What muscle variable(s) does the nervous system control in limb movements?

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.

Neurophysiology and neural engineering: a review

Neurophysiology is the branch of physiology concerned with understanding the function of neural systems. Neural engineering (also known as neuroengineering) is a discipline within biomedical engineering that uses engineering techniques to understand, repair, replace, enhance, or otherwise exploit the properties and functions of neural systems. In most cases neural engineering involves the development of an interface between electronic devices and living neural tissue. This review describes the origins of neural engineering, the explosive development of methods and devices commencing in the late 1950s, and the present-day devices that have resulted. The barriers to interfacing electronic devices with living neural tissues are many and varied, and consequently there have been numerous stops and starts along the way. Representative examples are discussed. None of this could have happened without a basic understanding of the relevant neurophysiology. I also consider examples of how neural engineering is repaying the debt to basic neurophysiology with new knowledge and insight.

Local Dynamic Stability of Spine Muscle Activation and Stiffness Patterns During Repetitive Lifting

To facilitate stable trunk kinematics, humans must generate appropriate motor patterns to effectively control muscle force and stiffness and respond to biomechanical perturbations and/or neuromuscular control errors. Thus, it is important to understand physiological variables such as muscle force and stiffness, and how these relate to the downstream production of stable spine and trunk movements. This study was designed to assess the local dynamic stability of spine muscle activation and rotational stiffness patterns using Lyapunov analyses, and relationships to the local dynamic stability of resulting spine kinematics, during repetitive lifting and lowering at varying combinations of lifting load and rate. With an increase in the load lifted at a constant rate there was a trend for decreased local dynamic stability of spine muscle activations and the muscular contributions to spine rotational stiffness; although the only significant change was for the full state space muscle activation stability (p < 0.05). With an increase in lifting rate with a constant load there was a significant decrease in the local dynamic stability of spine muscle activations and the muscular contributions to spine rotational stiffness (p ≤ 0.001 for all measures). These novel findings suggest that the stability of motor inputs and the muscular contributions to spine rotational stiffness can be altered by external task demands (load and lifting rate), and therefore are important variables to consider when assessing the stability of the resulting kinematics.

The representation of egocentric space in the posterior parietal cortex

The posterior parietal cortex (PPC) is the most likely site where egocentric spatial relationships are represented in the brain. PPC cells receive visual, auditory, somaesthetic, and vestibular sensory inputs; oculomotor, head, limb, and body motor signals; and strong motivational projections from the limbic system. Their discharge increases not only when an animal moves towards a sensory target, but also when it directs its attention to it. PPC lesions have the opposite effect: sensory inattention and neglect. The PPC does not seem to contain a "map" of the location of objects in space but a distributed neural network for transforming one set of sensory vectors into other sensory reference frames or into various motor coordinate systems. Which set of transformation rules is used probably depends on attention, which selectively enhances the synapses needed for making a particular sensory comparison or aiming a particular movement.

A comparison between force and position control strategies in myoelectric prostheses

This work studies the simultaneous and proportional myoelectric force and position estimation of multiple degrees of freedom (DOFs) for unilateral transradial amputees. Two experiments were conducted to compare force and position control paradigms. In the first, a force experiment, subjects performed isometric contractions, while the force applied by the limb and EMG were recorded. In the second, a position experiment, dynamic contractions were permitted during which position of the limb and EMG were measured. Artificial neural networks (ANNs) were trained to estimate force/position from EMG of the contralateral limb during mirrored bilateral contractions. This study involved contractions with combined activations of three DOFs including wrist: flexion/extension, radial/ulnar deviation and forearm supination/pronation. For the given data set, while force estimation demonstrated high accuracy (R(2)=0.84±0.02), position estimation performance was relatively poor (R(2)=0.57±0.05). Two healthy subjects participated in this work.

Does the nervous system depend on kinesthetic information to control natural limb movements?

This target article draws together two groups of experimental studies on the control of human movement through peripheral feedback and centrally generated signals of motor commands. First, during natural movement, feedback from muscle, joint, and cutaneous afferents changes; in human subjects these changes have reflex and kinesthetic consequences. Recent psychophysical and microneurographic evidence suggests that joint and even cutaneous afferents may have a proprioceptive role. Second, the role of centrally generated motor commands in the control of normal movements and movements following acute and chronic deafferentation is reviewed. There is increasing evidence that subjects can perceive their motor commands under various conditions, but that this is inadequate for normal movement; deficits in motor performance arise when the reliance on proprioceptive feedback is abolished either experimentally or because of pathology. During natural movement, the CNS appears to have access to functionally useful input from a range of peripheral receptors as well as from internally generated command signals. The unanswered questions that remain suggest a number of avenues for further research.

