Neural Representations for Sensory-Motor Control, II: Learning a Head-Centered Visuomotor Representation of 3-D Target Position

A neural network model is described for how an invariant head-centered representation of 3-D target position can be autonomously learned by the brain in real time. Once learned, such a target representation may be used to control both eye and limb movements. The target representation is derived from the positions of both eyes in the head, and the locations which the target activates on the retinas of both eyes. A Vector Associative Map, or YAM, learns the many-to-one transformation from multiple combinations of eye-and-retinal position to invariant 3-D target position. Eye position is derived from outflow movement signals to the eye muscles. Two successive stages of opponent processing convert these corollary discharges into a. head-centered representation that closely approximates the azimuth, elevation, and vergence of the eyes' gaze position with respect to a cyclopean origin located between the eyes. YAM learning combines this cyclopean representation of present gaze position with binocular retinal information about target position into an invariant representation of 3-D target position with respect to the head. YAM learning can use a teaching vector that is externally derived from the positions of the eyes when they foveate the target. A YAM can also autonomously discover and learn the invariant representation, without an explicit teacher, by generating internal error signals from environmental fluctuations in which these invariant properties are implicit. YAM error signals are computed by Difference Vectors, or DVs, that are zeroed by the YAM learning process. YAMs may be organized into YAM Cascades for learning and performing both sensory-to-spatial maps and spatial-to-motor maps. These multiple uses clarify why DV-type properties are computed by cells in the parietal, frontal, and motor cortices of many mammals. YAMs are modulated by gating signals that express different aspects of the will-to-act. These signals transform a single invariant representation into movements of different speed (GO signal) and size (GRO signal), and thereby enable YAM controllers to match a planned action sequence to variable environmental conditions.National Science Foundation (IRI-87-16960, IRI-90-24877); Office of Naval Research (N00014-92-J-1309

Adaptive Neural Networks for Control of Movement Trajectories Invariant under Speed and Force Rescaling

This article describes two neural network modules that form part of an emerging theory of how adaptive control of goal-directed sensory-motor skills is achieved by humans and other animals. The Vector-Integration-To-Endpoint (VITE) model suggests how synchronous multi-joint trajectories are generated and performed at variable speeds. The Factorization-of-LEngth-and-TEnsion (FLETE) model suggests how outflow movement commands from a VITE model may be performed at variable force levels without a loss of positional accuracy. The invariance of positional control under speed and force rescaling sheds new light upon a familiar strategy of motor skill development: Skill learning begins with performance at low speed and low limb compliance and proceeds to higher speeds and compliances. The VITE model helps to explain many neural and behavioral data about trajectory formation, including data about neural coding within the posterior parietal cortex, motor cortex, and globus pallidus, and behavioral properties such as Woodworth's Law, Fitts Law, peak acceleration as a function of movement amplitude and duration, isotonic arm movement properties before and after arm-deafferentation, central error correction properties of isometric contractions, motor priming without overt action, velocity amplification during target switching, velocity profile invariance across different movement distances, changes in velocity profile asymmetry across different movement durations, staggered onset times for controlling linear trajectories with synchronous offset times, changes in the ratio of maximum to average velocity during discrete versus serial movements, and shared properties of arm and speech articulator movements. The FLETE model provides new insights into how spina-muscular circuits process variable forces without a loss of positional control. These results explicate the size principle of motor neuron recruitment, descending co-contractive compliance signals, Renshaw cells, Ia interneurons, fast automatic reactive control by ascending feedback from muscle spindles, slow adaptive predictive control via cerebellar learning using muscle spindle error signals to train adaptive movement gains, fractured somatotopy in the opponent organization of cerebellar learning, adaptive compensation for variable moment-arms, and force feedback from Golgi tendon organs. More generally, the models provide a computational rationale for the use of nonspecific control signals in volitional control, or "acts of will", and of efference copies and opponent processing in both reactive and adaptive motor control tasks.National Science Foundation (IRI-87-16960); Air Force Office of Scientific Research (90-0128, 90-0175

Cortical Models for Movement Control

Defense Advanced Research Projects Agency and Office of Naval Research (N0014-95-l-0409)

Inertial Load Compensation by a Model Spinal Circuit During Single Joint Movement

Office of Naval Research (N00014-92-J-1309); CONACYT (Mexico) (63462

How Spinal Neural Networks Reduce Discrepancies between Motor Intention and Motor Realization

This paper attempts a rational, step-by-step reconstruction of many aspects of the mammalian neural circuitry known to be involved in the spinal cord's regulation of opposing muscles acting on skeletal segments. Mathematical analyses and local circuit simulations based on neural membrane equations are used to clarify the behavioral function of five fundamental cell types, their complex connectivities, and their physiological actions. These cell types are: α-MNs, γ-MNs, IaINs, IbINs, and Renshaw cells. It is shown that many of the complexities of spinal circuitry are necessary to ensure near invariant realization of motor intentions when descending signals of two basic types independently vary over large ranges of magnitude and rate of change. Because these two types of signal afford independent control, or Factorization, of muscle LEngth and muscle TEnsion, our construction was named the FLETE model (Bullock and Grossberg, 1988b, 1989). The present paper significantly extends the range of experimental data encompassed by this evolving model.National Science Foundation (IRI-87-16960, IRI-90-24877); Instituto Tecnológico y de Estudios Superiores de Monterre

