole-body feedback and ankle proprioception to perceive small locomotor disturbances

To prevent a fall when a disturbance to walking is encountered requires sensory information about the disturbance to be sensed, integrated, and then used to generate an appropriate corrective motor response. Prior research has shown that feedback of whole-body motion (e.g., center-of-mass kinematics) drives this corrective response. Here, we hypothesized that young adults also use whole-body motion to perceive locomotor disturbances. 15 subjects performed a locomotor discrimination task in which the supporting leg was slowed during stance every 8–12 steps to emulate subtle slips. The perception threshold of these disturbances was determined using a psychometrics approach and found to be 0.08 ± 0.03 m/s. Whole-body feedback was examined through center-of-mass (CoM) kinematics and whole-body angular momentum (WBAM). Perturbation-induced deviations of CoM and WBAM were calculated in response to the two perturbation levels nearest each subject's perception threshold. Consistent with our hypothesis, we identified significantly higher perturbation induced deviations for perceived perturbations in sagittal-plane WBAM, anteroposterior CoM velocity, and vertical CoM velocity and acceleration. Because whole body motion is not sensed directly but instead arises from the integration of various sensory feedback signals, we also explored local sensory feedback contributions to the perception of locomotor disturbances. Local sensory feedback was estimated through kinematic analogues of vision (head angle), vestibular (head angular velocity), proprioception (i.e., sagittal hip, knee, and ankle angles), and somatosensation (i.e., anterior-posterior & mediolateral center-of-pressure, COP). We identified significantly higher perturbation induced deviations for perceived perturbations in sagittal-plane ankle angle. These results provide evidence for both whole-body feedback and ankle proprioception as important for the perception of subtle slip-like locomotor disturbances in young adults. Our interpretation is ankle proprioception is a dominant contributor to estimates of whole-body motion to perceive locomotor disturbances.

Variational and phase response analysis for limit cycles with hard boundaries, with applications to neuromechanical control problems

Motor systems show an overall robustness, but because they are highly nonlinear, understanding how they achieve robustness is difficult. In many rhythmic systems, robustness against perturbations involves response of both the shape and the timing of the trajectory. This makes the study of robustness even more challenging. To understand how a motor system produces robust behaviors in a variable environment, we consider a neuromechanical model of motor patterns in the feeding apparatus of the marine mollusk Aplysia californica (Shaw et al. in J Comput Neurosci 38(1):25–51, 2015; Lyttle et al. in Biol Cybern 111(1):25–47, 2017). We established in (Wang et al. in SIAM J Appl Dyn Syst 20(2):701–744, 2021. https://doi.org/10.1137/20M1344974) the tools for studying combined shape and timing responses of limit cycle systems under sustained perturbations and here apply them to study robustness of the neuromechanical model against increased mechanical load during swallowing. Interestingly, we discover that nonlinear biomechanical properties confer resilience by immediately increasing resistance to applied loads. In contrast, the effect of changed sensory feedback signal is significantly delayed by the firing rates’ hard boundary properties. Our analysis suggests that sensory feedback contributes to robustness in swallowing primarily by shifting the timing of neural activation involved in the power stroke of the motor cycle (retraction). This effect enables the system to generate stronger retractor muscle forces to compensate for the increased load, and hence achieve strong robustness. The approaches that we are applying to understanding a neuromechanical model in Aplysia, and the results that we have obtained, are likely to provide insights into the function of other motor systems that encounter changing mechanical loads and hard boundaries, both due to mechanical and neuronal firing properties.

Vibration transmission in bone-anchored prosthesis under vertical load. Cadaver study.

