scholarly journals The Effects of Motor Modularity on Performance, Learning, and Generalizability in Upper-Extremity Reaching: a Computational Analysis

2019 ◽  
Author(s):  
Mazen Al Borno ◽  
Jennifer L. Hicks ◽  
Scott L. Delp
Keyword(s):  
Upper Extremity ◽  
Realistic Model ◽  
Computational Time ◽  
Muscle Synergies ◽  
Performance Deficits ◽  
Reaching Task ◽  
Learning Rates ◽  
The Central Nervous System ◽  
The Rich ◽  
Low Dimensional

AbstractIt has been hypothesized that the central nervous system simplifies the production of movement by limiting motor commands to a small set of modules known as muscle synergies. Recently, investigators have questioned whether a low-dimensional controller can produce the rich and flexible behaviors seen in everyday movements. To study this issue, we implemented muscle synergies in a biomechanically realistic model of the human upper extremity and performed computational experiments to determine whether synergies introduced task performance deficits, facilitated the learning of movements, and generalized to different movements. We derived sets of synergies from the muscle excitations our dynamic optimizations computed for a nominal task (reaching in a plane). Then we compared the performance and learning rates of a controller that activated all muscles independently to controllers that activated the synergies derived from the nominal reaching task. We found that a controller based on synergies had errors within 1 cm of a full-dimensional controller and achieved faster learning rates (as estimated from computational time to converge). The synergy-based controllers could also accomplish new tasks–such as reaching to targets on a higher or lower plane, and starting from alternate initial poses–with average errors similar to a full-dimensional controller.

scholarly journals The effects of motor modularity on performance, learning and generalizability in upper-extremity reaching: a computational analysis

2020 ◽  
Vol 17 (167) ◽  
pp. 20200011
Author(s):  
Mazen Al Borno ◽  
Jennifer L. Hicks ◽  
Scott L. Delp
Keyword(s):  
Upper Extremity ◽  
Realistic Model ◽  
Computational Time ◽  
Muscle Synergies ◽  
Performance Deficits ◽  
Reaching Task ◽  
Learning Rates ◽  
The Central Nervous System ◽  
The Rich ◽  
Low Dimensional

It has been hypothesized that the central nervous system simplifies the production of movement by limiting motor commands to a small set of modules known as muscle synergies. Recently, investigators have questioned whether a low-dimensional controller can produce the rich and flexible behaviours seen in everyday movements. To study this issue, we implemented muscle synergies in a biomechanically realistic model of the human upper extremity and performed computational experiments to determine whether synergies introduced task performance deficits, facilitated the learning of movements, and generalized to different movements. We derived sets of synergies from the muscle excitations our dynamic optimizations computed for a nominal task (reaching in a plane). Then we compared the performance and learning rates of a controller that activated all muscles independently to controllers that activated the synergies derived from the nominal reaching task. We found that a controller based on synergies had errors within 1 cm of a full-dimensional controller and achieved faster learning rates (as estimated from computational time to converge). The synergy-based controllers could also accomplish new tasks—such as reaching to targets on a higher or lower plane, and starting from alternative initial poses—with average errors similar to a full-dimensional controller.

The flexible recruitment of muscle synergies depends on the required force-generating capability

2014 ◽  
Vol 112 (2) ◽  
pp. 316-327 ◽  
Author(s):  
Shota Hagio ◽  
Motoki Kouzaki
Keyword(s):  
Central Nervous System ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Isometric Force ◽  
Nonnegative Matrix ◽  
Muscle Synergies ◽  
Joint Angles ◽  
Activation Patterns ◽  
The Central Nervous System ◽  
Low Dimensional

To simplify redundant motor control, the central nervous system (CNS) may modularly organize and recruit groups of muscles as “muscle synergies.” However, smooth and efficient movements are expected to require not only low-dimensional organization, but also flexibility in the recruitment or combination of synergies, depending on force-generating capability of individual muscles. In this study, we examined how the CNS controls activations of muscle synergies as changing joint angles. Subjects performed multidirectional isometric force generations around right ankle and extracted the muscle synergies using nonnegative matrix factorization across various knee and hip joint angles. As a result, muscle synergies were selectively recruited with merging or decomposition as changing the joint angles. Moreover, the activation profiles, including activation levels and the direction indicating the peak, of muscle synergies across force directions depended on the joint angles. Therefore, we suggested that the CNS selects appropriate muscle synergies and controls their activation patterns based on the force-generating capability of muscles with merging or decomposing descending neural inputs.

