Analysis of a muscular control system in human movements

In this work, we have studied a muscular control system under experimental conditions for analyzing the dynamic behavior of individual muscles and theoretical considerations for elucidating its control strategy. Movement of human limbs is achieved by joint torques and each torque is specified as the sum of torques generated by muscle forces. The behavior of individual muscles is controlled by the neural input which is estimated by means of an electromyogram (EMG). In this study, the EMGs for a flexor and an extensor are measured in elbow joint movements and the dynamic behavior of individual muscles is analyzed. As a result, it is verified that both a flexor and an extensor are activated throughout the entire movement and that the activation of muscles is controlled above a specific limit independent of the hand-held load. Subsequently, a system model for simulating elbow joint movements is developed which includes the muscle dynamic relationship between the neural input and the isometric force. The minimum limit of muscle activation that has been confirmed in experiments is provided as a constraint of the neural input and the criterion is defined by a derivative of the isometric force of individual muscles. The optimal trajectories formulated under these conditions are quantitatively compared with the experimentally observed trajectories, and the control strategy of a muscular control system is studied. Finally, a muscular control system in multi-joint arm movements is discussed with regard to the comparative analysis between observed and optimal trajectories. Received: 7 April 1999 / Accepted in revised form: 27 July 1999     

The role of primary motor cortex in goal-directed movements: insights from neurophysiological studies on non-human primates

Neurophysiological studies on non-human primates have provided a large body of information on the response patterns of neurons in primary motor cortex during volitional motor tasks. Rather than finding a single simple pattern of activity in primary motor cortex neurons, these studies illustrate that neural activity in this area reflects many different types of information, including spatial goals, hand motion, joint motion, force output and electromyographic activity. This richness in the response characteristics of neurons makes estimates of any single variable on motor performance from population signals imprecise and prone to errors. It initially seems puzzling that so many different types of information are represented in primary motor cortex. However, such richness in neural responses reflects its important role in converting high-level behavioral goals generated in other cortical regions into complex spatiotemporal patterns to control not only alpha-motoneuron activity but also other features of spinal processing.     

Optimal feedback control to describe multiple representations of primary motor cortex neurons

Primary motor cortex (M1) neurons are tuned in response to several parameters related to motor control, and it was recently reported that M1 is important in feedback control. However, it remains unclear how M1 neurons encode information to control the musculoskeletal system. In this study, we examined the underlying computational mechanisms of M1 based on optimal feedback control (OFC) theory, which is a plausible hypothesis for neuromotor control. We modelled an isometric torque production task that required joint torque to be regulated and maintained at desired levels in a musculoskeletal system physically constrained by muscles, which act by pulling rather than pushing. Then, a feedback controller was computed using an optimisation approach under the constraint. In the presence of neuromotor noise, known as signal-dependent noise, the sensory feedback gain is tuned to an extrinsic motor output, such as the hand force, like a population response of M1 neurons. Moreover, a distribution of the preferred directions (PDs) of M1 neurons can be predicted via feedback gain. Therefore, we suggest that neural activity in M1 is optimised for the musculoskeletal system. Furthermore, if the feedback controller is represented in M1, OFC can describe multiple representations of M1, including not only the distribution of PDs but also the response of the neuronal population.     

Transformation of the afferent tactile signal into a motor command in the cat motor cortex

Activity of 112 neurons of the precruciate motor cortex in cats was studied during a forelimb placing reaction to tactile stimulation of its distal parts. The latent period of response of the limb to tactile stimulation was: for flexors of the elbow (biceps brachii) 30–40 msec, for the earliest reponses of cortical motor neurons about 20 msec. The biceps response was observed 5–10 msec after the end of stimulation of the cortex with a series of pulses lasting 25 msec. Two types of excitatory responses of the neurons were identified: responses of sensory type observed to each tactile stimulation of the limb and independent of the presence or absence of motion, and responses of motor type, which developed parallel with the motor response of the limb and were not observed in the absence of motion. The minimal latent period of the responses of motor type was equal to the latent period of the sensory responses to tactile stimulation (20±10 msec). Stimulation of the cortex through the recording microelectrode at the site of derivation of unit activity, which increased during active flexion of the forelimb at the elbow (11 stimuli at intervals of 2.5 msec, current not exceeding 25 µA), in 70% of cases evoked an electrical response in the flexor muscle of the elbow.M. V. Lomonosov Moscow State University. Translated from Neirofiziologiya, Vol. 9, No. 2, pp. 115–123, March–April, 1977.     

