Biomedical subjects

M Kawato

Publications and source records attributed to M Kawato.

At least 19 recordsLinked to original sources

PubMed2001

The central nervous system stabilizes unstable dynamics by learning optimal impedance.

To manipulate objects or to use tools we must compensate for any forces arising from interaction with the physical environment. Recent studies indicate that this compensation is achieved by learning an internal model of the dynamics, that is, a neural representation of the relation between motor command and movement. In these studies interaction with the physical environment was stable, but many common tasks are intrinsically unstable. For example, keeping a screwdriver in the slot of a screw is unstable because excessive force parallel to the slot can cause the screwdriver to slip and because misdirected force can cause loss of contact between the screwdriver and the screw. Stability may be dependent on the control of mechanical impedance in the human arm because mechanical impedance can generate forces which resist destabilizing motion. Here we examined arm movements in an unstable dynamic environment created by a robotic interface. Our results show that humans learn to stabilize unstable dynamics using the skillful and energy-efficient strategy of selective control of impedance geometry.

Adaptation, Physiological↗

PubMed2001

Exploration of signal transduction pathways in cerebellar long-term depression by kinetic simulation.

Because multiple molecular signal transduction pathways regulate cerebellar long-term depression (LTD), which is thought to be a possible molecular and cellular basis of cerebellar learning, the systematic relationship between cerebellar LTD and the currently known signal transduction pathways remains obscure. To address this issue, we built a new diagram of signal transduction pathways and developed a computational model of kinetic simulation for the phosphorylation of AMPA receptors, known as a key step for expressing cerebellar LTD. The phosphorylation of AMPA receptors in this model consists of an initial phase and an intermediate phase. We show that the initial phase is mediated by the activation of linear cascades of protein kinase C (PKC), whereas the intermediate phase is mediated by a mitogen-activated protein (MAP) kinase-dependent positive feedback loop pathway that is responsible for the transition from the transient phosphorylation of the AMPA receptors to the stable phosphorylation of the AMPA receptors. These phases are dually regulated by the PKC and protein phosphatase pathways. Both phases also require nitric oxide (NO), although NO per se does not show any ability to induce LTD; this is consistent with a permissive role as reported experimentally (Lev-Ram et al., 1997). Therefore, the kinetic simulation is a powerful tool for understanding and exploring the behaviors of complex signal transduction pathways involved in cerebellar LTD.

Cerebellum↗

PubMed2001

Quantitative examinations for multi joint arm trajectory planning--using a robust calculation algorithm of the minimum commanded torque change trajectory.

In previous research, criteria based on optimal theories were examined to explain trajectory features in time and space in multi joint arm movement. Four criteria have been proposed. They were the minimum hand jerk criterion (by which a trajectory is planned in an extrinsic-kinematic space), the minimum angle jerk criterion (which is planned in an intrinsic-kinematic space), the minimum torque change criterion (where control objects are joint links; it is planned in an intrinsic-dynamic-mechanical space), and the minimum commanded torque change criterion (which is planned in an intrinsic space considering the arm and muscle dynamics). Which of these is proper as a criterion for trajectory planning in the central nervous system has been investigated by comparing predicted trajectories based on these criteria with previously measured trajectories. Optimal trajectories based on the two former criteria can be calculated analytically. In contrast, optimal trajectories based on the minimum commanded torque change criterion are difficult to be calculated, even with numerical methods. In some cases, they can be computed by a Newton-like method or a steepest descent method combined with a penalty method. However, for a realistic physical parameter range, the former becomes unstable quite often and the latter is unreliable about the optimality of the obtained solution. In this paper, we propose a new method to stably calculate optimal trajectories based on the minimum commanded torque change criterion. The method can obtain trajectories satisfying Euler-Poisson equations with a sufficiently high accuracy. In the method, a joint angle trajectory, which satisfies the boundary conditions strictly, is expressed by using orthogonal polynomials. The coefficients of the orthogonal polynomials are estimated by using a linear iterative calculation so as to satisfy the Euler-Poisson equations with a sufficiently high accuracy. In numerical experiments, we show that the optimal solution can be computed in a wide work space and can also be obtained in a short time compared with the previous methods. Finally, we perform supplementary examinations of the experiments by Nakano, Imamizu, Osu, Uno, Gomi, Yoshioka et al. (1999). Estimation of dynamic joint torques and trajectory formation from surface electromyography signals using a neural network model. Biological Cybernetics, 73, 291-300. Their experiments showed that the measured trajectory is the closest to the minimum commanded torque change trajectory by statistical examination of many point-to-point trajectories over a wide range in a horizontal and sagittal work space. We recalculated the minimum commanded torque change trajectory using the proposed method, and performed the same examinations as previous investigations. As a result, it could be reconfirmed that the measured trajectory is closest to the minimum commanded torque change trajectory previously reported.

