Tangential hand velocity profiles of rapid human arm movements often appearas sequences of several bell-shaped acceleration-deceleration phases called submovements or movement units. This suggests how the nervous system might efficiently control a motor plant in the presence of noise and feedback delay. Another critical observation is that stochasticity ina motor control problem makes the optimal control policy essentially differentfrom the optimal control policy for the deterministic case. We use a simplified dynamic model of an arm and address rapid aimed arm movements. We use reinforcement learning as a tool to approximate the optimal policy in the presence of noise and feedback delay. Using a simplified model we show that multiple submovements emerge as an optimal policy in the presence of noise and feedback delay. The optimal policy in this situation is to drive the arm's end point close to the target by one fast submovement and then apply a few slow submovements to accurately drivethe arm's end point into the target region. In our simulations, the controller sometimes generates corrective submovements before the initial fast submovement is completed, much like the predictive corrections observedin a number of psychophysical experiments.

Also, so far only uni-circular motion has been tested.

The mystery of belief propagation (BP) decoder, especially of the turbo decoding, is studied from information geometrical viewpoint. The loopy belief network (BN) of turbo codes makes it difficult to obtain the true "belief" by BP, and the characteristics of the algorithm and its equilibrium arenot clearly understood. Our study gives an intuitive understanding of the mechanism, and a new framework for the analysis. Based on the framework, we reveal basic properties of the turbo decoding.

In this paper we will show that a restricted class of constrained minimum divergenceproblems, named generalized inference problems, can be solved by approximating the KL divergence with a Bethe free energy. The algorithm we derive is closely related to both loopy belief propagation anditerative scaling. This unified propagation and scaling algorithm reduces to a convergent alternative to loopy belief propagation when no constraints are present. Experiments show the viability of our algorithm.

We develop a tree-based reparameterization framework that provides anew conceptual view of a large class of iterative algorithms for computing approximate marginals in graphs with cycles. It includes belief propagation (BP), which can be reformulated as a very local form of reparameterization. More generally, we consider algorithms that perform exact computations over spanning trees of the full graph. On the practical side, we find that such tree reparameterization (TRP) algorithms have convergence properties superior to BP. The reparameterization perspective also provides a number of theoretical insights into approximate inference, including anew characterization of fixed points; and an invariance intrinsic to TRP /BP. These two properties enable us to analyze and bound the error between the TRP /BP approximations and the actual marginals. While our results arise naturally from the TRP perspective, most of them apply in an algorithm-independent manner to any local minimum of the Bethe free energy.

Tangential hand velocity profiles of rapid human arm movements often appear as sequences of several bell-shaped acceleration-deceleration phases called submovements or movement units. This suggests how the nervous system might efficiently control a motor plant in the presence of noise and feedback delay. Another critical observation is that stochasticity in a motor control problem makes the optimal control policy essentially different from the optimal control policy for the deterministic case. We use a simplified dynamic model of an arm and address rapid aimed arm movements. We use reinforcement learning as a tool to approximate the optimal policy in the presence of noise and feedback delay. Using a simplified model we show that multiple submovements emerge as an optimal policy in the presence of noise and feedback delay. The optimal policy in this situation is to drive the arm's end point close to the target by one fast submovement and then apply a few slow submovements to accurately drive the arm's end point into the target region. In our simulations, the controller sometimes generates corrective submovements before the initial fast submovement is completed, much like the predictive corrections observed in a number of psychophysical experiments.

The image flow can be considerably more complicated than merely an expanding pattern of motion vectors centered on the heading direction (Figure 1). Flow caused by eye rotation (Figure 1 b) causes the center of flow to be displaced (compare Figure 1a and c). The effect of rotation depends on the ratio ofrotation and translation speed.

Cortical neurons might be considered as threshold elements integrating in parallel many excitatory and inhibitory inputs. Due to the apparent variability of cortical spike trains this yields a strongly fluctuating membrane potential, such that threshold crossings are highly irregular. Here we study how a neuron could maximize its sensitivity w.r.t. a relatively small subset of excitatory input. Weak signals embedded in fluctuations is the natural realm of stochastic resonance. The neuron's response is described in a hazard-function approximation applied to an Ornstein-Uhlenbeck process.

The mystery of belief propagation (BP) decoder, especially of the turbo decoding, is studied from information geometrical viewpoint. The loopy belief network (BN) of turbo codes makes it difficult to obtain the true "belief" by BP, and the characteristics of the algorithm and its equilibrium are not clearly understood. Our study gives an intuitive understanding of the mechanism, and a new framework for the analysis. Based on the framework, we reveal basic properties of the turbo decoding.

In this paper we will show that a restricted class of constrained minimum divergence problems, named generalized inference problems, can be solved by approximating the KL divergence with a Bethe free energy. The algorithm we derive is closely related to both loopy belief propagation and iterative scaling. This unified propagation and scaling algorithm reduces to a convergent alternative to loopy belief propagation when no constraints are present. Experiments show the viability of our algorithm.