Contribution of muscle short-range stiffness to initial changes in joint kinetics and kinematics during perturbations to standing balance: A simulation study

Simulating realistic musculoskeletal dynamics is critical to understanding neural control of muscle activity evoked in sensorimotor feedback responses that have inherent neural transmission delays. Thus, the initial mechanical response of muscles to perturbations in the absence of any change in muscle activity determines which corrective neural responses are required to stabilize body posture. Muscle short-range stiffness, a history-dependent property of muscle that causes a rapid and transient rise in muscle force upon stretch, likely affects musculoskeletal dynamics in the initial mechanical response to perturbations. Here we identified the contributions of short-range stiffness to joint torques and angles in the initial mechanical response to support surface translations using dynamic simulation. We developed a dynamic model of muscle short-range stiffness to augment a Hill-type muscle model. Our simulations show that short-range stiffness can provide stability against external perturbations during the neuromechanical response delay. Assuming constant muscle activation during the initial mechanical response, including muscle short-range stiffness was necessary to account for the rapid rise in experimental sagittal plane knee and hip joint torques that occurs simultaneously with very small changes in joint angles and reduced root mean square errors between simulated and experimental torques by 56% and 47%, respectively. Moreover, forward simulations lacking short-range stiffness produced unreasonably large joint angle changes during the initial response. Using muscle models accounting for short-range stiffness along with other aspects of history-dependent muscle dynamics may be important to advance our ability to simulate inherently unstable human movements based on principles of neural control and biomechanics.     

Pre-processing and transfer entropy measures in motor neurons controlling limb movements

Directed information transfer measures are increasingly being employed in modeling neural system behavior due to their model-free approach, applicability to nonlinear and stochastic signals, and the potential to integrate repetitions of an experiment. Intracellular physiological recordings of graded synaptic potentials provide a number of additional challenges compared to spike signals due to non-stationary behaviour generated through extrinsic processes. We therefore propose a method to overcome this difficulty by using a preprocessing step based on Singular Spectrum Analysis (SSA) to remove nonlinear trends and discontinuities. We apply the method to intracellular recordings of synaptic responses of identified motor neurons evoked by stimulation of a proprioceptor that monitors limb position in leg of the desert locust. We then apply normalized delayed transfer entropy measures to neural responses evoked by displacements of the proprioceptor, the femoral chordotonal organ, that contains sensory neurones that monitor movements about the femoral-tibial joint. We then determine the consistency of responses within an individual recording of an identified motor neuron in a single animal, between repetitions of the same experiment in an identified motor neurons in the same animal and in repetitions of the same experiment from the same identified motor neuron in different animals. We found that delayed transfer entropy measures were consistent for a given identified neuron within and between animals and that they predict neural connectivity for the fast extensor tibiae motor neuron.     

Neuronal responses in area 7 of the cat cortex during defensive conditioning

Responses of neurons in area 7 of the parietal association cortex during and after formation of a defensive conditioned reflex to sound were recorded in waking cats. Changes in spike responses of the neurons as a result of the onset of conditioned reflex limb movements were observed in 68% of neurons. Spike responses of neurons formed as a result of learning appeared only if conditioned-reflex limb movements appeared, and they were not observed if, for some reason or other, movements were absent after presentation of the positive conditioned stimulus or on extinction of the reflex. Responses of 46% neurons to conditioned stimulation preceded the conditioned-reflex motor responses by 50–450 msec. The remaining responding neurons were recruited into the response after the beginning of movement. Characteristic spike responses of neurons to the conditioned stimulus appeared 500–900 msec before the beginning of movement and, in the case of appearance of special, "prolonged" motor responses of limb withdrawal, evoked by subsequent reinforcing stimulation.     

