Reflex regulation of antagonist muscles for control of joint equilibrium position

On the analysis and control of a bipedal legged locomotion model via partial feedback linearization

In this study, we introduce a new model for bipedal locomotion that enhances the classical spring-loaded inverted pendulum (SLIP) model. Our proposed model incorporates a damping term in the leg spring, a linear actuator serially interconnected to the leg, and a rotary actuator affixed to the hip. The distinct feature of this new model is its ability to overcome the non-integrability challenge inherent in the conventional SLIP models through the application of partial feedback linearization. By leveraging these actuators, our model enhances the stability and robustness of the locomotion mechanism, particularly when navigating across varied terrain profiles. To validate the effectiveness and practicality of this model, we conducted detailed simulation studies, benchmarking its performance against other recent models outlined in the literature. Our findings suggest that the redundancy in actuation introduced by our model significantly facilitates both open-loop and closed-loop walking gait, showcasing promising potential for the future of bipedal locomotion, especially for bio-inspired robotics applications in outdoor and rough terrains.

Enhancing Force Control of Prosthetic Controller for Hand Prosthesis by Mimicking Biological Properties

Prosthetic hands are frequently rejected due to frustrations in daily uses. By adopting principles of human neuromuscular control, it could potentially achieve human-like compliance in hand functions, thereby improving functionality in prosthetic hand. Previous studies have confirmed the feasibility of real-time emulation of neuromuscular reflex for prosthetic control. This study further to explore the effect of feedforward electromyograph (EMG) decoding and proprioception on the biomimetic controller. The biomimetic controller included a feedforward Bayesian model for decoding alpha motor commands from stump EMG, a muscle model, and a closed-loop component with a model of muscle spindle modified with spiking afferents. Real-time control was enabled by neuromorphic hardware to accelerate evaluation of biologically inspired models. This allows us to investigate which aspects in the controller could benefit from biological properties for improvements on force control performance. 3 non-disabled and 3 amputee subjects were recruited to conduct a "press-without-break" task, subjects were required to press a transducer till the pressure stabilized in an expected range without breaking the virtual object. We tested whether introducing more complex but biomimetic models could enhance the task performance. Data showed that when replacing proportional feedback with the neuromorphic spindle, success rates of amputees increased by 12.2% and failures due to breakage decreased by 26.3%. More prominently, success rates increased by 55.5% and failures decreased by 79.3% when replacing a linear model of EMG with the Bayesian model in the feedforward EMG processing. Results suggest that mimicking biological properties in feedback and feedforward control may improve the manipulation of objects by amputees using prosthetic hands. Clinical and Translational Impact Statement: This control approach may eventually assist amputees to perform fine force control when using prosthetic hands, thereby improving the motor performance of amputees. It highlights the promising potential of the biomimetic controller integrating biological properties implemented on neuromorphic models as a viable approach for clinical application in prosthetic hands.

Focal Vibration Stretches Muscle Fibers by Producing Muscle Waves

Focal vibration is an effective intervention for the management of spasticity. However, its neuromechanical effects, particularly how tonic vibration reflex is induced explicitly, remain implicit. In this paper, we utilize a high-speed camera and a method of image processing to quantify the muscle vibration rigorously and disclose the neuromechanical mechanism of focal vibration. The vibration of 75 Hz is applied on the muscle belly of the biceps brachii and muscle responses are captured by a high-speed camera in profile. The muscle silhouettes are identified by the Canny edge detector to represent the stretch of muscle fibers, and the consistency between the muscle stretch and profile deformation has been confirmed by the magnetic resonance imaging in advance. Oscillations of muscle points discretized by pixels are identified by the fast Fourier transformation, respectively, and results demonstrate that focal vibration stretches muscle by producing muscle waves. Specifically, each point vibrates harmonically, and, given the linear phase modulation with transverse position, the muscle vibration propagates as traveling waves. The propagation of muscle waves is associated with muscle stretch, whose frequency is the same with the vibrator due to the curved baseline, and thus induces the tonic vibration reflex via spinal circuits.