Algorithms↗

PubMed2001

Change in neuronal firing patterns in the process of motor command generation for the ocular following response.

To explore the process of motor command generation for the ocular following response, we recorded the activity of single neurons in the medial superior temporal (MST) area of the cortex, the dorsolateral pontine nucleus (DLPN), and the ventral paraflocculus (VPFL) of the cerebellum of alert monkeys during ocular following elicited by sudden movements of a large-field pattern. Using second-order linear-regression models, we analyzed the quantitative relationships between neuronal firing frequency patterns and eye movements or retinal errors specified by three parameters (position, velocity, and acceleration). We first attempted to reconstruct the temporal waveform of each neuronal response to each visual stimulus and computed the coefficients for each parameter using the least-square error method for each stimulus condition. The temporal firing patterns were generally well reconstructed [coefficient of determination index (CD) > 0.7] from either the retinal error or the associated ocular following response. In the MST and DLPN datasets, however, the fit with the retinal error model was generally better than with the eye-movement model, and the estimated coefficients of acceleration and velocity ranged widely, indicating that temporal patterns in these regions showed considerable diversity. The acceleration component is greater in MST and DLPN than in VPFL, suggesting that an integration occurs in this pathway. When we determined how well the temporal patterns of the neuronal responses of a given cell could be reconstructed for all visual stimuli using a single set of coefficients, good fits were found only for Purkinje cells (P- cells) in the VPFL using the eye-movement model. In these cases, the coefficients of acceleration and velocity for each cell were similar, and the mean ratio of the acceleration and velocity coefficients was close to that of motor neurons. These results indicate that individual MST and DLPN neurons are each encoding some selective aspects of the sensory stimulus (visual motion), whereas the P-cells in VPFL are encoding the complete dynamic command signals for the associated motor response (ocular following). We conclude that the sensory-to-motor transformation for the ocular following response occurs at the P-cells in VPFL.

Animals↗

PubMed2001

Mosaic model for sensorimotor learning and control.

Humans demonstrate a remarkable ability to generate accurate and appropriate motor behavior under many different and often uncertain environmental conditions. We previously proposed a new modular architecture, the modular selection and identification for control (MOSAIC) model, for motor learning and control based on multiple pairs of forward (predictor) and inverse (controller) models. The architecture simultaneously learns the multiple inverse models necessary for control as well as how to select the set of inverse models appropriate for a given environment. It combines both feedforward and feedback sensorimotor information so that the controllers can be selected both prior to movement and subsequently during movement. This article extends and evaluates the MOSAIC architecture in the following respects. The learning in the architecture was implemented by both the original gradient-descent method and the expectation-maximization (EM) algorithm. Unlike gradient descent, the newly derived EM algorithm is robust to the initial starting conditions and learning parameters. Second, simulations of an object manipulation task prove that the architecture can learn to manipulate multiple objects and switch between them appropriately. Moreover, after learning, the model shows generalization to novel objects whose dynamics lie within the polyhedra of already learned dynamics. Finally, when each of the dynamics is associated with a particular object shape, the model is able to select the appropriate controller before movement execution. When presented with a novel shape-dynamic pairing, inappropriate activation of modules is observed followed by on-line correction.