The development of contact force construction in the dynamic-contact task of cycling [corrected

Purposeful movement requires that an individual produce appropriate joint torques to accelerate segments, and when environmental contact is involved, to develop task-appropriate contact forces. Developmental research has been confined largely to the mastery of unconstrained movement skills (pointing, kicking). The purpose of this study was to study the developmental progression that characterizes the interaction of muscular and non-muscular forces in tasks constrained by contact with the environment. Seven younger children (YC, 6-8 years), 7 older children (OC, 9-11 years) and 7 adults (AD) pedaled an ergometer (80 rpm) at an anthropometrically scaled cycling power. Resultant forces measured at the pedal's surface were decomposed into muscle, inertia and gravity components. Muscle pedal forces were further examined in terms of the underlying lower extremity joint torques and kinematic weights that constitute the muscular component of the pedal force. Data showed children applied muscle forces to the pedal in a significantly different manner compared to adults, and that this was due to the children's lower segmental mass and inertia. The children adjusted the contribution of the proximal joint muscle torques to compensate for reduced contributions to the resultant pedal force by gravitational and inertial components. These data show that smaller segmental mass and inertia limit younger children's ability to construct the dynamic-contact task of cycling in an adult-like form. On the basis of these results, however, the children's response was not "immature". Rather, the results show a task-appropriate adaptation to lower segmental mass and inertia.     

A pilot study investigating motor adaptations when learning to walk with a whole-body powered exoskeleton

Evidence is emerging on how whole-body powered exoskeleton (EXO) use impacts users in basic occupational work scenarios, yet our understanding of how users learn to use this complex technology is limited. We explored how novice users adapted to using an EXO during gait. Six novices and five experienced users completed the study. Novices completed an initial training/familiarization gait session, followed by three subsequent gait sessions using the EXO, while experienced users completed one gait session with the EXO. Spatiotemporal gait measures, pelvis and lower limb joint kinematics, muscle activities, EXO torques, and human-EXO interaction forces were measured. Adaptations among novices were most pronounced in spatiotemporal gait measures, followed by joint kinematics, with smaller changes evident in muscle activity and EXO joint torques. Compared to the experienced users, novices exhibited a shorter step length and walked with significantly greater anterior pelvic tilt and less hip extension. Novices also used lower joint torques from the EXO at the hip and knee, and they had greater biceps femoris activity. Overall, our results may suggest that novices exhibited clear progress in learning, but they had not yet adopted motor strategies similar to those of experienced users after the three sessions. We suggest potential future directions to enhance motor adaptations to powered EXO in terms of both training protocols and human-EXO interfaces.     

Dynamic Neural Network Models of the Premotoneuronal Circuitry Controlling Wrist Movements in Primates

Dynamic recurrent neural networks were derived to simulate neuronal populations generating bidirectional wrist movements in the monkey. The models incorporate anatomical connections of cortical and rubral neurons, muscle afferents, segmental interneurons and motoneurons; they also incorporate the response profiles of four populations of neurons observed in behaving monkeys. The networks were derived by gradient descent algorithms to generate the eight characteristic patterns of motor unit activations observed during alternating flexion-extension wrist movements. The resulting model generated the appropriate input-output transforms and developed connection strengths resembling those in physiological pathways. We found that this network could be further trained to simulate additional tasks, such as experimentally observed reflex responses to limb perturbations that stretched or shortened the active muscles, and scaling of response amplitudes in proportion to inputs. In the final comprehensive network, motor units are driven by the combined activity of cortical, rubral, spinal and afferent units during step tracking and perturbations.The model displayed many emergent properties corresponding to physiological characteristics. The resulting neural network provides a working model of premotoneuronal circuitry and elucidates the neural mechanisms controlling motoneuron activity. It also predicts several features to be experimentally tested, for example the consequences of eliminating inhibitory connections in cortex and red nucleus. It also reveals that co-contraction can be achieved by simultaneous activation of the flexor and extensor circuits without invoking features specific to co-contraction.     