Coordinated alpha and gamma control of muscles and spindles in movement and posture

Mounting evidence suggests that both α and γ motoneurons are active during movement and posture, but how does the central motor system coordinate the α-γ controls in these tasks remains sketchy due to lack of in vivo data. Here a computational model of α-γ control of muscles and spindles was used to investigate α-γ integration and coordination for movement and posture. The model comprised physiologically realistic spinal circuitry, muscles, proprioceptors, and skeletal biomechanics. In the model, we divided the cortical descending commands into static and dynamic sets, where static commands (α_s and γ_s) were for posture maintenance and dynamic commands (α_d and γ_d) were responsible for movement. We matched our model to human reaching movement data by straightforward adjustments of descending commands derived from either minimal-jerk trajectories or human EMGs. The matched movement showed smooth reach-to-hold trajectories qualitatively close to human behaviors, and the reproduced EMGs showed the classic tri-phasic patterns. In particular, the function of γ_d was to gate the α_d command at the propriospinal neurons (PN) such that antagonistic muscles can accelerate or decelerate the limb with proper timing. Independent control of joint position and stiffness could be achieved by adjusting static commands. Deefferentation in the model indicated that accurate static commands of α_s and γ_s are essential to achieve stable terminal posture precisely, and that the γ_d command is as important as the α_d command in controlling antagonistic muscles for desired movements. Deafferentation in the model showed that losing proprioceptive afferents mainly affected the terminal position of movement, similar to the abnormal behaviors observed in human and animals. Our results illustrated that tuning the simple forms of α-γ commands can reproduce a range of human reach-to-hold movements, and it is necessary to coordinate the set of α-γ descending commands for accurate and stable control of movement and posture.

Modular control of movement and posture by the corticospinal alpha-gamma motor systems

It is widely assumed that neural control of movement is carried out by the a motor system sufficiently. The role of the γ motor system in movement and posture has not been adequately addressed in motor control studies. Here, we propose a modular control model for movement and posture based on propriospinal neuronal (PN) network and spinal α-γ motor system. In the modular control model, the a and γ motor commands are divided into static and dynamic functions. The static commands are specified by the higher center of brain for posture control, and the dynamic commands for movement generation, respectively. Centrally planned kinematics based on the minimal jerk criterion is conveyed to the periphery via the γ motor system, while centrally programmed bi-phasic burst pattern of muscle activation is relayed to a pair of antagonistic muscles through the a motor system via the PN. Results of simulation showed that elbow kinematics and biceps and triceps activations displayed the similar kinematic and EMG features of fast reaching movement in human. This suggests a hypothesis that the α-γ motor systems can achieve modular control of movement and posture in parallel.

Spinal circuits can accommodate interaction torques during multijoint limb movements

The dynamic interaction of limb segments during movements that involve multiple joints creates torques in one joint due to motion about another. Evidence shows that such interaction torques are taken into account during the planning or control of movement in humans. Two alternative hypotheses could explain the compensation of these dynamic torques. One involves the use of internal models to centrally compute predicted interaction torques and their explicit compensation through anticipatory adjustment of descending motor commands. The alternative, based on the equilibrium-point hypothesis, claims that descending signals can be simple and related to the desired movement kinematics only, while spinal feedback mechanisms are responsible for the appropriate creation and coordination of dynamic muscle forces. Partial supporting evidence exists in each case. However, until now no model has explicitly shown, in the case of the second hypothesis, whether peripheral feedback is really sufficient on its own for coordinating the motion of several joints while at the same time accommodating intersegmental interaction torques. Here we propose a minimal computational model to examine this question. Using a biomechanics simulation of a two-joint arm controlled by spinal neural circuitry, we show for the first time that it is indeed possible for the neuromusculoskeletal system to transform simple descending control signals into muscle activation patterns that accommodate interaction forces depending on their direction and magnitude. This is achieved without the aid of any central predictive signal. Even though the model makes various simplifications and abstractions compared to the complexities involved in the control of human arm movements, the finding lends plausibility to the hypothesis that some multijoint movements can in principle be controlled even in the absence of internal models of intersegmental dynamics or learned compensatory motor signals.

Computationally efficient modeling of proprioceptive signals in the upper limb for prostheses: a simulation study

Accurate models of proprioceptive neural patterns could 1 day play an important role in the creation of an intuitive proprioceptive neural prosthesis for amputees. This paper looks at combining efficient implementations of biomechanical and proprioceptor models in order to generate signals that mimic human muscular proprioceptive patterns for future experimental work in prosthesis feedback. A neuro-musculoskeletal model of the upper limb with 7 degrees of freedom and 17 muscles is presented and generates real time estimates of muscle spindle and Golgi Tendon Organ neural firing patterns. Unlike previous neuro-musculoskeletal models, muscle activation and excitation levels are unknowns in this application and an inverse dynamics tool (static optimization) is integrated to estimate these variables. A proprioceptive prosthesis will need to be portable and this is incompatible with the computationally demanding nature of standard biomechanical and proprioceptor modeling. This paper uses and proposes a number of approximations and optimizations to make real time operation on portable hardware feasible. Finally technical obstacles to mimicking natural feedback for an intuitive proprioceptive prosthesis, as well as issues and limitations with existing models, are identified and discussed.