Sensory feedback between a device and its user helps to improve the effectiveness of control and training processes. These improvements involve corrective actions and the accumulation of experience to accelerate patient training in device control. Vibrations from rehabilitation devices are used as sensory feedback signals for improving rehabilitation outcomes. Variations in the bone-anchored prosthesis frequency responses and vibration transmissibility under compressive loading were studied. Five laboratory samples consisting of the above-knee prosthesis, custom-made implant, and cadaver bone were first axially compressed using universal testing machine; preloaded construction vibrations were generated with a shaker to imitate external forces acting on these samples. The oscillations at the sample surface control points were tested with a laser vibrometer. For different values of axial loading, the frequency responses of the samples and indexes of vibration attenuation were obtained to examine the correlations between vibration transmissibility in the samples and axial loads. Increase in axial loading caused an increase in the resonance frequency and a simultaneous decrease in vibratory displacement within the sample. At low frequencies (40-80 Hz), increasing the axial load degraded transmissibility; at medium and high frequencies, transmissibility changes were unstable on increasing the axial load. The osseoperception phenomenon is because of perceived prosthesis vibration analysis by the user. Vibration transmissibility of a prosthesis sample depends on the axial compression and frequency of excitation. Decreasing the vibration amplitude while increasing axial load is the primary reason for reduced perception of vibration.

Transforming representations of movement from body- to world-centric space.

When an animal moves through the world, its brain receives a stream of information about the body's translational velocity from motor commands and sensory feedback signals. These incoming signals are referenced to the body, but ultimately, they must be transformed into world-centric coordinates for navigation1,2. Here we show that this computation occurs in the fan-shaped body in the brain of Drosophila melanogaster. We identify two cell types, PFNd and PFNv3-5, that conjunctively encode translational velocity and heading as a fly walks. In these cells, velocity signals are acquired from locomotor brain regions6 and are multiplied with heading signals from the compass system. PFNd neurons prefer forward-ipsilateral movement, whereas PFNv neurons prefer backward-contralateral movement, and perturbing PFNd neurons disrupts idiothetic path integration in walking flies7. Downstream, PFNd and PFNv neurons converge onto hΔB neurons, with a connectivity pattern that pools together heading and translation direction combinations corresponding to the same movement in world-centric space. This network motif effectively performs a rotation of the brain's representation of body-centric translational velocity according to the current heading direction. Consistent with our predictions, we observe that hΔB neurons form a representation of translationalvelocity in world-centric coordinates. By integrating this representation over time, it should be possible for the brain to form a working memory of the path travelled through the environment8-10.

Rotational dynamics in motor cortex are consistent with a feedback controller.

Recent studies have identified rotational dynamics in motor cortex (MC), which many assume arise from intrinsic connections in MC. However, behavioral and neurophysiological studies suggest that MC behaves like a feedback controller where continuous sensory feedback and interactions with other brain areas contribute substantially to MC processing. We investigated these apparently conflicting theories by building recurrent neural networks that controlled a model arm and received sensory feedback from the limb. Networks were trained to counteract perturbations to the limb and to reach toward spatial targets. Network activities and sensory feedback signals to the network exhibited rotational structure even when the recurrent connections were removed. Furthermore, neural recordings in monkeys performing similar tasks also exhibited rotational structure not only in MC but also in somatosensory cortex. Our results argue that rotational structure may also reflect dynamics throughout the voluntary motor system involved in online control of motor actions.

Speech rate association with cerebellar white-matter diffusivity in adults with persistent developmental stuttering.

Speech rate is a basic characteristic of language production, which affects the speaker's intelligibility and communication efficiency. Various speech disorders, including persistent developmental stuttering, present altered speech rate. Specifically, adults who stutter (AWS) typically exhibit a slower speech rate compared to fluent speakers. Evidence from imaging studies suggests that the cerebellum contributes to the paced production of speech. People who stutter show structural and functional abnormalities in the cerebellum. However, the involvement of the cerebellar pathways in controlling speech rate remains unexplored. Here, we assess the association of the cerebellar peduncles with speech rate in AWS and control speakers. Diffusion MRI and speech-rate data were collected in 42 participants (23 AWS, 19 controls). We used deterministic tractography with Automatic Fiber segmentation and Quantification (AFQ) to identify the superior, middle, and inferior cerebellar peduncles (SCP, MCP, ICP) bilaterally, and quantified fractional anisotropy (FA) and mean diffusivity (MD) along each tract. No significant differences were observed between AWS and controls in the diffusivity values of the cerebellar peduncles. However, AWS demonstrated a significant negative association between speech rate and FA within the left ICP, a major cerebellar pathway that transmits sensory feedback signals from the olivary nucleus into the cerebellum. The involvement of the ICP in controlling speech production in AWS is compatible with the view that stuttering stems from hyperactive speech monitoring, where even minor deviations from the speech plan are considered as errors. In conclusion, our findings suggest a plausible neural mechanism for speech rate reduction observed in AWS.