Interaction between neurons of the frontal cortex and hippocampus during the realization of choice of food reinforcement quality in cats

Six cats were subjected to the procedure of appetitive instrumental conditioning (with light as a conditioned stimuls) by the method of the "active choice" of reinforcement quality. Short-delay conditioned bar-press responses were rewarded with bread-meat mixture, and the delayed responses were reinforced by meat. The animals differed in behavior strategy: four animals preferred the bar-pressing with a long delay (the so-called "self-control" group), and two cats preferred the bar-pressing with a short delay (the so-called "impulsive" group). Multiunit activity in the frontal cortex and hippocampus (CA3) was recorded via chronically implanted nichrome wire semimicroelectrodes. An interaction between the neighboring neurons in the frontal cortex and hippocampus (within local neural networks) and between the neurons of the frontal cortex and hippocampus (distributed neural networks in frontal-hippocampal and hippocampal-frontal directions) was evaluated by means of statistical crosscorrelation analysis of spike trains. Crosscorrelations between neuronal spike trains in the delay range of 0-100 ms were explored. It was shown that the number of crosscorrelations between the neuronal discharges both in the local and distributed networks was significantly higher in the "self-control" cats. It was suggested that the local and distributed neural networks of the frontal cortex and hippocampus are involved in the system of brain structures which determine the behavioral strategy of animals in the "self-control" group.     

Modulation of shoulder muscle and joint function using a powered upper-limb exoskeleton

Robotic-assistive exoskeletons can enable frequent repetitive movements without the presence of a full-time therapist; however, human-machine interaction and the capacity of powered exoskeletons to attenuate shoulder muscle and joint loading is poorly understood. This study aimed to quantify shoulder muscle and joint force during assisted activities of daily living using a powered robotic upper limb exoskeleton (ArmeoPower, Hocoma). Six healthy male subjects performed abduction, flexion, horizontal flexion, reaching and nose touching activities. These tasks were repeated under two conditions: (i) the exoskeleton compensating only for its own weight, and (ii) the exoskeleton providing full upper limb gravity compensation (i.e., weightlessness). Muscle EMG, joint kinematics and joint torques were simultaneously recorded, and shoulder muscle and joint forces calculated using personalized musculoskeletal models of each subject’s upper limb. The exoskeleton reduced peak joint torques, muscle forces and joint loading by up to 74.8% (0.113 Nm/kg), 88.8% (5.8%BW) and 68.4% (75.6%BW), respectively, with the degree of load attenuation strongly task dependent. The peak compressive, anterior and superior glenohumeral joint force during assisted nose touching was 36.4% (24.6%BW), 72.4% (13.1%BW) and 85.0% (17.2%BW) lower than that during unassisted nose touching, respectively. The present study showed that upper limb weight compensation using an assistive exoskeleton may increase glenohumeral joint stability, since deltoid muscle force, which is the primary contributor to superior glenohumeral joint shear, is attenuated; however, prominent exoskeleton interaction moments are required to position and control the upper limb in space, even under full gravity compensation conditions. The modeling framework and results may be useful in planning targeted upper limb robotic rehabilitation tasks.     

Effects of force detecting sense organs on muscle synergies are correlated with their response properties

Sense organs that monitor forces in legs can contribute to activation of muscles as synergist groups. Previous studies in cockroaches and stick insects showed that campaniform sensilla, receptors that encode forces via exoskeletal strains, enhance muscle synergies in substrate grip. However synergist activation was mediated by different groups of receptors in cockroaches (trochanteral sensilla) and stick insects (femoral sensilla). The factors underlying the differential effects are unclear as the responses of femoral campaniform sensilla have not previously been characterized. The present study characterized the structure and response properties (via extracellular recording) of the femoral sensilla in both insects. The cockroach trochantero-femoral (TrF) joint is mobile and the joint membrane acts as an elastic antagonist to the reductor muscle. Cockroach femoral campaniform sensilla show weak discharges to forces in the coxo-trochanteral (CTr) joint plane (in which forces are generated by coxal muscles) but instead encode forces directed posteriorly (TrF joint plane). In stick insects, the TrF joint is fused and femoral campaniform sensilla discharge both to forces directed posteriorly and forces in the CTr joint plane. These findings support the idea that receptors that enhance synergies encode forces in the plane of action of leg muscles used in support and propulsion.     