Corticomuscular transmission of tremor signals by propriospinal neurons in Parkinson's disease

Cortical oscillatory signals of single and double tremor frequencies act together to cause tremor in the peripheral limbs of patients with Parkinson's disease (PD). But the corticospinal pathway that transmits the tremor signals has not been clarified, and how alternating bursts of antagonistic muscle activations are generated from the cortical oscillatory signals is not well understood. This paper investigates the plausible role of propriospinal neurons (PN) in C3–C4 in transmitting the cortical oscillatory signals to peripheral muscles. Kinematics data and surface electromyogram (EMG) of tremor in forearm were collected from PD patients. A PN network model was constructed based on known neurophysiological connections of PN. The cortical efferent signal of double tremor frequencies were integrated at the PN network, whose outputs drove the muscles of a virtual arm (VA) model to simulate tremor behaviors. The cortical efferent signal of single tremor frequency actuated muscle spindles. By comparing tremor data of PD patients and the results of model simulation, we examined two hypotheses regarding the corticospinal transmission of oscillatory signals in Parkinsonian tremor. Hypothesis I stated that the oscillatory cortical signals were transmitted via the mono-synaptic corticospinal pathways bypassing the PN network. The alternative hypothesis II stated that they were transmitted by way of PN multi-synaptic corticospinal pathway. Simulations indicated that without the PN network, the alternating burst patterns of antagonistic muscle EMGs could not be reliably generated, rejecting the first hypothesis. However, with the PN network, the alternating burst patterns of antagonist EMGs were naturally reproduced under all conditions of cortical oscillations. The results suggest that cortical commands of single and double tremor frequencies are further processed at PN to compute the alternating burst patterns in flexor and extensor muscles, and the neuromuscular dynamics demonstrated a frequency dependent damping on tremor, which may prevent tremor above 8 Hz to occur.

A Simplified Spinal-Like Controller Facilitates Muscle Synergies and Robust Reaching Motions

We develop an adaptive controller for multi-joint, multi-muscle arm movements based on simplified spinal-like circuits found in the periphery, muscle synergies, and interpretations of gain-field projections from reach related neurons in the Superior Colliculus. The resulting innovation provides a highly robust sensory based controller that can be adapted to systems which require multi-muscle co-ordination. It provides human-like responses during perturbations elicited either internally or by the environment and for simple point-to-point reaching. We simulate limb motion and EMGs in Simulink using Virtual Muscle models and a variety of paradigms, including motion with external perturbations, and varying levels of antagonist muscle co-contractions. The results show that the system can exhibit smooth coordinated motions, without explicit kinematic or dynamic planning even in the presence of perturbations. In addition, we show by varying the level of muscle co-contractions from 0% to 40%, that the effects of external perturbations on joint trajectories can be reduced by up to 42%. The improved controller design is novel providing robust behavior during dynamic events and an automatic adaptive response from sensory-integration.

A novel experimental method to evaluate motor task control in Parkinson's patients

In this paper, a novel experimental method was developed to study planar arm movement control in tremor dominant Parkinson's (PD) patients. The method utilized a ball-bearing supported fiberglass brace apparatus against gravity to maintain the upper extremity in the horizontal plane. Subjects can perform postural and movement tasks with minimum damping effects. Arm movements were recorded using the MotionMonitor II system concurrently with EMGs of multiple muscles. Testing results in normal subjects with and without the brace support showed that the inertia and damping effects were negligible for oscillatory arm movement at maximum voluntary frequency (MVF). The tremor behaviors in horizontal posture maintenance and reaching movement in three PD subjects were also obtained with this method. The average frequency of postural tremor was 4.34 ± 0.15 Hz in all arm positions tested. However, the tremor magnitudes changed significantly with posture locations. In performing reaching movements, the tremor was inhibited prior to reaching, but resumed after reaching. These results may provide interesting insights into the pathological mechanisms of Parkinsonian tremor, as well as the modular nature of neural control of movements.

