Understanding Parkinsonian handwriting through a computational model of basal ganglia

Handwriting in Parkinson's disease (PD) is typically characterized by micrographia, jagged line contour, and unusual fluctuations in pen tip velocity. Although PD handwriting features have been used for diagnostics, they are not based on a signaling model of basal ganglia (BG). In this letter, we present a computational model of handwriting generation that highlights the role of BG. When PD conditions like reduced dopamine and altered dynamics of the subthalamic nucleus and globus pallidus externa subsystems are simulated, the handwriting produced by the model manifested characteristic PD handwriting distortions like micrographia and velocity fluctuations. Our approach to PD modeling is in tune with the perspective that PD is a dynamic disease.     

A globally optimal estimator for the delta-lognormal modeling of fast reaching movements

Fast reaching movements are an important component of our daily interaction with the world and are consequently under investigation in many fields of science and engineering. Today, useful models are available for such studies, with tools for solving the inverse dynamics problem involved by these analyses. These tools generally provide a set of model parameters that allows an accurate and locally optimal reconstruction of the original movements. Although the solutions that they generate may provide a data curve fitting that is sufficient for some pattern recognition applications, the best possible solution is often necessary in others, particularly those involving neuroscience and biomedical signal processing. To generate these solutions, we present a globally optimal parameter extractor for the delta-lognormal modeling of reaching movements based on the branch-and-bound strategy. This algorithm is used to test the impact of white noise on the delta-lognormal modeling of reaching movements and to benchmark the state-of-the-art locally optimal algorithm. Our study shows that, even with globally optimal solutions, parameter averaging is important for obtaining reliable figures. It concludes that physiologically derived rules are necessary, in addition to global optimality, to achieve meaningful ?Λ extractions which can be used to investigate the control patterns of these movement primitives.     

Control design for human-like reaching movements using redundancy in robot arm-trunk systems

This paper develops a control algorithm to show the human-like reaching movements in humanoid redundant systems involving the trunk. This algorithm neither requires the computation of pseudo-inverse of Jacobian nor does it need the optimization of any artificial performance index. The control law accommodates the time-varying temporal properties of the muscle stiffness and damping as well as low-pass filter characteristics of human muscles. It uses a time-varying damping shaping matrix and a bijective joint muscle mapping function to describe the spatial characteristics of human reaching motion like quasi-straight line trajectory of the end-effector and symmetric bell shaped velocity profile as well as the temporal characteristics like the occurrence of the peak velocity of the trunk motion after the peak velocity of the arm motion. The aspect of self-motion is also analyzed using the null-space motion of the manipulator Jacobian. The effects of the control parameters on the motion pattern are analyzed in detail and some basic guidelines have been provided to select their proper values. Simulation results show the efficacy of the newly developed algorithm in describing humanmotion characteristics.     

Kinematic properties of human arm reaching movements in a three-dimensional space

In this study, kinematic properties of human arm reaching movements have been analyzed by use of experimental results of arm trajectories observed in a three-dimensional (3D) space. In the beginning, hand paths obtained by the experiments are kinematically analyzed to pursue their linearity, and we successfully specify a plane on which a hand moves. In the next place, the hand speed profile is calculated by use of position data observed by the experiment in a 3D space. Besides, the hand speed profile is also analytically produced under the minimum jerk criterion with respect to the displacement along the hand path. These observed and produced trajectories are compared, and the similarity of two trajectories has been demonstrated. As a result of the analyses for path and the speed profile of a hand, kinematic properties of human arm trajectories have been identified.     

A three-dimensional dynamic posture prediction model for in-vehicle seated reaching movements: development and validation

A three-dimensional dynamic posture prediction model for simulating in-vehicle reaching movements is presented. The model employs a four-segment 7-degrees-of-freedom linkage structure to represent the torso, clavicle and right extremity. It relies on an optimization-based differential inverse kinematics to estimate a set of four weighting parameters that quantify a timeconstant, inter-segment motion apportionment strategy. In the development 100 seated reaching movements performed by 10 subjects towards five in-vehicle targets were modelled, resulting in 100 sets of weighting Statistical analysis was then conducted to relate these parameters to and individual attributes. In the validation phase, the generalized model, parameter values statistically synthesized, was applied to novel data sets 700 different reaching movements (towards different targets and/or by subjects). The results demonstrated the model's ability to generate close in prediction: the overall mean time-averaged error in joint angle 5.2°, and the median was 4.7°, excluding reaches towards two extreme targets which modelling errors were excessive). The model's general success in and its unique characteristics led to implications with regard to the and underlying control strategies of human reaching movements.     