Brain↗

PubMed2000

Human cerebellar activity reflecting an acquired internal model of a new tool.

Theories of motor control postulate that the brain uses internal models of the body to control movements accurately. Internal models are neural representations of how, for instance, the arm would respond to a neural command, given its current position and velocity. Previous studies have shown that the cerebellar cortex can acquire internal models through motor learning. Because the human cerebellum is involved in higher cognitive function as well as in motor control, we propose a coherent computational theory in which the phylogenetically newer part of the cerebellum similarly acquires internal models of objects in the external world. While human subjects learned to use a new tool (a computer mouse with a novel rotational transformation), cerebellar activity was measured by functional magnetic resonance imaging. As predicted by our theory, two types of activity were observed. One was spread over wide areas of the cerebellum and was precisely proportional to the error signal that guides the acquisition of internal models during learning. The other was confined to the area near the posterior superior fissure and remained even after learning, when the error levels had been equalized, thus probably reflecting an acquired internal model of the new tool.

Adult↗

PubMed2000

Motor dynamics encoding in the rostral zone of the cat cerebellar flocculus during vertical optokinetic eye movements.

The complex spike (CS) and simple spike (SS) activities of Purkinje cells in the rostral zone of the cerebellar flocculus were recorded in alert cats during optokinetic responses (OKR) elicited by a stimulus sequence consisting of a constant-speed visual pattern movement in one direction for 1 s and then in the opposite direction for 1 s. The quick-phase-free trials were selected. Ninety-eight cells were identified as rostral zone cells by the direction-selective CS activity that was modulated during vertical but not horizontal stimuli. In most of the majority population (88 cells), with an increasing CS firing rate during upward OKR and an increasing SS rate during downward OKR, the inverse dynamics approach was successful and the time course of the SS rate was reconstructed (mean coefficient of determination, 0.70 and 0.72 during upward and downward stimuli, respectively) by a linear weighted superposition of the eye acceleration, velocity, position, and constant terms, at a given time delay (mean 10 ms) from the unit response to the eye-movement response. Standard regression coefficient (SRC) analysis revealed that the contribution of the velocity term (mean SRC 0.98 for upward and 0.80 for downward) to regression was dominant over acceleration (mean SRC 0.018 and 0.058) and position (-0.14 and -0.12) terms. The velocity coefficient during upward stimuli (6.6 spikes/s per degree/s) was significantly (P<0.01) larger than that during downward stimuli (4.9 spikes/s per degree/s). In most of the minority population (10 cells), with both CS and SS firing rates increasing during upward OKR, the inverse dynamics approach was not successful. It is concluded that 1) in the cat rostral zone Purkinje cells, in which the preferred direction is upward for CS and downward for SS, eye velocity and acceleration information is encoded in SS firing to counteract the viscosity and inertia forces, respectively, on the eye during vertical OKR; 2) the eye position information encoded in SS firing is inappropriate for counteracting the elastic force; 3) encoding of eye velocity information during upward OKR is quantitatively different from that during downward OKR: SS firing modulation is larger for upward than for downward OKR of the same amplitude; and 4) encoding of motor dynamics is obscure in cells in which the preferred direction is upward for both CS and SS.

Acceleration↗

PubMed2000

A method for measuring endpoint stiffness during multi-joint arm movements.

Current methods for measuring stiffness during human arm movements are either limited to one-joint motions, or lead to systematic errors. The technique presented here enables a simple, accurate and unbiased measurement of endpoint stiffness during multi-joint movements. Using a computer-controlled mechanical interface, the hand is displaced relative to a prediction of the undisturbed trajectory. Stiffness is then computed as the ratio of restoring force to displacement amplitude. Because of the accuracy of the prediction (< 1 cm error after 200 ms) and the quality of the implementation, the movement is not disrupted by the perturbation. This technique requires only 13 as many trials to identify stiffness as the method of Gomi and Kawato (1997, Biological Cybernetics 76, 163-171) and may, therefore, be used to investigate the evolution of stiffness during motor adaptation.