Flexible Spiking CPGs for Online Manipulation During Hexapod Walking.

Neural signals for locomotion are influenced both by the neural network architecture and sensory inputs coordinating and adapting the gait to the environment. Adaptation relies on the ability to change amplitude, frequency, and phase of the signals within the sensorimotor loop in response to external stimuli. However, in order to experiment with closed-loop control, we first need a better understanding of the dynamics of the system and how adaptation works. Based on insights from biology, we developed a spiking neural network capable of continuously changing amplitude, frequency, and phase online. The resulting network is deployed on a hexapod robot in order to observe the walking behavior. The morphology and parameters of the network results in a tripod gait, demonstrating that a design without afferent feedback is sufficient to maintain a stable gait. This is comparable to results from biology showing that deafferented samples exhibit a tripod-like gait and adds to the evidence for a meaningful role of network topology in locomotion. Further, this work enables research into the role of sensory feedback and high-level control signals in the adaptation of gait types. A better understanding of the neural control of locomotion relates back to biology where it can provide evidence for theories that are currently not testable on live insects.

The effect of sensory feedback topology on the entrainment of a neural oscillator with a compliant foil for swimming systems

The sensorimotor system of fish endows them with remarkable swimming performance that is unmatched by current underwater robotic vehicles. To close the gap between the capabilities of fish and the capabilities of underwater vehicles engineers are investigating how fish swim. In particular, engineers are exploring the sensorimotor systems of fish that control the motion of fins. It is generally accepted that specialized neural circuits (known as central pattern generators) within the sensorimotor system produce the periodic drive signal that is used to control the motion of fins. An important aspect of these circuits is that their output signal can be modified by sensory feedback. Specifically, the way in which sensory feedback signals are applied to a CPG (i.e. the sensory feedback topology) affects the CPG’s entrainment characteristics. This has been shown in simulation but has not been investigated in a robot interacting in the real-world. Furthermore, CPG-based control has only limitedly been applied to fish like robots and many questions remain as to how it should be applied to these types of systems. In this work we examine the effect of sensory feedback topology on the entrainment characteristics of a CPG-based neural oscillator driving three different foils swimming in flow. Additionally, we investigate how sensory feedback should be acquired from a foil and applied to a neural oscillator to promote beneficial swimming characteristics.

The somatosensory cortex receives information about motor output.

During voluntary movement, the somatosensory system not only passively receives signals from the external world but also actively processes them via interactions with the motor system. However, it is still unclear how and what information the somatosensory system receives during movement. Using simultaneous recordings of activities of the primary somatosensory cortex (S1), the motor cortex (MCx), and an ensemble of afferent neurons in behaving monkeys combined with a decoding algorithm, we reveal the temporal profiles of signal integration in S1. While S1 activity before movement initiation is accounted for by MCx activity alone, activity during movement is accounted for by both MCx and afferent activities. Furthermore, premovement S1 activity encodes information about imminent activity of forelimb muscles slightly after MCx does. Thus, S1 receives information about motor output before the arrival of sensory feedback signals, suggesting that S1 executes online processing of somatosensory signals via interactions with the anticipatory information.

Contribution of the Cerebellum to Predictive Motor Control and Its Evaluation in Ataxic Patients.