A three-dimensional dynamic posture prediction model for simulating in-vehicle seated reaching movements: development and validation

A three-dimensional dynamic posture prediction model for simulating in-vehicle seated reaching movements is presented. The model employs a four-segment 7-degrees-of-freedom linkage structure to represent the torso, clavicle and right upper extremity. It relies on an optimization-based differential inverse kinematics approach to estimate a set of four weighting parameters that quantify a time-constant, inter-segment motion apportionment strategy. In the development phase, 100 seated reaching movements performed by 10 subjects towards five typical in-vehicle targets were modelled, resulting in 100 sets of weighting parameters. Statistical analysis was then conducted to relate these parameters to target and individual attributes. In the validation phase, the generalized model, with parameter values statistically synthesized, was applied to novel data sets containing 700 different reaching movements (towards different targets and/or by different subjects). The results demonstrated the model's ability to generate close representations in prediction: the overall mean time-averaged error in joint angle was 5.2 degrees, and the median was 4.7 degrees, excluding reaches towards two extreme targets (for which modelling errors were excessive). The model's general success in prediction and its unique characteristics led to implications with regard to the performance and underlying control strategies of human reaching movements.     

Self-organization in the basal ganglia with modulation of reinforcement signals

Self-organization is one of fundamental brain computations for forming efficient representations of information. Experimental support for this idea has been largely limited to the developmental and reorganizational formation of neural circuits in the sensory cortices. We now propose that self-organization may also play an important role in short-term synaptic changes in reward-driven voluntary behaviors. It has recently been shown that many neurons in the basal ganglia change their sensory responses flexibly in relation to rewards. Our computational model proposes that the rapid changes in striatal projection neurons depend on the subtle balance between the Hebb-type mechanisms of excitation and inhibition, which are modulated by reinforcement signals. Simulations based on the model are shown to produce various types of neural activity similar to those found in experiments.     

Minimum acceleration criterion with constraints implies bang-bang control as an underlying principle for optimal trajectories of arm reaching movements

Rapid arm-reaching movements serve as an excellent test bed for any theory about trajectory formation. How are these movements planned? A minimum acceleration criterion has been examined in the past, and the solution obtained, based on the Euler-Poisson equation, failed to predict that the hand would begin and end the movement at rest (i.e., with zero acceleration). Therefore, this criterion was rejected in favor of the minimum jerk, which was proved to be successful in describing many features of human movements. This letter follows an alternative approach and solves the minimum acceleration problem with constraints using Pontryagin's minimum principle. We use the minimum principle to obtain minimum acceleration trajectories and use the jerk as a control signal. In order to find a solution that does not include nonphysiological impulse functions, constraints on the maximum and minimum jerk values are assumed. The analytical solution provides a three-phase piecewise constant jerk signal (bang-bang control) where the magnitude of the jerk and the two switching times depend on the magnitude of the maximum and minimum available jerk values. This result fits the observed trajectories of reaching movements and takes into account both the extrinsic coordinates and the muscle limitations in a single framework. The minimum acceleration with constraints principle is discussed as a unifying approach for many observations about the neural control of movements.     

Right hand advantage in visually guided reaching and aiming movements: brief review and comments

Although understanding of the organization and control of visually guided reaching and aiming movements is still sketchy and incomplete, evidence from behavioural studies supports the contention that right-handed individuals typically execute aiming movements with better speed, smoothness and consistency, and with a greater degree of spatial precision when performing them with their right hand. Creative attempts to account for the superiority of the right hand on a variety of visually guided reaching and aiming tasks have focused on the processing characteristics of the contralateral or left cerebral hemisphere. This brief review summarizes the research conducted over the last few decades on the subject, highlights the theoretical interpretations offered to explain manual asymmetries in the organization and control of goal-directed movements and identifies directions for further empirical research. The theoretical and practical implications of laterality research efforts along the lines of goal-directed behaviour are discussed.     