Arm↗

PubMed2000

A mathematical analysis of the characteristics of the system connecting the cerebellar ventral paraflocculus and extraoculomotor nucleus of alert monkeys during upward ocular following responses.

Movements of the visual scene evoke short-latency ocular-following-responses (OFR). Many studies suggest that a neural pathway containing the cerebellar-ventral-paraflocculus (VPFL) mediates OFR. The relationship between eye movement and simple-spike firing in the VPFL during OFR has been studied in detail using an inverse dynamics approach. The relationship between eye movement and cell firing in the extraoculomotor nucleus (MN) has already been reported. However, no studies have examined the information transformation that occurs between the VPFL and the MN during OFR. In this paper, using an inverse dynamics approach, we derive a transfer function that represents the characteristics of the structure connecting the VPFL and the MN during upward OFR. This structure appears to contain a kind of neural integrator, which constructs eye-velocity-and-position information from eye-acceleration-and-velocity information. We propose a diagram for the neural integration commonly at work during all types of upward eye movement. This is a closed-loop circuit containing a low-pass filter. The low-pass filter can construct eye-velocity-and-position information from an eye-acceleration-velocity-position command similar to the final motor command used commonly for all upward eye movements. Anatomical and electrophysiological data suggest that the vestibular nuclei-interstitial nucleus of Cajal-vestibular nuclei loop might perform such neural integration.

Action Potentials↗

PubMed2000

Purkinje cell activity in the middle zone of the cerebellar flocculus during optokinetic and vestibular eye movement in cats.

Based on the inverse dynamics theory, a previous paper reconstructed simple-spike (SS) firing rates of Purkinje cells in the cat's flocculus middle-zone by a linear-weighted summation of eye acceleration, velocity, and position during optokinetic response (OKR). The present study investigated the SS rates during combined optokinetic and vestibular stimuli of the cells recorded in the previous paper. During the sinusoidal vestibuloocular reflex (VOR) in the light (VORL) and in the dark (VORD) the firing modulation was small. During VOR suppression (VORS) by head and visual-pattern rotation in the same direction, the modulation was deep, with the peak coinciding roughly with peak ipsiversive head velocity. During VOR enhancement (VORE), the modulation was deep, with the peak coinciding roughly with peak contraversive head velocity. If we interpret these data in relation to eye and head movements, the cells in the cat were comparable to the horizontal-gaze-velocity Purkinje cells in the monkey that encode a linear summation of eye and head velocity signals. Alternatively, if we interpret the data on the basis of the inverse dynamics theory, the SS rates during VORL, VORS, and VORE were well-fitted by the OKR components of the movements (subtraction of VORD from VORL, VORS, and VORE eye movements, respectively), but not by the whole movements, using the coefficients calculated during OKR. It is concluded that the data are interpretable by both theories when the VOR gain (eye movement/head movement) is close to 1 and the firing is dominated by eye velocity information.

Action Potentials↗

PubMed1999

Composition and decomposition of internal models in motor learning under altered kinematic and dynamic environments.

The learning process of reaching movements was examined under novel environments whose kinematic and dynamic properties were altered. We used a kinematic transformation (visuomotor rotation), a dynamic transformation (viscous curl field), and a combination of these transformations. When the subjects learned the combined transformation, reaching errors were smaller if the subject first learned the separate kinematic and dynamic transformations. Reaching errors under the kinematic (but not the dynamic) transformation were smaller if subjects first learned the combined transformation. These results suggest that the brain learns multiple internal models to compensate for each transformation and has some ability to combine and decompose these internal models as called for by the occasion.

Adult↗

PubMed1999

Cerebro-cerebellar functional connectivity revealed by the laterality index in tool-use learning.

