A model of the neuro-musculo-skeletal system for human locomotion

The generation of human locomotion was examined by linking computational neuroscience with biomechanics from the perspective of nonlinear dynamical theory. We constructed a model of human locomotion, which includes a musculo-skeletal system with 8 segments and 20 muscles, a neural rhythm generator composed of 7 pairs of neural oscillators, and mechanisms for processing and transporting sensory and motor signals. Using a computer simulation, we found that locomotion emerged as a stable limit cycle that was generated by the global entrainment between the musculo-skeletal system, the neural system, and the environment. Moreover, the walking movements of the model could be compared quantitatively with those of experimental studies in humans.Part of this paper was presented to IVth International Symposium on Computer Simulation in Biomechanics, Paris, France, July 1, 1993     

Generation of human bipedal locomotion by a bio-mimetic neuro-musculo-skeletal model

To emulate the actual neuro-control mechanism of human bipedal locomotion, an anatomically and physiologically based neuro-musculo-skeletal model is developed. The human musculo-skeletal system is constructed as seven rigid links in a sagittal plane, with a total of nine principal muscles. The nervous system consists of an alpha motoneuron and proprioceptors such as a muscle spindle and a Golgi tendon organ for each muscle. At the motoneurons, feedback signals from the proprioceptors are integrated with the signal induced by foot–ground contact and input from the rhythm pattern generator; a muscle activation signal is produced accordingly. Weights of connection in the neural network are optimized using a genetic algorithm, thus maximizing walking distance and minimizing energy consumption. The generated walking pattern is in remarkably good agreement with that of actual human walking, indicating that the locomotory pattern could be generated automatically, according to the musculo-skeletal structures and the connections of the peripheral nervous system, particularly due to the reciprocal innervation in the muscle spindles. Using the proposed model, the flow of sensory-motor information during locomotion is estimated and a possible neuro-control mechanism is discussed. Received: 03 December 1998 / Accepted in revised form: 09 June 2000     

A model of neuro-musculo-skeletal system for human locomotion under position constraint condition

The human locomotion was studied on the basis of the interaction of the musculo-skeletal system, the neural system and the environment. A mathematical model of human locomotion under position constraint condition was established. Besides the neural rhythm generator, the posture controller and the sensory system, the environment feedback controller and the stability controller were taken into account in the model. The environment feedback controller was proposed for two purposes, obstacle avoidance and target position control of the swing foot. The stability controller was proposed to imitate the self-balancing ability of a human body and improve the stability of the model. In the stability controller, the ankle torque was used to control the velocity of the body gravity center. A prediction control algorithm was applied to calculate the torque magnitude of the stability controller. As an example, human stairs climbing movement was simulated and the results were given. The simulation result proved that the mathematical modeling of the task was successful.     

A model of the neuro-musculo-skeletal system for anticipatory adjustment of human locomotion during obstacle avoidance

Theoretical studies on human locomotion have shown that a stable and flexible gait emerges from the dynamic interaction between the rhythmic activity of a neural system composed of a neural rhythm generator (RG) and the rhythmic movement of the musculo-skeletal system. This study further explores the mechanism of the anticipatory control of locomotion based on the emergent properties of a neural system that generates the basic pattern of gait. A model of the neuro-musculo-skeletal system to execute the task of stepping over a visible obstacle with both limbs during walking is described. The RG in the neural system was combined with a system referred to as a discrete movement generator (DM), which receives both the output of the RG and visual information regarding the obstacle and generates discrete signals for modification of the basic gait pattern. A series of computer simulations demonstrated that an obstacle placed at an arbitrary position can be cleared by sequential modifications of gait: (1) modulating the step length when approaching the obstacle and (2) modifying the trajectory of the swing limbs while stepping over it. This result suggests that anticipatory adjustments are produced not by the unidirectional flow of the information from visual signals to motor commands but by the bi-directional circulation of information between the DM and the RG. The validity of this model is discussed in relation to motor cortical activity during anticipatory modifications in cats and the ecological psychology of visuo-motor control in humans. Received: 19 September 1996 / Accepted in revised form: 21 March 1997     