Goal-directed movements are predictive and multimodal in nature, especially for moving targets. For instance, during a reaching movement for a moving target, humans need to predict both motion of the target and movement of the limb. Recent computational studies show that the cerebellum predicts current and future states of the body and its environment using internal forward models. Sensory feedback signals from the periphery have delays in reaching the central nervous system, ranging between tens to hundreds of milliseconds. It is well known in engineering that feedback control based on time-delayed inputs can result in oscillatory and often unstable movements. In contrast, the brain predicts a current state from a previous state using forward models. This predictive mechanism most likely underpins stable and dexterous control of reaching movements. Although the cerebro-cerebellum has long been suggested as loci of various forward models, few methods are available to evaluate accuracy of the forward models in patients with cerebellar ataxia. Recently, we developed a non-invasive method to analyze receipt of motor commands in terms of movement kinematics for the wrist joint (Br/Kr ratio). In the present study, we have identified two components (F1 and F2) of the smooth pursuit movement. We found that the two components were in different control modes with different Br/Kr ratios. The major F1 component in a lower frequency range encodes both velocity and position of the moving target (higher Br/Kr ratio) to synchronize movement of the wrist joint with motion of the target in a predictive manner. The minor F2 component in a higher frequency range is biased to position control in order to generate intermittent small step-wise movements. In cerebellar patients, the F1 component shows a selective decrease in the Br/Kr ratio, which is correlated with decrease in accuracy of the pursuit movement. We conclude that the Br/Kr ratio of the F1 component provides a unique parameter to evaluate accuracy of the predictive control. We also discuss the pathophysiological and clinical implications for clinical ataxiology.

Applications of Robotics in Welding

An Industrial robot is reprogrammable, automatically controlled, multifunctional manipulator programmable in three or more axes, which may be either fixed in place or mobile for use in industrial automation applications. Technical innovations in robotic welding has facilitated manual welding processes in sever working conditions with enormous heat and fumes to be replaced with robotic welding. The robotic welding has greater capability to control robot motion, welding parameters and enhanced wrong detection and wrong correction. Major difficulties in robotic welding are joint edge inspection, weld penetration control, seam tracking of joints, and width or profile measurement of a joint. These problems can be more easily solved by use of sensory feedback signals from weld joint. Robotic welding system has intelligent and effective control system that can track the joint, monitor the joint in process and accounts for variation in joint location. Sensors play an important role in robotic welding systems with adaptive and intelligent control system features that can track the joint, account for variation in joint location and geometry monitor in-process quality of the weld. In this paper various aspects of robotic welding, robot programming, and problems associated with robot welding are undertaken.

Linear feature projection-based real-time decoding of limb state from dorsal root ganglion recordings.

Proprioceptive afferent activities recorded by a multichannel microelectrode have been used to decode limb movements to provide sensory feedback signals for closed-loop control in a functional electrical stimulation (FES) system. However, analyzing the high dimensionality of neural activity is one of the major challenges in real-time applications. This paper proposes a linear feature projection method for the real-time decoding of ankle and knee joint angles. Single-unit activity was extracted as a feature vector from proprioceptive afferent signals that were recorded from the L7 dorsal root ganglion during passive movements of ankle and knee joints. The dimensionality of this feature vector was then reduced using a linear feature projection composed of projection pursuit and negentropy maximization (PP/NEM). Finally, a time-delayed Kalman filter was used to estimate the ankle and knee joint angles. The PP/NEM approach had a better decoding performance than did other feature projection methods, and all processes were completed within the real-time constraints. These results suggested that the proposed method could be a useful decoding method to provide real-time feedback signals in closed-loop FES systems.

Linear Feature Projection-Based Sensory Event Detection from the Multiunit Activity of Dorsal Root Ganglion Recordings.