A learned neural network that simulates properties of the neuronal population vector

This paper considers steady-state and timedependent characteristics of the response of the hidden-layer neurons in a dynamic model for the neural network trained through supervised learning to perform transformation of input signals into output signals. This transformation is set up so as to correspond to variation in the directions of two-dimensional vectors and is treated as creation by the network of a movement direction in response to a stimulus direction. The input vector is encoded in the state of the input layer at the initial instant of time, and the output vector in the state of the output layer at great values of time. After the network has been trained on examples of the input-output relation, the hidden neurons turn out to be broadly tuned to direction. The corresponding dependence for their activity is approximated with a smooth function, whose maximum allows some preferred direction to be attributed to each neuron. If each hidden neuron is assigned a vector pointing in its preferred direction, then any arbitrarily chosen direction can be characterized by an imaginary neuronal population vector (Georgopoulos et al. 1986) defined as the sum of the vectors of preferred direction for the neurons, with the weights equal to their activities for the chosen direction. It is demonstrated that, although hidden neurons are broadly tuned to direction, the population vector points in a direction congruent with that of the input vector at the initial moment of time and accurately predicts the direction of the output vector at great values of time. In between, the population vector turns continuously from the one direction towards the other. The dynamic and stationary properties of the population vector of the hidden-layer neurons, as obtained within the framework of the model in question, show a close similarity to the experimentally observed (Georgopoulos et al. 1986; Georgopoulos et al. 1989) behaviour of the population vector constructed in the same manner on the ensemble of motor cortex neurons sensitive to a certain type of movement.     

The individual organization of frontal-hippocampal networks during realization of different behavioral tasks

Four cats were subjected to appetitive instrumental conditioning with light as a conditioned stimulus by the method of "active choice" of the reinforcement quality: short-delay conditioned bar-press responses were followed by bread-meat mixture and the delayed responses--by meat. The animals differed in behavior strategy: four animals preferred bar-pressing with long delay (so called "self-control" group); two animal preferred bar-pressing with short-delay (so called "impulsive" group). Then all the animals were learned to short-delay (1 s) instrumental conditioned reflex to light (CS+) reinforced by meat. The multiunit activity in the frontal cortex and the hippocampus (CA3) was recorded through chronically implanted nichrome-wire semimicroelectrodes. The interactions among the neighboring neurons in the frontal cortex and hippocampus (within the local neuronal networks) and between the neurons of the frontal cortex and hippocampus (distributed neuronal networks of frontal-hippocampal and hippocampal-frontal directions) were evaluated by means of statistical crosscorrelation analysis of the spike trains. Crosscorrelation interneuronal connections in the delay range 0-100 ms were explored. It was shown that the functional organization of the frontal and hippocampal neuronal networks differed in choice behavior and was similar during realization of short-delayed conditioned reflex. We suggest that the local and distributed neural networks of the frontal cortex and hippocampus take part in the realization of cognitive behavior, in particularly in the processes of the decision making.     

Cortical mechanisms of smooth eye movements revealed by dynamic covariations of neural and behavioral responses

Neural activity in the frontal eye fields controls smooth pursuit eye movements, but the relationship between single neuron responses, cortical population responses, and eye movements is not well understood. We describe an approach to dynamically link trial-to-trial fluctuations in neural responses to parallel variations in pursuit and demonstrate that individual neurons predict eye velocity fluctuations at particular moments during the course of behavior, while the population of neurons collectively tiles the entire duration of the movement. The analysis also reveals the strength of correlations in the eye movement predictions derived from pairs of simultaneously recorded neurons and suggests a simple model of cortical processing. These findings constrain the primate cortical code for movement, suggesting that either a few neurons are sufficient to drive pursuit at any given time or that many neurons operate collectively at each moment with remarkably little variation added to motor command signals downstream from the cortex.     

A mechanical link model of two-toed sloths: no pendular mechanics during suspensory locomotion

