Optimal impedance control for task achievement in the presence of signal-dependent noise.

There is an infinity of impedance parameter values, and thus different co-contraction levels, that can produce similar movement kinematics from which the CNS must select one. Although signal-dependent noise (SDN) predicts larger motor-command variability during higher co-contraction, the relationship between impedance and task performance is not theoretically obvious and thus was examined here. Subjects made goal-directed, single-joint elbow movements to either move naturally to different target sizes or voluntarily co-contract at different levels.

A via-point time optimization algorithm for complex sequential trajectory formation.

In our previous research, we proposed a method for the reproduction of complex movement trajectories and robot arm control that could mimic fast, skilled human movements. That method is based on bi-directional theory and uses a representation of a set of via-points as boundary conditions or control variables to perform robot arm trajectory control. The via-points are extracted from human movement data and the resultant via-point representation is able to regenerate handwritten characters, control a Kendama toy, and perform a tennis serve.

Reinforcement learning with via-point representation.

In this paper, we propose a new learning framework for motor control. This framework consists of two components: reinforcement learning and via-point representation. In the field of motor control, conventional reinforcement learning has been used to acquire control sequences such as cart-pole or stand-up robot control.

A computational model of spatio-temporal dynamics in depth filling-in.

We present a computational model based on the heat conduction equation, which can well explain human performance of depth interpolation. The model assumes that the depth information is locally represented and spatial integration is made by iterative processing of mutual interaction of neighbors. It reconstructs a dynamically transforming surface which is in good agreement with the results of psychophysical experiments on depth perception of untextured (uniform-colored) surface moving in depth.

A neural correlate of reward-based behavioral learning in caudate nucleus: a functional magnetic resonance imaging study of a stochastic decision task.

Humans can acquire appropriate behaviors that maximize rewards on a trial-and-error basis. Recent electrophysiological and imaging studies have demonstrated that neural activity in the midbrain and ventral striatum encodes the error of reward prediction. However, it is yet to be examined whether the striatum is the main locus of reward-based behavioral learning.

Functional magnetic resonance imaging examination of two modular architectures for switching multiple internal models.

An internal model is a neural mechanism that can mimic the input-output properties of a controlled object such as a tool. Recent research interests have moved on to how multiple internal models are learned and switched under a given context of behavior. Two representative computational models for task switching propose distinct neural mechanisms, thus predicting different brain activity patterns in the switching of internal models.

Random presentation enables subjects to adapt to two opposing forces on the hand.

Studies have shown that humans cannot simultaneously learn opposing force fields or opposing visuomotor rotations, even when provided with arbitrary contextual information, probably because of interference in their working memory. In contrast, we found that subjects can adapt to two opposing force fields when provided with contextual cues and can consolidate motor memories if random and frequent switching occurs. Because significant aftereffects were seen, this study suggests that multiple internal models can be acquired simultaneously during learning and predictively switched, depending only on contextual information.

Inter-module credit assignment in modular reinforcement learning.

Critical issues in modular or hierarchical reinforcement learning (RL) are (i) how to decompose a task into sub-tasks, (ii) how to achieve independence of learning of sub-tasks, and (iii) how to assure optimality of the composite policy for the entire task. The second and last requirements are often under trade-off. We propose a method for propagating the reward for the entire task achievement between modules.

Adaptation to stable and unstable dynamics achieved by combined impedance control and inverse dynamics model.

This study compared adaptation in novel force fields where trajectories were initially either stable or unstable to elucidate the processes of learning novel skills and adapting to new environments. Subjects learned to move in a null force field (NF), which was unexpectedly changed either to a velocity-dependent force field (VF), which resulted in perturbed but stable hand trajectories, or a position-dependent divergent force field (DF), which resulted in unstable trajectories. With practice, subjects learned to compensate for the perturbations produced by both force fields.

Different mechanisms involved in adaptation to stable and unstable dynamics.

Recently, we demonstrated that humans can learn to make accurate movements in an unstable environment by controlling magnitude, shape, and orientation of the endpoint impedance. Although previous studies of human motor learning suggest that the brain acquires an inverse dynamics model of the novel environment, it is not known whether this control mechanism is operative in unstable environments. We compared learning of multijoint arm movements in a "velocity-dependent force field" (VF), which interacted with the arm in a stable manner, and learning in a "divergent force field" (DF), where the interaction was unstable.

Characterization and transcription of the genes encoding enzymes involved in butyrate production in Butyrivibrio fibrisolvens.

Genes encoding enzymes that catalyze butyryl-CoA formation from acetyl-CoA in a type II strain of Butyrivibrio fibrisolvens were analyzed. The genes encoding thiolase, beta-hydroxybutyryl-CoA dehydrogenase, butyryl-CoA dehydrogenase, and electron transfer flavoproteins were clustered, but the crotonase gene was not present in this region. The deduced amino acid sequences of these enzymes were similar to those of clostridia.

The neural computation of the aperture problem: an iterative process.