Afferent signals recorded from the dorsal root ganglion can be used to extract sensory information to provide feedback signals in a functional electrical stimulation (FES) system. The goal of this study was to propose an efficient feature projection method for detecting sensory events from multiunit activity-based feature vectors of tactile afferent activity. Tactile afferent signals were recorded from the L4 dorsal root ganglion using a multichannel microelectrode for three types of sensory events generated by mechanical stimulation on the rat hind paw. The multiunit spikes (MUSs) were extracted as multiunit activity-based feature vectors and projected using a linear feature projection method which consisted of projection pursuit and negentropy maximization (PP/NEM). Finally, a multilayer perceptron classifier was used to detect sensory events. The proposed method showed a detection accuracy superior to those of other linear and nonlinear feature projection methods and all processes were completed within real-time constraints. Results suggest that the proposed method could be useful to detect sensory events in real time. We have demonstrated the methodology for an efficient feature projection method to detect real-time sensory events from the multiunit activity of dorsal root ganglion recordings. The proposed method could be applied to provide real-time sensory feedback signals in closed-loop FES systems.

Modulation–demodulation hypothesis of periodic breathing in human respiration

Periodic breathing (PB) is a diseased condition of the cardiorespiratory system, and mathematically it is modelled as an oscillation. Modeling approaches replicate periodic oscillation in the minute ventilation due to a higher than normal gain of the feedback signals from the chemoreceptors coupled with a longer than normal latency in feedback, and do not consider the waxing–waning pattern of the oronasal airflow. In this work, a noted regulation model is extended by integrating respiratory mechanics and respiratory central pattern generator (rCPG) model, using modulation–demodulation1 hypothesis. This is a top-down modeling approach, and it is assumed that the sensory feedback signal from the chemoreceptors modulates the output of the rCPG model. It is also assumed that the brainstem network is responsible for the demodulation process. The respiratory mechanics is modeled as a multi-input multi-output (MIMO) system, where modulated and demodulated neural signals are applied as input and the minute ventilation and the oronasal airflow are specified as output. The minute ventilation signal drives the regulation model, completing the feedback loop. The proposed model is validated by comparing the model output with the clinical data. Using the modulation–demodulation hypothesis, a respiratory mechanics model is formulated in the form of a linear state-space model, which can be useful for providing assisted ventilation in clinical conditions.

A unified internal model theory to resolve the paradox of active versus passive self-motion sensation.

Brainstem and cerebellar neurons implement an internal model to accurately estimate self-motion during externally generated ('passive') movements. However, these neurons show reduced responses during self-generated ('active') movements, indicating that predicted sensory consequences of motor commands cancel sensory signals. Remarkably, the computational processes underlying sensory prediction during active motion and their relationship to internal model computations during passive movements remain unknown. We construct a Kalman filter that incorporates motor commands into a previously established model of optimal passive self-motion estimation. The simulated sensory error and feedback signals match experimentally measured neuronal responses during active and passive head and trunk rotations and translations. We conclude that a single sensory internal model can combine motor commands with vestibular and proprioceptive signals optimally. Thus, although neurons carrying sensory prediction error or feedback signals show attenuated modulation, the sensory cues and internal model are both engaged and critically important for accurate self-motion estimation during active head movements.

Emergent central pattern generator behavior in chemical coupled two-compartment models with time delay

This paper proposes that modified two-compartment Pinsky–Rinzel (PR) neural model can be used to develop the simple form of central pattern generator (CPG). The CPG is called as ‘half-central oscillator’, which constructed by two inhibitory chemical coupled PR neurons with time delay. Some key properties of PR neural model related to CPG are studied and proved to meet the requirements of CPG. Using the simple CPG network, we first study the relationship between rhythmical output and key factors, including ambient noise, sensory feedback signals, morphological character of single neuron as well as the coupling delay time. We demonstrate that, appropriate intensity noise can enhance synchronization between two coupled neurons. Different output rhythm of CPG network can be entrained by sensory feedback signals. We also show that the morphology of single neuron has strong effect on the output rhythm. The phase synchronization indexes decrease with the increase of morphology parameter’s difference. Through adjusting coupled delay time, we can get absolutely phase synchronization and antiphase state of CPG. Those results of simulation show the feasibility of PR neural model as a valid CPG as well as the emergent behaviors of the particularly CPG.