Sloths are morphologically specialized in suspensory quadrupedal locomotion and posture. During steady-state locomotion they utilize a trot-like footfall sequence. Contrasting the growing amount of published accounts of the functional morphology and kinematics of sloth locomotion, no study concerned with the dynamics of their quadrupedal suspensory locomotion has been conducted. Brachiating primates have been shown to travel at low mechanical costs using pendular mechanics, but this is associated with considerable dynamic forces exerted onto the support. To test whether sloth locomotion can be described by simple connected pendulum mechanics, we analyzed the dynamics of sloth locomotion with use of a mechanical segment link model. The model integrates the body segment parameters and is driven by kinematic data with both segment parameters and kinematic data obtained from the same sloth individual. No simple pendular mechanics were present. We then used the model to carry out an inverse dynamic analysis. The analysis allowed us to estimate net limb joint torques and substrate reaction forces during the contact phases. Predominant flexing limb joint torque profiles in the shoulder, elbow, hip, and knee are in stark contrast to published dominant extensor torques in the limb joints of pronograde quadrupedal mammals. This dissimilarity likely reflects the inverse orientation of the sloth towards the gravity vector. Nevertheless, scapular pivot and shoulder seem to provide the strongest torque for progression as expected based on unchanged basic kinematic pattern previously described. Our model predicts that sloths actively reduce the dynamical forces and moments that are transmitted onto the support. We conclude that these findings reflect the need to reduce the risk of breaking supports because in this case sloths would likely be unable to react quickly enough to prevent potentially lethal falls. To achieve this, sloths seem to avoid the dynamical consequences of effective pendular mechanics.     

Distinct neuronal types contribute to hybrid temporal encoding strategies in primate auditory cortex

Studies of the encoding of sensory stimuli by the brain often consider recorded neurons as a pool of identical units. Here, we report divergence in stimulus-encoding properties between subpopulations of cortical neurons that are classified based on spike timing and waveform features. Neurons in auditory cortex of the awake marmoset (Callithrix jacchus) encode temporal information with either stimulus-synchronized or nonsynchronized responses. When we classified single-unit recordings using either a criteria-based or an unsupervised classification method into regular-spiking, fast-spiking, and bursting units, a subset of intrinsically bursting neurons formed the most highly synchronized group, with strong phase-locking to sinusoidal amplitude modulation (SAM) that extended well above 20 Hz. In contrast with other unit types, these bursting neurons fired primarily on the rising phase of SAM or the onset of unmodulated stimuli, and preferred rapid stimulus onset rates. Such differentiating behavior has been previously reported in bursting neuron models and may reflect specializations for detection of acoustic edges. These units responded to natural stimuli (vocalizations) with brief and precise spiking at particular time points that could be decoded with high temporal stringency. Regular-spiking units better reflected the shape of slow modulations and responded more selectively to vocalizations with overall firing rate increases. Population decoding using time-binned neural activity found that decoding behavior differed substantially between regular-spiking and bursting units. A relatively small pool of bursting units was sufficient to identify the stimulus with high accuracy in a manner that relied on the temporal pattern of responses. These unit type differences may contribute to parallel and complementary neural codes.

Neurons in auditory cortex show highly diverse responses to sounds. This study suggests that neuronal type inferred from baseline firing properties accounts for much of this diversity, with a subpopulation of bursting units being specialized for precise temporal encoding.     

Hierarchical genetic algorithm versus static optimization-investigation of elbow flexion and extension movements

The applicability of static optimization (and, respectively, frequently used objective functions) for prediction of individual muscle forces for dynamic conditions has often been discussed. Some of the problems are whether time-independent objective functions are suitable, and how to incorporate muscle physiology in models. The present paper deals with a twofold task: (1) implementation of hierarchical genetic algorithm (HGA) based on the properties of the motor units (MUs) twitches, and using multi-objective, time-dependent optimization functions; and (2) comparison of the results of the HGA application with those obtained through static optimization with a criterion "minimum of a weighted sum of the muscle forces raised to the power of n". HGA and its software implementation are presented. The moments of neural stimulation of all MUs are design variables coding the problem in the terms of HGA. The main idea is in using genetic operations to find these moments, so that the sum of MUs twitches satisfies the imposed goals (required joint moments, minimal sum of muscle forces, etc.). Elbow flexion and extension movements with different velocities are considered as proper illustration. It is supposed that they are performed by two extensor muscles and three flexor muscles. The results show that HGA is a suitable means for precise investigation of motor control. Many experimentally observed phenomena (such as antagonistic co-contraction, three-phasic behavior of the muscles during fast movements) can find their explanation by the properties of the MUs twitches. Static optimization is also able to predict three-phasic behavior and could be used as practicable and computationally inexpensive method for total estimation of the muscle forces.