Evaluation of feedforward and feedback contributions to hand stiffness and variability in multijoint arm control

The purpose of this study is to validate a neuromechanical model of the virtual arm (VA) by comparing emerging behaviors of the model to those of experimental observations. Hand stiffness of the VA model was obtained by either theoretical computation or simulated perturbations. Variability in hand position of the VA was generated by adding signal dependent noise (SDN) to the motoneuron pools of muscles. Reflex circuits of Ia, Ib and Renshaw cells were included to regulate the motoneuron pool outputs. Evaluation of hand stiffness and variability was conducted in simulations with and without afferent feedback under different patterns of muscle activations during postural maintenance. The simulated hand stiffness and variability ellipses captured the experimentally observed features in shape, magnitude and orientation. Steady state afferent feedback contributed significantly to the increase in hand stiffness by 35.75±16.99% in area, 18.37±7.80% and 16.15±7.15% in major and minor axes; and to the reduction of hand variability by 49.41±21.19% in area, 36.89±12.78% and 18.87±23.32% in major and minor axes. The VA model reproduced the neuromechanical behaviors that were consistent with experimental data, and it could be a useful tool for study of neural control of posture and movement, as well as for application to rehabilitation.

Fusimotor control of spindle sensitivity regulates central and peripheral coding of joint angles

Proprioceptive afferents from muscle spindles encode information about peripheral joint movements for the central nervous system (CNS). The sensitivity of muscle spindle is nonlinearly dependent on the activation of gamma (γ) motoneurons in the spinal cord that receives inputs from the motor cortex. How fusimotor control of spindle sensitivity affects proprioceptive coding of joint position is not clear. Furthermore, what information is carried in the fusimotor signal from the motor cortex to the muscle spindle is largely unknown. In this study, we addressed the issue of communication between the central and peripheral sensorimotor systems using a computational approach based on the virtual arm (VA) model. In simulation experiments within the operational range of joint movements, the gamma static commands (γ(s)) to the spindles of both mono-articular and bi-articular muscles were hypothesized (1) to remain constant, (2) to be modulated with joint angles linearly, and (3) to be modulated with joint angles nonlinearly. Simulation results revealed a nonlinear landscape of Ia afferent with respect to both γ(s) activation and joint angle. Among the three hypotheses, the constant and linear strategies did not yield Ia responses that matched the experimental data, and therefore, were rejected as plausible strategies of spindle sensitivity control. However, if γ(s) commands were quadratically modulated with joint angles, a robust linear relation between Ia afferents and joint angles could be obtained in both mono-articular and bi-articular muscles. With the quadratic strategy of spindle sensitivity control, γ(s) commands may serve as the CNS outputs that inform the periphery of central coding of joint angles. The results suggest that the information of joint angles may be communicated between the CNS and muscles via the descending γ(s) efferent and Ia afferent signals.

The role of propriospinal neuronal network in transmitting the alternating muscular activities of flexor and extensor in parkinsonian tremor

It has been shown that normal cyclic movement of human arm and resting limb tremor in Parkinson's disease (PD) are associated with the oscillatory neuronal activities in different cerebral networks, which are transmitted to the antagonistic muscles via the same spinal pathway. There are mono-synaptic and multi-synaptic corticospinal pathways for conveying motor commands. This study investigates the plausible role of propriospinal neuronal (PN) network in the C3-C4 levels in multi-synaptic transmission of cortical commands for oscillatory movements. A PN network model is constructed based on known neurophysiological connections, and is hypothesized to achieve the conversion of cortical oscillations into alternating antagonistic muscle bursts. Simulations performed with a virtual arm (VA) model indicate that without the PN network, the alternating bursts of antagonistic muscle EMG could not be reliably generated, whereas with the PN network, the alternating pattern of bursts were naturally displayed in the three pairs of antagonist muscles. Thus, it is suggested that oscillations in the primary motor cortex (M1) of single and double tremor frequencies are processed at the PN network to compute the alternating burst pattern in the flexor and extensor muscles.

BIOMECHANICAL STABILITY ANALYSIS OF THE λ-MODEL CONTROLLING ONE JOINT

Computer modeling and control of the human motor system might be helpful for understanding the mechanism of human motor system and for the diagnosis and treatment of neuromuscular disorders. In this paper, a brief view of the equilibrium point hypothesis for human motor system modeling is given, and the λ-model derived from this hypothesis is studied. The stability of the λ-model based on equilibrium and Jacobian matrix is investigated. The results obtained in this paper suggest that the λ-model is stable and has a unique equilibrium point under certain conditions.