Use of neural oscillators triggered by loading and hip angles to study the activation patterns at the ankle during walking in humans

Spinale Mustergeneratoren (SPG) sind neuronale Netze ohne supraspinales Input, die zyklische Bewegungen steuern. Wir wollten untersuchen, ob sich SPG an die variablen Anforderungen verschiedener Geschwindigkeiten, Störungen und ungewöhnlicher Koordinationsmuster beim Gehen anpassen können. Das SPG-Modell ist ein Oszillator aus zwei Neuronen; eines aktiviert einen Dorsalextensor und das andere einen Plantarflexor. Das Output des Oszillators repräsentiert die jeweilige Muskelaktivierung. Die Modellparameter wurden angepasst, um eine optimale Passung zwischen simulierten und gemessenen elektromyographischen Daten von gesunden Probanden zu erzielen. Eine hohe Korrelation zwischen simulierten und gemessenen Muskelaktivierungen beim normalen Gehen wies darauf hin, dass spinale Kontrolle in Modellen vom Gehen beim Menschen berücksichtigt sollte werden. Unsere experimentellen Ergebnisse zeigen, dass der Soleus vom Rückenmark kontrolliert werden könnte, aber nicht der Tibialis anterior

Identifying Plant and Feedback in Human Posture Control

Human upright bipedal stance is a classic example of a control system consisting of a plant (i.e., the physical body and its actuators) and feedback (i.e., neural control) operating continuously in a closed loop. Determining the mechanistic basis of behavior in a closed loop control system is problematic because experimental manipulations or deficits due to trauma/injury influence all parts of the loop. Moreover, experimental techniques to open the loop (e.g., isolate the plant) are not viable because bipedal upright stance is not possible without feedback. The goal of the proposed study is to use a technique called closed loop system identification (CLSI) to investigate properties of the plant and feedback separately. Human upright stance has typically been approximated as a single-joint inverted pendulum, simplifying not only the control of a multi-linked body but also how sensory information is processed relative to body dynamics. However, a recent study showed that a single-joint approximation is inadequate. Trunk and leg segments are in-phase at frequencies below 1 Hz of body sway and simultaneously anti-phase at frequencies above 1 Hz during quiet stance. My dissertation studies have investigated the coordination between the leg and trunk segments and how sensory information is processed relative to that coordination. For example, additional sensory information provided through visual or light touch information led to a change of the in-phase pattern but not the anti-phase pattern, indicating that the anti-phase pattern may not be neurally controlled, but more a function of biomechanical properties of a two-segment body. In a subsequent study, I probed whether an internal model of the body processes visual information relative to a single or double-linked body. The results suggested a simple control strategy that processes sensory information relative to a single-joint internal model providing further evidence that the anti-phase pattern is biomechanically driven. These studies suggest potential mechanisms but cannot rule out alternative hypotheses because the source of behavioral changes can be attributed to properties of the plant and/or feedback. Here I adopt the CLSI approach using perturbations to probe separate processes within the postural control loop. Mechanical perturbations introduce sway as an input to the feedback, which in turn generates muscle activity as an output. Visual perturbations elicit muscle activity (a motor command) as an input to the plant, which then triggers body sway as an output. Mappings of muscle activity to body sway and body sway to muscle activity are used to identify properties of the plant and feedback, respectively. The results suggest that feedback compensates for the low-pass properties of the plant, except at higher frequencies. An optimal control model minimizing the amount of muscle activation suggests that the mechanism underlying this lack of compensation may be due to an uncompensated time delay. These techniques have the potential for more precise identification of the source of deficits in the postural control loop, leading to improved rehabilitation techniques and treatment of balance deficits, which currently contributes to 40% of nursing home admissions and costs the US health care system over $20B per year

Effectiveness of Mechanical Horse-Riding Simulators on Postural Balance in Neurological Rehabilitation: Systematic Review and Meta-Analysis

Mechanical horse-riding simulators consist of a device that mimics the movement of a real horse, generating between 50 and 100 three-dimensional physical movements (forward and back, left and right, up and down). The main objective of this study is to analyze the effectiveness of mechanical horse-riding simulators to improve postural balance in subjects with neurological disorders. The search was conducted during January-March 2019 in PubMed, Physiotherapy Evidence Database (PEDro), Cochrane, Web of Science, CINAHL, and Scopus. The methodological quality of the studies was evaluated through the PEDro scale. A total of seven articles were included in this systematic review, of which four contributed information to the meta-analysis. Statistical analysis showed favorable results for balance in stroke patients, measured by the Berg Balance Scale (standardized mean difference (SMD) = 3.24; 95%; confidence interval (CI): 1.66-4.83). Not conclusive results were found in sitting postural balance, measured using the Gross Motor Function Measure-66 (GMFM-66) Sitting Dimension, in patients with cerebral palsy. Most studies have shown beneficial effects on postural balance compared with conventional physical therapy. However, due to the limited number of articles and their low methodological quality, no solid conclusions can be drawn about the effectiveness of this therapy