Improved interactive color visualization approach for hyperspectral images

2021 ◽  
pp. 147387162110481
Author(s):  
Haijun Yu ◽  
Shengyang Li
Keyword(s):  
Real Time ◽  
Hyperspectral Images ◽  
High Dimensional ◽  
Interactive Control ◽  
Output Image ◽  
Dr Method ◽  
The Rich ◽  
Low Dimensional ◽  
Color Visualization ◽  
Fusion Coefficient

Hyperspectral images (HSIs) have become increasingly prominent as they can maintain the subtle spectral differences of the imaged objects. Designing approaches and tools for analyzing HSIs presents a unique set of challenges due to their high-dimensional characteristics. An improved color visualization approach is proposed in this article to achieve communication between users and HSIs in the field of remote sensing. Under the real-time interactive control and color visualization, this approach can help users intuitively obtain the rich information hidden in original HSIs. Using the dimensionality reduction (DR) method based on band selection, high-dimensional HSIs are reduced to low-dimensional images. Through drop-down boxes, users can freely specify images that participate in the combination of RGB channels of the output image. Users can then interactively and independently set the fusion coefficient of each image within an interface based on concentric circles. At the same time, the output image will be calculated and visualized in real time, and the information it reflects will also be different. In this approach, channel combination and fusion coefficient setting are two independent processes, which allows users to interact more flexibly according to their needs. Furthermore, this approach is also applicable for interactive visualization of other types of multi-layer data.

scholarly journals Ensemble Classification through Random Projections for single-cell RNA-seq data

2020 ◽  
Author(s):  
Aristidis G. Vrahatis ◽  
Sotiris Tasoulis ◽  
Spiros Georgakopoulos ◽  
Vassilis Plagianakos
Keyword(s):  
Single Cell ◽  
Random Projection ◽  
Classification Performance ◽  
Majority Voting ◽  
Ensemble Classification ◽  
High Dimensionality ◽  
Computational Time ◽  
Biomedical Data ◽  
Rna Seq ◽  
Low Dimensional

AbstractNowadays the biomedical data are generated exponentially, creating datasets for analysis with ultra-high dimensionality and complexity. This revolution, which has been caused by recent advents in biotechnologies, has driven to big-data and data-driven computational approaches. An indicative example is the emerging single-cell RNA-sequencing (scRNA-seq) technology, which isolates and measures individual cells. Although scRNA-seq has revolutionized the biotechnology domain, such data computational analysis is a major challenge because of their ultra-high dimensionality and complexity. Following this direction, in this work we study the properties, effectiveness and generalization of the recently proposed MRPV algorithm for single cell RNA-seq data. MRPV is an ensemble classification technique utilizing multiple ultra-low dimensional Random Projected spaces. A given classifier determines the class for each sample for all independent spaces while a majority voting scheme defines their predominant class. We show that Random Projection ensembles offer a platform not only for a low computational time analysis but also for enhancing classification performance. The developed methodologies were applied to four real biomedical high dimensional data from single-cell RNA-seq studies and compared against well-known and similar classification tools. Experimental results showed that based on simplistic tools we can create a computationally fast, simple, yet effective approach for single cell RNA-seq data with ultra-high dimensionality.

scholarly journals Lower complexity of motor primitives ensures robust control of high-speed human locomotion

2020 ◽  
Author(s):  
Alessandro Santuz ◽  
Antonis Ekizos ◽  
Yoko Kunimasa ◽  
Kota Kijima ◽  
Masaki Ishikawa ◽  
...  
Keyword(s):  
High Speed ◽  
Muscle Activation ◽  
Human Locomotion ◽  
Muscle Synergies ◽  
Activation Patterns ◽  
Motor Primitives ◽  
Muscle Activation Patterns ◽  
Relative Contribution ◽  
Muscle Activations ◽  
Low Dimensional