A control theoretic model of the forearm

In this paper, a control theoretic model of the forearm is developed and analyzed, and a computational method for predicting muscle activations necessary to generate specified motions is described. A detailed geometric model of the forearm kinematics, including the carrying angle and models of how the biceps and the supinator tendons wrap around the bones, is used. Also, including a dynamics model, the final model is a system of differential equations where the muscle activations play the role of control signals. Due to the large number of muscles, the problem of finding muscle activations is redundant, and this problem is solved by an optimization procedure. The computed muscle activations for ballistic movements clearly recaptures the triphasic ABC (Activation-Braking-Clamping) pattern. It is also transparent, from the muscle activation patterns, how the muscles cooperate and counteract in order to accomplish desired motions. A comparison with previously reported experimental data is included and the model predictions can be seen to be partially in agreement with the experimental data.     

A rigid body model of the forearm

In this article the forearm, with its complex, continuous motion of masses during pronation/supination, was approximated by a rigid body model consisting of a radial segment rotating around an ulnar segment. The method used to obtain the model parameters is based on three-dimensional voxel data that include velocity information. We propose a criterion that allows the voxels to be attributed to either of the two segments. It is based on the notion that the rotational kinetic energy determined from the voxel data equals the kinetic energy of the rigid body model. To obtain a three-dimensional smoothing we further propose a parameterization of the shape of both segments. These shapes can then be used to determine the dynamic integrals of the segments, i.e. mass, center of mass, and inertia. Using this approach we determined all model parameters for a human forearm from three series of MRI scans in a supinated, a pronated, and an intermediate position. In the appendix, a procedure is described that allows the dynamic quantities to be scaled homogeneously without recalculation of the integrals. Thus, this article provides all essential parameters required for three-dimensional dynamic simulations of general movements of the forearm.     

A dynamic model of the forearm including fatigue

A biomechanical model of the forearm, consisting of 61 muscle-tendon systems or tendons and 8 sections, is presented. The model can be used to calculate the muscle forces when resultant of the external forces and the motion is known. Calculations are based on constraints of muscle forces, joint forces, contact forces, and tendon junctions, and a load sharing principle telling which of the feasible solutions are likely and which are not. Fatigue is accounted for by updating the upper limits of the muscle forces according to the loading history. As an example, the model is used to predict the load sharing between the fingers when they are pressed against a table with a given total force.     

Activation-threshold tuning in an affinity model for the T-cell repertoire

Naive T cells respond to peptides from foreign proteins and remain tolerant to self peptides from endogenous proteins. It has been suggested that self tolerance comes about by a 'tuning' mechanism, i.e. by increasing the T-cell activation threshold upon interaction with self peptides. Here, we explore how such an adaptive mechanism of T-cell tolerance would influence the reactivity of the T-cell repertoire to foreign peptides. We develop a computer simulation model in which T cells are tolerized by increasing their activation-threshold dependent on the affinity with which they see self peptides presented in the thymus. Thus, different T cells acquire different activation thresholds (i.e. different cross-reactivities). In previous mathematical models, T-cell tolerance was deletional and based on a fixed cross-reactivity parameter, which was assumed to have evolved to an optimal value. Comparing these two different tolerance-induction mechanisms, we found that the tuning model performs somewhat better than an optimized deletion model in terms of the reactivity to foreign antigens. Thus, evolutionary optimization of clonal cross-reactivity is not required. A straightforward extension of the tuning model is to delete T-cell clones that obtain a too high activation threshold, and to replace these by new clones. The reactivity of the immune repertoires of such a replacement model is enchanced compared with the basic tuning model. These results demonstrate that activation-threshold tuning is a functional mechanism for self tolerance induction.     