A MECHANISTIC APPROACH TO POSTURAL DEVELOPMENT IN CHILDREN

Upright standing is intrinsically unstable and requires active control. The central nervous system's feedback process is the active control that integrates multi-sensory information to generate appropriate motor commands to control the plant (the body with its musculotendon actuators). Maintaining standing balance is not trivial for a developing child because the feedback and the plant are both developing and the sensory inputs used for feedback are continually changing. Knowledge gaps exist in characterizing the critical ability of adaptive multi-sensory reweighting for standing balance control in children. Furthermore, the separate contributions of the plant and feedback and their relationship are poorly understood in children, especially when considering that the body is multi-jointed and feedback is multi-sensory. The purposes of this dissertation are to use a mechanistic approach to study multi-sensory abilities of typically developing (TD) children and children with Developmental Coordination Disorder (DCD). The specific aims are: 1) to characterize postural control under different multi-sensory conditions in TD children and children with DCD; 2) to characterize the development of adaptive multi-sensory reweighting in TD children and children with DCD; and, 3) to identify the plant and feedback for postural control in TD children and how they change in response to visual reweighting. In the first experiment (Aim 1), TD children, adults, and 7-year-old children with DCD are tested under four sensory conditions (no touch/no vision, with touch/no vision, no touch/with vision, and with touch/with vision). We found that touch robustly attenuated standing sway in all age groups. Children with DCD used touch less effectively than their TD peers and they also benefited from using vision to reduce sway. In the second experiment (Aim 2), TD children (4- to 10-year-old) and children with DCD (6- to 11-year-old) were presented with simultaneous small-amplitude touch bar and visual scene movement at 0.28 and 0.2 Hz, respectively, within five conditions that independently varied the amplitude of the stimuli. We found that TD children can reweight to both touch and vision from 4 years on and the amount of reweighting increased with age. However, multi-sensory fusion (i.e., inter-modal reweighting) was only observed in the older children. Children with DCD reweight to both touch and vision at a later age (10.8 years) than their TD peers. Even older children with DCD do not show advanced multisensory fusion. Two signature deficits of multisensory reweighting are a weak vision reweighting and a general phase lag to both sensory modalities. The final aim involves closed-loop system identification of the plant and feedback using electromyography (EMG) and kinematic responses to a high- or low-amplitude visual perturbation and two mechanical perturbations in children ages six and ten years and adults. We found that the plant is different between children and adults. Children demonstrate a smaller phase difference between trunk and leg than adults at higher frequencies. Feedback in children is qualitatively similar to adults. Quantitatively, children show less phase advance at the peak of the feedback curve which may be due to a longer time delay. Under the high and low visual amplitude conditions, children show less gain change (interpreted as reweighting) than adults in the kinematic and EMG responses. The observed kinematic and EMG reweighting are mainly due to the different use of visual information by the central nervous system as measured by the open-loop mapping from visual scene angle to EMG activity. The plant and the feedback do not contribute to reweighting

The effects of peripheral nerve impairments on postural control and mobility among people with peripheral neuropathy

Approximately 20 million Americans are suffering Peripheral Neuropathy (PN). It is estimated that the prevalence of all-cause PN is about 2.4% in the entire adult population, whereas over 8-10% in the population segment over the age of 55 (Martyn & Hughes, 1997). Peripheral Neuropathy leads to a high risk of falling, resulting from the deficits of postural control caused by the impaired peripheral nerves, especially the degenerative somatosensory system. To date, there is no effective medical treatment for the disease but pain managements. The deficits of postural control decrease the life quality of this population. The degeneration of peripheral nerves reduces sensory inputs from the somatosensory system to central nervous system via spinal reflexive loop, which should provide valuable real-time information for balance correction. Therefore, it is necessary to investigate how PN affects the somatosensory system regarding postural control. Besides that, people with PN may develop a compensatory mechanism which could be reinforced by exercise training, ultimately to improve balance and mobility in their daily life. The neuroplasticity may occur within somatosensory system by relying on relative intact sensory resources. Hence, unveiling the compensatory mechanism in people with PN may help in understanding (a) essential sensations or function of peripheral nerves to postural control, (b) effective strategy of physical treatments for people with PN, and (c) task-dependent sensory information requirements. Therefore, this dissertation discussed the roles of foot sole sensation, ankle proprioception, and stretch reflex on balance as well as gait among people with PN. Furthermore, the discussion of the coupling between small and large afferent reflexive loops may spot the compensatory mechanism in people with PN