Multiunit Activity-Based Real-Time Limb-State Estimation from Dorsal Root Ganglion Recordings

Proprioceptive afferent activities could be useful for providing sensory feedback signals for closed-loop control during functional electrical stimulation (FES). However, most previous studies have used the single-unit activity of individual neurons to extract sensory information from proprioceptive afferents. This study proposes a new decoding method to estimate ankle and knee joint angles using multiunit activity data. Proprioceptive afferent signals were recorded from a dorsal root ganglion with a single-shank microelectrode during passive movements of the ankle and knee joints, and joint angles were measured as kinematic data. The mean absolute value (MAV) was extracted from the multiunit activity data, and a dynamically driven recurrent neural network (DDRNN) was used to estimate ankle and knee joint angles. The multiunit activity-based MAV feature was sufficiently informative to estimate limb states, and the DDRNN showed a better decoding performance than conventional linear estimators. In addition, processing time delay satisfied real-time constraints. These results demonstrated that the proposed method could be applicable for providing real-time sensory feedback signals in closed-loop FES systems.

Inaccurate Saccades and Enhanced Vestibulo-Ocular Reflex Suppression during Combined Eye-Head Movements in Patients with Chronic Neck Pain: Possible Implications for Cervical Vertigo.

The primate ocular motor system is designed to acquire peripheral targets of interest by coordinating visual, vestibular, and neck muscle activation signals. The vestibulo-ocular reflex (VOR) is greatly reduced at the onset of large eye-head (gaze) saccades and resumes before the end of the saccades to stabilize eye-in-orbit and ensure accurate target acquisition. Previous studies have relied on manipulating head movements in normal individuals to study VOR suppression and gaze kinematics. We sought to determine if reduced head-on-trunk movement alters VOR suppression and gaze accuracy similar to experiments involving normal subjects and if intentionally increasing head and neck movement affects these dynamics. We measured head and gaze movements using magnetic search coil oculography in eight patients with cervical soft tissue disorders and seven healthy subjects. All participants made horizontal head-free saccades to acquire a laser dot target that stepped pseudorandomly 30-65° to either side of orbital mid-position, first using typical head and eye movements and again after being instructed to increase head amplitudes as much as possible. Compared to healthy subjects, patients made smaller head movements that contributed only 6% to total gaze saccade amplitudes. Head movements were also slowed, prolonged, and delayed. VOR suppression was increased and prolonged. Gaze saccades were inaccurate and delayed with long durations and decreased peak velocities. In patients with chronic neck pain, the internal commands issued for combined eye-head movements have large enough amplitudes to create accurate gaze saccades; however, because of increased neck stiffness and viscosity, the head movements produced are smaller, slower, longer, and more delayed than they should be. VOR suppression is disproportionate to the size of the actual gaze saccades because sensory feedback signals from neck proprioceptors are non-veridical, likely due to prolonged coactivation of cervical muscles. The outcome of these changes in eye-head kinematics is head-on-trunk stability at the expense of gaze accuracy. In the absence of vestibular loss, the practical consequences may be dizziness (cervical vertigo) in the short term and imbalance and falls in the long term.

Flexible Electronics: An Epidermal Stimulation and Sensing Platform for Sensorimotor Prosthetic Control, Management of Lower Back Exertion, and Electrical Muscle Activation (Adv. Mater. 22/2016).

The design of an ultrathin, conformal electronic device that integrates electrotactile stimulation with electromyography, temperature, and strain sensing in a single, simple platform is reported by J. A. Rogers and co-workers on page 4462. Demonstrated application possibilities include prosthetic control with sensory feedback, monitors, and stimulation signals related to lower back exertion, and electrical muscle stimulation with feedback control.