DESIGN OF A THRESHOLD FES CONTROL SYSTEM FOR ARM MOVEMENT

The existing functional electrical stimulation (FES) techniques often required to solve the complex "inverse dynamic problem" to calculate the muscle torques for moving along a desired trajectory. According to the threshold control theory of voluntary motor control, a bio-mimetic threshold control strategy for the FES controller is designed and tested in the human arm movement. The arm is modeled as three segments connected by two hinges joints. The movement is driven by seven muscles and limited in the horizontal plane. All muscles are described by a modified Hill-type muscle model. Simulation results suggest that the threshold FES control system can realize point to point movement and can approximately follow the desired traces in presence of feedback delays up to 20 ms. The movement can also maintain stability under external perturbation or external load. The control system can be employed in clinical application because of the following advantages: (1) The control strategy includes some mature control techniques which had been realized in hardware. (2) Only sophisticated sensors of goniometer and the surface electrodes are needed to provide feedbacks and muscle stimulation. (3) The performance of the control system will not be critically influenced by the slight change of musculo-tendon parameters and feedback delays, and even the parameters of controller are fixed.

An EMG-driven musculoskeletal model for the estimation of biomechanical parameters of wrist flexors

A musculoskeletal model of wrist flexors comprising musculoskeletal dynamics and limb anatomy was experimentally validated with healthy subjects during maximum voluntary contractions. Electromyography signals recorded from flexors were used as input, while measured torques exerted by the hand were compared to the torques predicted by the model. The root mean square error and the normalized root mean square error calculated during estimation and validation phases were compared. In total, six subject-specific musculoskeletal parameters were estimated, while biomechanical indexes such as the operating range of the flexors, the stiffness of the wrist flexion musculotendon actuators, and the contribution of the muscle fibers to the joint moment were computed. Results are in agreement with previously published data.

Spinal-like regulator facilitates control of a two-degree-of-freedom wrist

The performance of motor tasks requires the coordinated control and continuous adjustment of myriad individual muscles. The basic commands for the successful performance of a sensorimotor task originate in "higher" centers such as the motor cortex, but the actual muscle activation and resulting torques and motion are considerably shaped by the integrative function of the spinal interneurons. The relative contributions of brain and spinal cord are less clear for reaching movements than for automatic tasks such as locomotion. We have modeled a two-axis, four-muscle wrist joint with realistic musculoskeletal mechanics and proprioceptors and a network of regulatory circuitry based on the classical types of spinal interneurons (propriospinal, monosynaptic Ia-excitatory, reciprocal Ia-inhibitory, Renshaw inhibitory, and Ib-inhibitory pathways) and their supraspinal control (via biasing activity, presynaptic inhibition, and fusimotor gain). The modeled system has a very large number of control inputs, not unlike the real spinal cord that the brain must learn to control to produce desired behaviors. It was surprisingly easy to program this model to emulate actual performance in four very different but well described behaviors: (1) stabilizing responses to force perturbations; (2) rapid movement to position target; (3) isometric force to a target level; and (4) adaptation to viscous curl force fields. Our general hypothesis is that, despite its complexity, such regulatory circuitry substantially simplifies the tasks of learning and producing complex movements.

Dynamic simulation of perturbation responses in a closed-loop virtual arm model

A closed-loop virtual arm (VA) model has been developed in SIMULINK environment by adding spinal reflex circuits and propriospinal neural networks to the open-loop VA model developed in early study [1]. An improved virtual muscle model (VM4.0) is used to speed up simulation and to generate more precise recruitment of muscle force at low levels of muscle activation. Time delays in the reflex loops are determined by their synaptic connections and afferent transmission back to the spinal cord. Reflex gains are properly selected so that closed-loop responses are stable. With the closed-loop VA model, we are developing an approach to evaluate system behaviors by dynamic simulation of perturbation responses. Joint stiffness is calculated based on simulated perturbation responses by a least-squares algorithm in MATLAB. This method of dynamic simulation will be essential for further evaluation of feedforward and reflex control of arm movement and position.