A spherical model for orientation and spatial-frequency tuning in a cortical hypercolumn

A theory is presented of the way in which the hypercolumns in primary visual cortex (V1) are organized to detect important features of visual images, namely local orientation and spatial-frequency. Given the existence in V1 of dual maps for these features, both organized around orientation pinwheels, we constructed a model of a hypercolumn in which orientation and spatial-frequency preferences are represented by the two angular coordinates of a sphere. The two poles of this sphere are taken to correspond, respectively, to high and low spatial-frequency preferences. In Part I of the paper, we use mean-field methods to derive exact solutions for localized activity states on the sphere. We show how cortical amplification through recurrent interactions generates a sharply tuned, contrast-invariant population response to both local orientation and local spatial frequency, even in the case of a weakly biased input from the lateral geniculate nucleus (LGN). A major prediction of our model is that this response is non-separable with respect to the local orientation and spatial frequency of a stimulus. That is, orientation tuning is weaker around the pinwheels, and there is a shift in spatial-frequency tuning towards that of the closest pinwheel at non-optimal orientations. In Part II of the paper, we demonstrate that a simple feed-forward model of spatial-frequency preference, unlike that for orientation preference, does not generate a faithful representation when amplified by recurrent interactions in V1. We then introduce the idea that cortico-geniculate feedback modulates LGN activity to generate a faithful representation, thus providing a new functional interpretation of the role of this feedback pathway. Using linear filter theory, we show that if the feedback from a cortical cell is taken to be approximately equal to the reciprocal of the corresponding feed-forward receptive field (in the two-dimensional Fourier domain), then the mismatch between the feed-forward and cortical frequency representations is eliminated. We therefore predict that cortico-geniculate feedback connections innervate the LGN in a pattern determined by the orientation and spatial-frequency biases of feed-forward receptive fields. Finally, we show how recurrent cortical interactions can generate cross-orientation suppression.     

Non-identifiability of the Rayleigh damping material model in Magnetic Resonance Elastography

Magnetic Resonance Elastography (MRE) is an emerging imaging modality for quantifying soft tissue elasticity deduced from displacement measurements within the tissue obtained by phase sensitive Magnetic Resonance Imaging (MRI) techniques. MRE has potential to detect a range of pathologies, diseases and cancer formations, especially tumors. The mechanical model commonly used in MRE is linear viscoelasticity (VE). An alternative Rayleigh damping (RD) model for soft tissue attenuation is used with a subspace-based nonlinear inversion (SNLI) algorithm to reconstruct viscoelastic properties, energy attenuation mechanisms and concomitant damping behavior of the tissue-simulating phantoms. This research performs a thorough evaluation of the RD model in MRE focusing on unique identification of RD parameters, _μI${\mu }_I$ and _ρI${\rho }_I$.     

A geometric model of orientation tuning dynamics in visual neurons]

A geometrical model for the dynamics of orientation tuning of visual neurones was proposed, which makes it possible to study the dynamics of configuration, localization, and weight of excitatory and inhibitory subzones of the receptive field. The model reproduces typical patterns of orientation tuning dynamics, observed in neurophysiological experiments on cat visual cortex neurones. The parameters of the model (size and mutual position of excitatory and inhibitory zones of the receptive field, their weight, and dynamics type) were estimated that correspond to the main types of orientation tuning dynamics in natural conditions. It is shown that selective and acute tuning of neurones can be formed and/or sharpened by intracotrical inhibition, while the dynamics of preferred orientation is due to changes in the geometry of the inhibitory subzone of the receptive field.     

A new kinematic model of pro- and supination of the human forearm

We introduce a new kinematic model describing the motion of the human forearm bones, ulna and radius, during forearm rotation. During this motion between the two forearm extrem-positions, referred to as supination (palm up) and pronation (palm down), effects occur, that cannot be explained by the the established kinematic model of R. Fick from 1904. Especially, the motion of the ulna is not properly reproduced by Fick's model. During forearm rotation an evasive motion of the ulna is observed by various authors, using magnetic resonance imaging MRI) technology. Our new kinematic model also simulates this evasive motion. Furthermore, the model is enlarged to include angulations of the forearm bones. Using these results the influence of forearm fractures on the range of forearm motion can be predicted. This knowledge can be used by surgeons to choose the optimal therapy in re-establishing free forearm mobility.     