The function of the lateral part of the human cerebellum was investigated through cerebro-cerebellar functional connectivity. We propose a laterality index method to reveal a functional and possibly anatomical pathway between the cerebral cortex and the cerebellum. The brain activity involved in learning a visually-guided tracking skill using a novel computer mouse was measured by functional magnetic resonance imaging. The imaging data analyzed using the method suggest that the simple lobule and semilunar lobule of the lateral cerebellum have connections with the pars opercularis and pars triangularis in the inferior frontal gyrus. A possible function of this cerebro-cerebellar communication loop is tool usage, which is in-between the cognitive and motor functions of the human cerebellum.

Adult↗

PubMed1999

Internal models for motor control and trajectory planning.

A number of internal model concepts are now widespread in neuroscience and cognitive science. These concepts are supported by behavioral, neurophysiological, and imaging data; furthermore, these models have had their structures and functions revealed by such data. In particular, a specific theory on inverse dynamics model learning is directly supported by unit recordings from cerebellar Purkinje cells. Multiple paired forward inverse models describing how diverse objects and environments can be controlled and learned separately have recently been proposed. The 'minimum variance model' is another major recent advance in the computational theory of motor control. This model integrates two furiously disputed approaches on trajectory planning, strongly suggesting that both kinematic and dynamic internal models are utilized in movement planning and control.

Animals↗

PubMed1999

[Campylobacter fetus bacteremia and thrombophlebitis in a patient with Waldenstrom's macroglobulinemia].

We report a 67-year-old male with Waldenstrom's macroglobulinemia who developed Campylobacter fetus subspesis fetus (C. fetus) bacteremia and thrombophlebitis. The patient developed a fever and pain in his left lower limb, and could not walk because of the pain. Radioisotopic venography showed thrombophlebitis in his left lower limb. His blood culture grew C. fetus. After starting intravenous PAPM/BP, his symptoms resolved promptly. In contrast to Campylobacter jejuni which is a common cause of infectious diarrhea, C. fetus infection has distinct clinical features showing systemic illness such as bacteremia and thrombophlebitis mainly occurring in immunocompromized patients. This organism should be considered as one of the possible pathogenes in the infectious complications of the immunocompromized patients.

Aged↗

PubMed1999

Quantitative examinations of internal representations for arm trajectory planning: minimum commanded torque change model.

Quantitative examinations of internal representations for arm trajectory planning: minimum commanded torque change model. A number of invariant features of multijoint planar reaching movements have been observed in measured hand trajectories. These features include roughly straight hand paths and bell-shaped speed profiles where the trajectory curvatures between transverse and radial movements have been found to be different. For quantitative and statistical investigations, we obtained a large amount of trajectory data within a wide range of the workspace in the horizontal and sagittal planes (400 trajectories for each subject). A pair of movements within the horizontal and sagittal planes was set to be equivalent in the elbow and shoulder flexion/extension. The trajectory curvatures of the corresponding pair in these planes were almost the same. Moreover, these curvatures can be accurately reproduced with a linear regression from the summation of rotations in the elbow and shoulder joints. This means that trajectory curvatures systematically depend on the movement location and direction represented in the intrinsic body coordinates. We then examined the following four candidates as planning spaces and the four corresponding computational models for trajectory planning. The candidates were as follows: the minimum hand jerk model in an extrinsic-kinematic space, the minimum angle jerk model in an intrinsic-kinematic space, the minimum torque change model in an intrinsic-dynamic-mechanical space, and the minimum commanded torque change model in an intrinsic-dynamic-neural space. The minimum commanded torque change model, which is proposed here as a computable version of the minimum motor command change model, reproduced actual trajectories best for curvature, position, velocity, acceleration, and torque. The model's prediction that the longer the duration of the movement the larger the trajectory curvature was also confirmed. Movements passing through via-points in the horizontal plane were also measured, and they converged to those predicted by the minimum commanded torque change model with training. Our results indicated that the brain may plan, and learn to plan, the optimal trajectory in the intrinsic coordinates considering arm and muscle dynamics and using representations for motor commands controlling muscle tensions.