The aperture problem is defined as one of integrating motion information from inside and outside of the aperture, and determination of the true direction of motion of a line. Much is known about it and many models have been proposed for its neural mechanisms. However, it is still a matter of debate whether the brain solves the problem by using only feed-forward neural connections, also known as the one-shot algorithm, or by using the iterative algorithm while utilizing feedback as well as horizontal neural connections.

Attentional modulation of oscillatory activity in human visual cortex.

The effects of attentional modulation on activity within the human visual cortex were investigated using magnetoencephalography. Chromatic sinusoidal stimuli were used to evoke activity from the occipital cortex, with attention directed either toward or away from the stimulus using a bar-orientation judgment task. For five observers, global magnetic field power was plotted as a function of time from stimulus onset.

Spatio-temporal dynamics of depth propagation on uniform region.

The depth of each point on a binocularly presented untextured horizontal bar is physically ambiguous except for the two vertical edges at both ends, since the correspondence between left and right images is not unique on such a uniform region. These depths, however, are unambiguously perceived, and this suggests the existence of some mechanism that interpolates the depth information from the two ends toward the center. Temporal properties of this integration process were examined by a phase-matching task, which allowed us to measure the phase of the perceived depth at the center of a horizontal bar when disparities at the ends were sinusoidally oscillated.

Acquisition and contextual switching of multiple internal models for different viscous force fields.

Humans can learn an enormous number of motor behaviors in different environments. To explain this, the MOSAIC model proposes that multiple internal models are acquired in the brain, which can be switched. However, previous behavioral studies that examined arm-movement adaptations to multiple environments reported a rather limited learning capability.

Functional significance of stiffness in adaptation of multijoint arm movements to stable and unstable dynamics.

This study compared the mechanisms of adaptation to stable and unstable dynamics from the perspective of changes in joint mechanics. Subjects were instructed to make point to point movements in force fields generated by a robotic manipulandum which interacted with the arm in either a stable or an unstable manner. After subjects adjusted to the initial disturbing effects of the force fields they were able to produce normal straight movements to the target.

Modular organization of internal models of tools in the human cerebellum.

Human capabilities in manipulating many different tools with dexterity suggest modular neural organization at functional levels, but anatomical modularity underlying the capabilities has yet to be demonstrated. Although modularity in phylogenetically older parts of the cerebellum is well known, comparable modularity in the lateral cerebellum for cognitive functions remains unknown. We investigated these issues by functional MRI (fMRI) based on our previous findings of a cerebellar internal model of a tool.

Internal forward models in the cerebellum: fMRI study on grip force and load force coupling.

Internal models are neural mechanisms that can mimic the input-output or output-input properties of the motor apparatus and external objects. Forward internal models predict sensory consequences from efference copies of motor commands. There is growing acceptance of the idea that forward models are important in sensorimotor integration as well as in higher cognitive function, but their anatomical loci and neural mechanisms are still largely unknown.

A unifying computational framework for motor control and social interaction.

Recent empirical studies have implicated the use of the motor system during action observation, imitation and social interaction. In this paper, we explore the computational parallels between the processes that occur in motor control and in action observation, imitation, social interaction and theory of mind. In particular, we examine the extent to which motor commands acting on the body can be equated with communicative signals acting on other people and suggest that computational solutions for motor control may have been extended to the domain of social interaction.

A tennis serve and upswing learning robot based on bi-directional theory.

We experimented on task-level robot learning based on bi-directional theory. The via-point representation was used for 'learning by watching'. In our previous work, we had a robot learn kendama (a Japanese game) in order to demonstrate a single simple task.

Multiple paired forward and inverse models for motor control.

Humans demonstrate a remarkable ability to generate accurate and appropriate motor behavior under many different and often uncertain environmental conditions. In this paper, we propose a modular approach to such motor learning and control. We review the behavioral evidence and benefits of modularity, and propose a new architecture based on multiple pairs of inverse (controller) and forward (predictor) models.

Cerebellar plasticity and the ocular following response.

We constructed a realistic simulation model to elucidate whether the characteristics of the cerebellar synaptic plasticity reported in vitro guide the acquisition and adaptation of the ocular following response (OFR). The model reconstructed the firing frequency of the inputs of granule cell axons (GCA), inhibitory cells (IC), and climbing fibers (CF) to cerebellar Purkinje cells for the OFR, to simulate the reported cerebellar plasticity, including long-term depression, long-term potentiation, and rebound potentiation. When the model used the same visual inputs as reported for monkeys, it successfully simulated the real characteristics of simple spikes in Purkinje cells of adult monkeys and adaptation of gain and direction.

[Bone lesions in elderly multiple myeloma].

We investigated the incidence of bone lesions in elderly cases of multiple myeloma (MM) and the course of those lesions, and also evaluated the relationships of skeletal symptoms with prognostic factors, and prognosis. The subjects were 146 patients, aged 65 years or more (median age 74, range 65-97 year), who were admitted to 11 institutions between January, 1988 and December, 1997. They consisted of 64 men and 82 women.