Parameter based tuning model for optimizing performance on GPU

Recently, the graphic processing units (GPUs) are becoming increasingly popular for the high performance computing applications. Although the GPUs provide high peak performance, exploiting the full performance potential for application programs, however, leaves a challenging task to the programmers. When launching a parallel kernel of an application on the GPU, the programmer needs to carefully select the number of blocks (grid size) and the number of threads per block (block size). These values determine the degree of SIMD parallelism and the multithreading, and greatly influence the performance. With a huge range of possible combinations of these values, choosing the right grid size and the block size is not straightforward. In this paper, we propose a mathematical model for tuning the grid size and the block size based on the GPU architecture parameters. Using our model we first calculate a small set of candidate grid size and block size values, then search for the optimal values out of the candidate values through experiments. Our approach significantly reduces the potential search space instead of exhaustive search approaches in the previous research. Thus our approach can be practically applied to the real applications.     

Resonance energy transfer study using a rhenium metal-ligand lipid conjugate as the donor in a model membrane.

We measured steady state and time-resolved resonance energy transfer between donors and acceptors in model membranes. The donor was a long lifetime rhenium-lipid complex, which displayed a mean lifetime of 1 microsecond and lifetime components as long as 3 microseconds in the labeled DOPC membranes. The transfer efficiencies were found to be substantially larger than those predicted without consideration of lateral diffusion. The larger transfer efficiencies are consistent with a mutual lateral diffusion coefficient in the membrane near 2 x 10(-8) cm2/s. These results demonstrate that lateral diffusion in membranes can be detected with microsecond lipid probes.     

Simulation model of the tuning mechanism in the visual cortex neuron for the perception of Y-shape images

We performed an imitation simulation of receptive fields (RF) of cat cortical neurons in the primary visual cortex, which were able to detect symmetrical and asymmetrical Y-like figures. We investigated the models of the receptive fields of neurons sensitive to Y-like figures through either the convergence from half-bar detectors or disinhibition mechanism. The model of an of the receptive fields of neurons sensitive to Y-like figures through either the convergence from half-bar detectors or disinhibition mechanism. The model of an-like figure detector on the basis of convergence from the angle and orientation detectors was advanced. Tuning of the simulated receptive fields to Y-like figures was compared with their tuning to cross-like figures. It was shown that the detectors of asymmetric Y-like figures are also detectors of a cross, whereas the detectors of symmetric Y-like figures are more sensitive to Y-like figures than to crosses. The features of the model critical for sensitivity to Y-like figures (the shape, localization, and weight of the RF zones) were specified.     

Vascular dominance in the forearm

The dominance of the radial or ulnar artery at the forearm level and their contributions to the circulation of the hand remain a matter of contention. Therefore, the authors proposed to investigate the predominance of one of these arteries first by anatomic studies on 40 fresh cadaver upper extremities, and then by dynamic studies. The dynamic studies included color Doppler sonography in 22 individuals (44 hands) and five-channel plethysmography in 40 individuals (40 right hands). It was found that the ulnar artery is dominant at the elbow, but after originating its collateral branches, the radial artery becomes the dominant artery in the distal forearm and, consequently, constitutes the major source of vascularization to the hand. The ulnar artery is rarely dominant at the forearm level and is physiologically less important. Therefore, there is no hemodynamic reason to prefer the radial artery to the ulnar artery for any invasive maneuvers.     

Venous emptying mediates a transient vasodilation in the human forearm

We tested the hypothesis that venous emptying serves as a stimulus for vasodilation in the human forearm. We compared the forearm blood flow (FBF; pulsed Doppler mean blood velocity and echo Doppler brachial artery diameter) response to temporary elevation of a resting forearm from below to above heart level when venous volume was allowed to drain versus when venous drainage was prevented by inflation of an upper arm cuff to approximately 30 mmHg. Arm elevation resulted in a rapid reduction in venous volume and pressure. Cuff inflation just before elevation effectively prevented these changes. FBF was briefly reduced by approximately 16% following arm elevation. A transient (86%) increase in blood flow began by approximately 5 s of arm elevation and peaked by 8 s, indicating a vasodilation. This response was completely abolished by preventing venous emptying. Arterial inflow below heart level was markedly elevated by 343% following brief (4 s) forearm elevation. This hyperemia was minor when venous emptying during forearm elevation had been prevented. We conclude that venous emptying serves as a stimulus for a transient (within 10 s) vasodilation in vivo. This vasodilation can substantially elevate arterial inflow.