Adult↗

PubMed1999

Electrophysiological properties of inferior olive neurons: A compartmental model.

As a step in exploring the functions of the inferior olive, we constructed a biophysical model of the olivary neurons to examine their unique electrophysiological properties. The model consists of two compartments to represent the known distribution of ionic currents across the cell membrane, as well as the dendritic location of the gap junctions and synaptic inputs. The somatic compartment includes a low-threshold calcium current (I(Ca_l)), an anomalous inward rectifier current (I(h)), a sodium current (I(Na)), and a delayed rectifier potassium current (I(K_dr)). The dendritic compartment contains a high-threshold calcium current (I(Ca_h)), a calcium-dependent potassium current (I(K_Ca)), and a current flowing into other cells through electrical coupling (I(c)). First, kinetic parameters for these currents were set according to previously reported experimental data. Next, the remaining free parameters were determined to account for both static and spiking properties of single olivary neurons in vitro. We then performed a series of simulated pharmacological experiments using bifurcation analysis and extensive two-parameter searches. Consistent with previous studies, we quantitatively demonstrated the major role of I(Ca_l) in spiking excitability. In addition, I(h) had an important modulatory role in the spike generation and period of oscillations, as previously suggested by Bal and McCormick. Finally, we investigated the role of electrical coupling in two coupled spiking cells. Depending on the coupling strength, the hyperpolarization level, and the I(Ca_l) and I(h) modulation, the coupled cells had four different synchronization modes: the cells could be in-phase, phase-shifted, or anti-phase or could exhibit a complex desynchronized spiking mode. Hence these simulation results support the counterintuitive hypothesis that electrical coupling can desynchronize coupled inferior olive cells.

Calcium Channels↗

PubMed1999

Using motor tasks to quantitatively judge 3-D surface curvatures.

The primary objective of this study was to quantitatively investigate the human perception of surface curvature by using virtual surfaces and motor tasks along with data analysis methods to estimate surface curvature from drawing movements. Three psychophysical experiments were conducted. In Experiment 1, we looked at subjects' sensitivity to the curvature of a curve lying on a surface and changes in the curvature as defined by Euler's formula, which relates maximum and minimum principal curvatures and their directions. Regardless of direction and surface shape (elliptic and hyperbolic), subjects could report the curvature of a curve lying on a surface through a drawing task. In addition, multiple curves drawn by subjects were used to reconstruct the surface. These reconstructed surfaces could be better accounted for by analysis that treated the drawing data as a set of curvatures rather than as a set of depths. A pointing task was utilized in Experiment 2, and subjects could report principal curvature directions of a surface rather precisely and consistently when the difference between principal curvatures was sufficiently large, but performance was poor for the direction of zero curvature (asymptotic direction) on a hyperbolic surface. In Experiment 3, it was discovered that sensitivity to the sign of curvature was different for perceptual judgments and motor responses, and there was also a difference for that of a curve itself and the same curve embedded in a surface. These findings suggest that humans are sensitive to relative changes in curvature and are able to comprehend quantitative surface curvature for some motor tasks.

Adult↗

PubMed1998

Internal models for motor control.

The process of moving the hand to a target in space involves a series of sensorimotor transformations that translate visual and other sensory information about the location of the target object and the limbs into a set of motor commands that will bring the hand to the desired position. Recent work at various laboratories has provided strong support for the hypothesis that the CNS learns and maintains internal models of sensorimotor transformations. An internal model is a neural system that mimics the behaviour of the sensorimotor system and objects in the external environment. Internal models enable the CNS to predict the consequences of motor commands and to determine the motor commands required to perform specific tasks. In this chapter, we first summarize recent computational, behavioural and neurophysiological studies that address the theoretical necessity of internal models, the locations of internal models, and the neural mechanism for acquiring internal models through learning. Then, we propose a new computational model of multiple internal models.

Animals↗