Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control最新文献

Striatum is a major stage of the motor loop but, despite a pivotal role in the execution of movements, it has been poorly studied thus far under Parkinsonian conditions and Deep Brain Stimulation (DBS). We propose a computational framework to analyze the spiking activity of striatal neurons under several conditions. This framework combines point process models and single unit recordings, and separately evaluates the effects of the spiking history, DBS frequency, and other cells on the neuronal discharge pattern, thus giving a full characterization of non-stationary neuronal dynamics and inter-neuronal dependencies. We applied this framework to 166 striatal neurons collected in a monkey both at rest and during DBS (30-130 Hz). Our analysis was conducted both before and after treatment of the animal with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), which evoked Parkinsonian-like motor disorders. We reported that high frequency (≥100 Hz) DBS reduces non-stationary dynamics and inter-neuronal dependencies by regularizing the discharge patterns both in MPTP and normal striatum, while the combination of MPTP and low frequency (30-80 Hz) DBS enhances these features, thus suggesting that pattern regularization in striatum might contribute to the therapeutic effect of high frequency DBS and presumably results from the overlap of feed-forward and feedback activation along the motor loop (reinforcement).

We introduce a message-passing algorithm to solve the Side Chain Positioning (SCP) problem. SCP is a crucial component of protein docking refinement, which is a key step of an important class of problems in computational structural biology called protein docking. We model SCP as a combinatorial optimization problem and formulate it as a Maximum Weighted Independent Set (MWIS) problem. We then employ a modified and convergent belief-propagation algorithm to solve a relaxation of MWIS and develop randomized estimation heuristics that use the relaxed solution to obtain an effective MWIS feasible solution. Using a benchmark set of protein complexes we demonstrate that our approach leads to more accurate docking predictions compared to a baseline algorithm that does not solve the SCP.

This paper presents a formulation of controlled geometric reduction with one degree of underactuation for mechanical systems with an unactuated cyclic variable subject to passive damping. We show that the first control term in the fully actuated case reduces to passive joint-velocity feedback, which can be equivalently provided by viscous friction. The underactuated control strategy is applied to a five-link 3D biped with a hip, torso, knees, and unactuated yaw at the foot contact point. We show asymptotically stable walking in the presence of passive yawing for realistic friction coefficients.

This paper presents a data-centric modeling and predictive control approach for nonlinear hybrid systems. System identification of hybrid systems represents a challenging problem because model parameters depend on the mode or operating point of the system. The proposed algorithm applies Model-on-Demand (MoD) estimation to generate a local linear approximation of the nonlinear hybrid system at each time step, using a small subset of data selected by an adaptive bandwidth selector. The appeal of the MoD approach lies in the fact that model parameters are estimated based on a current operating point; hence estimation of locations or modes governed by autonomous discrete events is achieved automatically. The local MoD model is then converted into a mixed logical dynamical (MLD) system representation which can be used directly in a model predictive control (MPC) law for hybrid systems using multiple-degree-of-freedom tuning. The effectiveness of the proposed MoD predictive control algorithm for nonlinear hybrid systems is demonstrated on a hypothetical adaptive behavioral intervention problem inspired by Fast Track, a real-life preventive intervention for improving parental function and reducing conduct disorder in at-risk children. Simulation results demonstrate that the proposed algorithm can be useful for adaptive intervention problems exhibiting both nonlinear and hybrid character.

Partitioning Magnetic-Resonance-Imaging (MRI) data into salient anatomic structures is a problem in medical imaging that has continued to elude fully automated solutions. Implicit functions are a common way to model the boundaries between structures and are amenable to control-theoretic methods. In this paper, the goal of enabling a human to obtain accurate segmentations in a short amount of time and with little effort is transformed into a control synthesis problem. Perturbing the state and dynamics of an implicit function's driving partial differential equation via the accumulated user inputs and an observer-like system leads to desirable closed-loop behavior. Using a Lyapunov control design, a balance is established between the influence of a data-driven gradient flow and the human's input over time. Automatic segmentation is thus smoothly coupled with interactivity. An application of the mathematical methods to orthopedic segmentation is shown, demonstrating the expected transient and steady state behavior of the implicit segmentation function and auxiliary observer.