Implications of neural networks for how we think about brain function

Engineers use neural networks to control systems too complex for conventional engineering solutions. To examine the behavior of individual hidden units would defeat the purpose of this approach because it would be largely uninterpretable. Yet neurophysiologists spend their careers doing just that! Hidden units contain bits and scraps of signals that yield only arcane hints about network function and no information about how its individual units process signals. Most literature on single-unit recordings attests to this grim fact. On the other hand, knowing a system's function and describing it with elegant mathematics tell one very little about what to expect of interneuronal behavior. Examples of simple networks based on neurophysiology are taken from the oculomotor literature to suggest how single-unit interpretability might decrease with increasing task complexity. It is argued that trying to explain how any real neural network works on a cell-by-cell, reductionist basis is futile and we may have to be content with trying to understand the brain at higher levels of organization.

Origins of a repetitive and co-contractive biphasic pattern of muscle activation in Parkinson's disease

In studies of electromyographic (EMG) patterns during movements in Parkinson's disease, often a repetitive and sometimes co-contractive pattern of antagonist muscle activation is observed. It has been suggested that the origin of such patterns of muscle activation is a central one arising from impairments in the basal ganglia structures and/or the cortex, although afferent inputs can also modulate the voluntary activity. A neural network model of Parkinson's disease, bradykinesia and rigidity, is extended to quantitatively study the conditions under which such a repetitive and co-contractive pattern of muscle activation appears. Computer simulations show that an oscillatory disrupted globus pallidus internal segment (GPi) response signal comprising at least two excitation-inhibition sequences as an input to a normally functioning cortico-spinal model of movement generation results in a repetitive, but not co-contractive agonist-antagonist pattern of muscle activation. A repetitive and co-contractive pattern of muscle activation results when also dopamine is depleted in the cortex. Finally, additional dopamine depletion in the spinal cord sites results in a reduction of the size, duration and rate of change of the repetitive and co-contractive EMG bursts. These results have important consequences in the development of Parkinson's Disease therapies such as dopamine replacement in cortex and spinal cord, which can alleviate some of the impairments of Parkinson's Disease such as slowness of movement (bradykinesia) and rigidity.

What does body configuration in microgravity tell us about the contribution of intra- and extrapersonal frames of reference for motor control?

The authors report that the reorganization of body configuration during weightlessness is based on an intrapersonal frame of reference such as the configuration of the support surface and the position of the body's center of gravity. These results stress the importance of “knowledge” of the state of internal geometric structures, which cannot be directly signalled by specific receptors responsible for direct dialogue with the physical external world.

Reciprocal and coactivation commands are not sufficient to describe muscle activation patterns

Recent results have shown that the relative activation of muscles is different for isometric contractions and for movements. These results exclude an explanation of muscle activation patterns by a combination ofreciprocal and coactivation commands. These results also indicate that joint stiffness is not uniquely determined and that it may be different for isometric contractions and movements.

Frames of reference interact and are task-dependent

The problem for the CNS in any particular movement task is to coordinate the various frames of reference appropriate to the task. Control variables are determined by this coordination. The coordination problem varies greatly from task to task, and so no single set of control variables is likely to account for a broad range of movement tasks.

The λ model for motor control: More than meets the eye

Understanding of the λ model has greatly increased in recent years as evidenced by most of the commentaries. Some commentators underscored the potential of the model to integrate aspects of different sensorimotor systems in the production of movement. Other commentators focused on not-yet-fully-developed parts of the model. A few persisted in misunderstanding some of its basic concepts, and on these grounds they reject it. In responding to commentaries we continue to elaborate on some fundamental points of the model, especially control variables, the idea of movement production by shifting the positional frame of reference and the hypothesis of biomechanical correspondence in motor control. We also continue to develop our ideas on the intrinsic generation of the frame of reference associated with external space and utilized for the control of arm movement and locomotion. The dynamic principles underlying the model are discussed in light of the dynamical systems approach.

Grip force adjustments during rapid hand movements suggest that detailed movement kinematics are predicted

The λ model suggests that detailed kinematics arise from changes in control variables and need not be explicitly planned. However, we have shown that when moving a grasped object, grip force is precisely modulated in phase with acceleration-dependent inertial load. This suggests that the motor system can predict detailed kinematics. This prediction may be based on a forward model of the dynamics of the loaded limb.