Integration of reinforcement learning and optimal decision-making theories of the basal ganglia

This article seeks to integrate two sets of theories describing action selection in the basal ganglia: reinforcement learning theories describing learning which actions to select to maximize reward and decision-making theories proposing that the basal ganglia selects actions on the basis of sensory evidence accumulated in the cortex. In particular, we present a model that integrates the actor-critic model of reinforcement learning and a model assuming that the cortico-basal-ganglia circuit implements a statistically optimal decision-making procedure. The values of cortico-striatal weights required for optimal decision making in our model differ from those provided by standard reinforcement learning models. Nevertheless, we show that an actor-critic model converges to the weights required for optimal decision making when biologically realistic limits on synaptic weights are introduced. We also describe the model's predictions concerning reaction times and neural responses during learning, and we discuss directions required for further integration of reinforcement learning and optimal decision-making theories.     

Right hand advantage in visually guided reaching and aiming movements: brief review and comments

Although understanding of the organization and control of visually guided reaching and aiming movements is still sketchy and incomplete, evidence from behavioural studies supports the contention that right-handed individuals typically execute aiming movements with better speed, smoothness and consistency, and with a greater degree of spatial precision when performing them with their right hand. Creative attempts to account for the superiority of the right hand on a variety of visually guided reaching and aiming tasks have focused on the processing characteristics of the contralateral or left cerebral hemisphere. This brief review summarizes the research conducted over the last few decades on the subject, highlights the theoretical interpretations offered to explain manual asymmetries in the organization and control of goal-directed movements and identifies directions for further empirical research. The theoretical and practical implications of laterality research efforts along the lines of goal-directed behaviour are discussed.     

Analysis of delay-induced basal ganglia oscillations: the role of external excitatory nuclei

Basal ganglia are interconnected deep brain structures involved in movement generation. Their persistent beta-band oscillations (13–30 Hz) are known to be linked to Parkinson’s disease motor symptoms. In this paper, we provide conditions under which these oscillations may occur, by explicitly considering the role of the pedunculopontine nucleus (PPN). We analyse the existence of equilibria in the associated firing-rate dynamics and study their stability by relying on a delayed multiple-input/multiple-output (MIMO) frequency analysis. Our analysis suggests that the PPN has an influence on the generation of pathological beta-band oscillations. These results are illustrated by simulations that confirm numerically the analytic predictions of our two main theorems.     

Modeling spatial layout for scene image understanding via a novel multiscale sum-product network

Semantic image segmentation is challenging due to the large intra-class variations and the complex spatial layouts inside natural scenes. This paper investigates this problem by designing a new deep architecture, called multiscale sum-product network (MSPN), which utilizes multiscale unary potentials as the inputs and models the spatial layouts of image content in a hierarchical manner. That is, the proposed MSPN models the joint distribution of multiscale unary potentials and object classes instead of single unary potentials in popular settings. Besides, MSPN characterizes scene spatial layouts in a fine-to-coarse manner to enforce the consistency in labeling. Multiscale unary potentials at different scales can thus help overcome semantic ambiguities caused by only evaluating single local regions, while long-range spatial correlations can further refine image labeling. In addition, higher orders are able to pose the constraints among labels. By this way, multi-scale unary potentials, long-range spatial correlations, higher-order priors are well modeled under the uniform framework in MSPN. We conduct experiments on two challenging benchmarks consisting of the MSRC-21 dataset and the SIFT FLOW dataset. The results demonstrate the superior performance of our method comparing with the previous graphical models for understanding scene images.     

Self-supervised learning for docking and target reaching

A robotic system using simple visual processing and controlled by neural networks is described. The robot performs docking and target reaching without prior geometric calibration of its components. All effects of control signals on the robot are learned by the controller through visual observation during a training period, and refined during actual operation. Minor changes in the system's configuration result in a brief period of degraded performance while the controller adapts to the new mappings.

It is shown that a neural network-based controller can perform rapidly and accurately, taking into account the non-linearities of various mapping functions. Such a controller is easy to train, tolerant of imprecise equipment configurations, and insensitive to camera perturbations following training. This method features real-time adaptivity to changes in mappings, and is simpler than traditional control techniques, which require the solution of the inverse perspective projection and inverse kinematics of the system.

Various operations including approaching, centering, paralleling, reaching and adjusting are performed by the robot as it navigates towards the target. The robot attempts to grasp targets that are sufficiently close, or approach them while avoiding collisions with obstacles.     