Computationally efficient models of neuromuscular recruitment and mechanics

We have improved the stability and computational efficiency of a physiologically realistic, virtual muscle (VM 3.*) model (Cheng et al 2000 J. Neurosci. Methods 101 117-30) by a simpler structure of lumped fiber types and a novel recruitment algorithm. In the new version (VM 4.0), the mathematical equations are reformulated into state-space representation and structured into a CMEX S-function in SIMULINK. A continuous recruitment scheme approximates the discrete recruitment of slow and fast motor units under physiological conditions. This makes it possible to predict force output during smooth recruitment and derecruitment without having to simulate explicitly a large number of independently recruited units. We removed the intermediate state variable, effective length (Leff), which had been introduced to model the delayed length dependency of the activation-frequency relationship, but which had little effect and could introduce instability under physiological conditions of use. Both of these changes greatly reduce the number of state variables with little loss of accuracy compared to the original VM. The performance of VM 4.0 was validated by comparison with VM 3.1.5 for both single-muscle force production and a multi-joint task. The improved VM 4.0 model is more suitable for the analysis of neural control of movements and for design of prosthetic systems to restore lost or impaired motor functions. VM 4.0 is available via the internet and includes options to use the original VM model, which remains useful for detailed simulations of single motor unit behavior.

Model-based sensorimotor integration for multi-joint control: development of a virtual arm model

An integrated, sensorimotor virtual arm (VA) model has been developed and validated for simulation studies of control of human arm movements. Realistic anatomical features of shoulder, elbow and forearm joints were captured with a graphic modeling environment, SIMM. The model included 15 musculotendon elements acting at the shoulder, elbow and forearm. Muscle actions on joints were evaluated by SIMM generated moment arms that were matched to experimentally measured profiles. The Virtual Muscle (VM) model contained appropriate admixture of slow and fast twitch fibers with realistic physiological properties for force production. A realistic spindle model was embedded in each VM with inputs of fascicle length, gamma static (gamma(stat)) and dynamic (gamma(dyn)) controls and outputs of primary (I(a)) and secondary (II) afferents. A piecewise linear model of Golgi Tendon Organ (GTO) represented the ensemble sampling (I(b)) of the total muscle force at the tendon. All model components were integrated into a Simulink block using a special software tool. The complete VA model was validated with open-loop simulation at discrete hand positions within the full range of alpha and gamma drives to extrafusal and intrafusal muscle fibers. The model behaviors were consistent with a wide variety of physiological phenomena. Spindle afferents were effectively modulated by fusimotor drives and hand positions of the arm. These simulations validated the VA model as a computational tool for studying arm movement control. The VA model is available to researchers at website http://pt.usc.edu/cel .

Stability analysis of the lambda-model during fast elbow movements

In control, stability insures the reproducibility of motions and the robustness to external and internal perturbations. In this short paper, the stability of the lambda-model during fast elbow movements is analyzed, and the operation regions of the unmeasurably descending commands are calculated to guarantee the stabilizing ability of the motor control system. In this system, the elbow is modeled as a pair of antagonist muscles around the hinge joint in the horizontal plane. Both extensor and flexor muscles are described by a Hill-type muscle model. The muscle activation is produced by the physiological lambda version of the equilibrium point hypothesis (EPH). Conditions for global stability are calculated analytically by the Lyapunov theory and contraction theory. The results suggest that to guarantee the stability, the descending commands R, C and mu from central nervous system (CNS) must be tuned to the muscle properties, the muscle geometry and the geometric properties of the linkage system.

Building a realistic neuronal model that simulates multi-joint arm and hand movements in 3D space

The question as to how the brain controls voluntary movements of the arm and hand still remains largely unsolved despite much research focused on behavioral studies, neurophysiological investigations, and neuronal modeling in computer science. This is because behavioral studies are usually performed without detailed knowledge of the underlying neuronal networks, neurophysiological studies often lack an understanding of the function, and neuronal models are frequently focused on a particular control problem with restricted knowledge of the underlying neuronal networks involved. Therefore, it seems appropriate to start by trying to integrate knowledge of neuronal networks with known function and computer based neuronal models to seek more realistic models that can better control robots or artificial limbs and hands. We propose to combine knowledge of a behavioral model for reaching with the hand toward an object, which is based on detailed knowledge of the underlying neuronal network, and a neuronal model that includes several functional levels, from the planning level via intermediate levels to the final level of control of motoneurons and muscles.

Modeling spinal sensorimotor control for reach task

The spinal sensorimotor control system executes movement instructions from the central controller in the brain that plans the task in terms of global requirements. Spinal circuits serve as a local regulator that tunes the neuromuscular apparatus to an optimal state for task execution. We hypothesize that reach tasks are controlled by a set of feedforward and feedback descending commands for trajectory and final posture, respectively. This paper presents the use of physiologically realistic models of the spinal sensorimotor system to demonstrate the feasibility of such dual control for reaching movements.