The organization of human postural movements: A formal basis and experimental synthesis

A scheme for understanding the organization of human postural movements is developed in the format of a position paper. The structural characteristics of the body and the geometry of muscular actions are incorporated into a three-dimensional graphical representation of human movement mechanics in the sagittal plane. A series of neural organizational hypotheses limit a theoretically infinite number of combinations of muscle contractions and associated movement trajectories for performing postural corrections: (1) Controls are organized to use the minimum number of muscles; (2) frequently performed movements are organized to require a minimum of neural decision-making.

These hypotheses lead to the prediction that postural movements are composed of muscle contractile strategies derived from a limited set of distinct contractile patterns. The imposition of two mechanical constraints related to the configuration of support and to requirements for body stability with respect to gravity predict the conditions under which individual movement strategies will be deployed.

A complementary organizational scheme for the senses is developed. We show that organization of postural movements into combinations of distinct strategies simplifies the interpretation of sensory inputs. The fine-tuning of movement strategies can be accomplished by breaking down the complex array of feedback information into a series of scalar quantities related to the parameters of the movement strategies. For example, the magnitude, aim, and curvature of the movement trajectory generated by an individual strategy can be adjusted independently.

The second half of the report compares theoretical predictions with a series of actual experimental observations on normal subjects and patients with known sensory and motor disorders. Actual postural movements conform to theoretical predictions about the composition of individual movement strategies and the conditions under which each strategy is used. Observations on patients suggest how breakdowns in individual steps within the logical process of organization can lead to specific movement abnormalities.

Discussion focuses on the areas needing further experimentation and on the implications of the proposed organizational scheme. We conclude that although our organizational scheme is not new in demonstrating the need for simplifying the neural control of movement, it is perhaps original in imposing discrete logical control upon a continuous mechanical system. The attraction of the scheme is that it provides a framework compatible with both mechanical and physiological information and amenable to experimental testing.

Let us accept a “controlled trade-off” model of motor control

The trade-off between force and length of muscle as adjusted by neural signals is a critical fact in the dynamics of motor control. Whether we call it “length-tension effect,” “feedback-like,” “invariant condition,” or “spring-like” is unimportant. We must not let semantics or details of representation obscure the basic physics of effects introduced by this trade-off in muscle.

Equilibrium-point control? Yes! Deterministic mechanisms of control? No!

The equilibrium-point hypothesis (the λ-model) is superior to all other models of single-joint control and provides deep insights into the mechanisms of control of multi-joint movements. Attempts at associating control variables with neurophysiological variables look confusing rather than promising. Probabilistic mechanisms may play an important role in movement generation in redundant systems.

The unobservability of central commands: Why testing hypotheses is so difficult

The experiments Feldman and Levin suggest do not definitively test their proposed solution to the problem of selecting muscle activations. Their test of the movement directions that elicit EMG activity can be interpreted without regard to the form of the central commands, and their fast elbow flexion test is based on a forward computation that obscures the insensitivity of the predicted trajectory to the details of the putative commands.

Biological variability and control of movements via δλ

Three issues related to Feldman and Levin's treatment of biological variability are discussed. We question the usefulness of the indirect component of δλ. We suggest that trade-offs between speed and accuracy in aimed movements support identification of δλ, rather than λ, as a control variable. We take issue with the authors' proposal for resolving redundancy in multi-joint movements, given recent data.

A few reasons why psychologlsts can adhere to Feldman and Levin's model

We emphasize the relevance to cognitive psychology of Feldman and Levin's theoretical position. Traditional views of motor control have failed to clearly separate “production control” at the level of motor command, based on task-independent CV (control variables), from intentional “product control” based on task-dependent parameters. Because F&L's approach concentrates on the first process (trajectory formation), it can distinguish the product control stage.

Levers to generate movement

The following questions are discussed: (1) Who determines the nature of “control variables”? (2) Is the “positional monopoly” healthy? (3) Does a descending command alter reflex threshold alone without eoncomitantly altering stiffness? (4) How does the CNS deal with history-dependent effects? (5) Should we abandon the idea that the CNS controls classical Newtonian variables such as muscle length?

Kinematic invariances and body schema

Generalizing the notion that muscles are positional frames of reference, a high-dimensional muscle space is defined for multi-muscle systems with an embedded low-dimensional motor manifold of functional articulators. A central representation of such a manifold is proposed as computational body schema. The example of the jaw-tongue system is presented, discussing the relation of functional articulators with kinematic invariances and control problems.