Making working memory work: a computational model of learning in the prefrontal cortex and basal ganglia

The prefrontal cortex has long been thought to subserve both working memory (the holding of information online for processing) and executive functions (deciding how to manipulate working memory and perform processing). Although many computational models of working memory have been developed, the mechanistic basis of executive function remains elusive, often amounting to a homunculus. This article presents an attempt to deconstruct this homunculus through powerful learning mechanisms that allow a computational model of the prefrontal cortex to control both itself and other brain areas in a strategic, task-appropriate manner. These learning mechanisms are based on subcortical structures in the midbrain, basal ganglia, and amygdala, which together form an actor-critic architecture. The critic system learns which prefrontal representations are task relevant and trains the actor, which in turn provides a dynamic gating mechanism for controlling working memory updating. Computationally, the learning mechanism is designed to simultaneously solve the temporal and structural credit assignment problems. The model's performance compares favorably with standard backpropagation-based temporal learning mechanisms on the challenging 1-2-AX working memory task and other benchmark working memory tasks.     

Model predictive control for fast reaching in clutter

A key challenge for haptically reaching in dense clutter is the frequent contact that can occur between the robot’s arm and the environment. We have previously used single-time-step model predictive control (MPC) to enable a robot to slowly reach into dense clutter using a quasistatic mechanical model. Rapid reaching in clutter would be desirable, but entails additional challenges due to dynamic phenomena that can lead to higher forces from impacts and other types of contact. In this paper, we present a multi-time-step MPC formulation that enables a robot to rapidly reach a target position in dense clutter, while regulating whole-body contact forces to be below a given threshold. Our controller models the dynamics of the arm in contact with the environment in order to predict how contact forces will change and how the robot’s end effector will move. It also models how joint velocities will influence potential impact forces. At each time step, our controller uses linear models to generate a convex optimization problem that it can solve efficiently. Through tens of thousands of trials in simulation, we show that with our dynamic MPC a simulated robot can, on average, reach goals 1.4 to 2 times faster than our previous controller, while attaining comparable success rates and fewer occurrences of high forces. We also conducted trials using a real 7 degree-of-freedom (DoF) humanoid robot arm with whole-arm tactile sensing. Our controller enabled the robot to rapidly reach target positions in dense artificial foliage while keeping contact forces low.     

Distributed algorithms for reaching consensus on general functions

This paper presents analysis and design results for distributed consensus algorithms in multi-agent networks. We consider continuous consensus functions of the initial state of the network agents. Under mild smoothness assumptions, we obtain necessary and sufficient conditions characterizing any algorithm that asymptotically achieves consensus. This characterization is the building block to obtain various design results for networks with weighted, directed interconnection topologies. We first identify a class of smooth functions for which one can synthesize in a systematic way distributed algorithms that achieve consensus. We apply this result to the family of weighted power mean functions, and characterize the exponential convergence properties of the resulting algorithms. We establish the validity of these results for scenarios with switching interconnection topologies. Finally, we conclude with two discontinuous distributed algorithms that achieve, respectively, max and min consensus in finite time.     

Modeling reaching impairment after stroke using a population vector model of movement control that incorporates neural firing-rate variability

The directional control of reaching after stroke was simulated by including cell death and firing-rate noise in a population vector model of movement control. In this model, cortical activity was assumed to cause the hand to move in the direction of a population vector, defined by a summation of responses from neurons with cosine directional tuning. Two types of directional error were analyzed: the between-target variability, defined as the standard deviation of the directional error across a wide range of target directions, and the within-target variability, defined as the standard deviation of the directional error for many reaches to a single target. Both between- and within-target variability increased with increasing cell death. The increase in between-target variability arose because cell death caused a nonuniform distribution of preferred directions. The increase in within-target variability arose because the magnitude of the population vector decreased more quickly than its standard deviation for increasing cell death, provided appropriate levels of firing-rate noise were present. Comparisons to reaching data from 29 stroke subjects revealed similar increases in between- and within-target variability as clinical impairment severity increased. Relationships between simulated cell death and impairment severity were derived using the between- and within-target variability results. For both relationships, impairment severity increased similarly with decreasing percentage of surviving cells, consistent with results from previous imaging studies. These results demonstrate that a population vector model of movement control that incorporates cosine tuning, linear summation of unitary responses, firing-rate noise, and random cell death can account for some features of impaired arm movement after stroke.