AbstractWalking and running are mechanically and energetically different locomotion modes. For selecting one or another, speed is a parameter of paramount importance. Yet, both are likely controlled by similar low-dimensional neuronal networks that reflect in patterned muscle activations called muscle synergies. Here, we investigated how humans synergistically activate muscles during locomotion at different submaximal and maximal speeds. We analysed the duration and complexity (or irregularity) over time of motor primitives, the temporal components of muscle synergies. We found that the challenge imposed by controlling high-speed locomotion forces the central nervous system to produce muscle activation patterns that are wider and less complex relative to the duration of the gait cycle. The motor modules, or time-independent coefficients, were redistributed as locomotion speed changed. These outcomes show that robust locomotion control at challenging speeds is achieved by modulating the relative contribution of muscle activations and producing less complex and wider control signals, whereas slow speeds allow for more irregular control.

Abstract 144: Forced Aerobic Exercise Enhances Upper Extremity Task Practice in Patients with Stroke

Stroke ◽  
2015 ◽  
Vol 46 (suppl_1) ◽  
Author(s):  
Susan Linder ◽  
Anson Rosenfeldt ◽  
Jay Alberts
Keyword(s):  
Aerobic Exercise ◽  
Upper Extremity ◽  
Motor Function ◽  
Cardiovascular Health ◽  
Statistical Significance ◽  
Voluntary Exercise ◽  
Depressive Symptomology ◽  
Task Practice ◽  
The Central Nervous System ◽  
Minute Session

Introduction: Aerobic exercise (AE) has been shown to improve cardiovascular health in individuals with stroke; however, the potential role of AE in enhancing neuroplasticity after stroke has not been systematically studied. We have implemented a forced exercise (FE) cycling intervention, initially developed for individuals with Parkinson’s disease, with a cohort of individuals with chronic stroke. We hypothesize that intensive AE training, when paired with repetitive task practice (RTP), will “prime” the central nervous system, to exploit the motor learning effects of task practice. Hypothesis: Individuals who perform FE followed by RTP will demonstrate greater improvements in motor and non-motor function compared to the voluntary rate aerobic exercise (VE) + RTP and RTP only groups. Individuals in both AE groups (FE and VE) will demonstrate greater improvements in VO2peak compared to the RTP only group. Methods: Fifteen individuals 6-12 months post-stroke were enrolled into one of the following groups: 1) Forced Exercise + RTP (FE + RTP); 2) Voluntary Exercise + RTP (VE + RTP); and 3) Time-matched RTP. Participants in the AE groups completed one 45-minute session of stationary cycling followed immediately by one 45-minute session of upper extremity RTP; however, the rate of cycling for the FE group was augmented to approximately 35% faster than their voluntary rate. All participants completed a total of 24 exercise sessions over an 8-week period. Results: While all three groups made significant improvements in motor function as measured by the Fugl-Meyer Assessment (p=.03), the FE+RTP group exceeded the VE+RTP and RTP only groups, approaching statistical significance (p=0.06), despite the two AE groups completing 44% less RTP practice time than the RTP group. Improvements in self-reported quality of life and depressive symptomology also improved across all three groups, with trends favoring the FE group. VO2peak improved by 1.1 and 2.68 mL/kg/min for the FE+RTP and VE+RTP groups, respectively; while VO2peak decreased by 0.85mL/kg/min in the RTP group. Conclusion: FE + RTP is a promising intervention to enhance motor and non-motor function, in addition to aerobic capacity in individuals 6-12 months after stroke.

scholarly journals Computation in a Single Neuron: Hodgkin and Huxley Revisited

2003 ◽  
Vol 15 (8) ◽  
pp. 1715-1749 ◽  
Author(s):  
Blaise Agüera y Arcas ◽  
Adrienne L. Fairhall ◽  
William Bialek
Keyword(s):  
Mutual Information ◽  
Feature Detection ◽  
Dimensional Space ◽  
Model Neuron ◽  
Realistic Model ◽  
High Time Resolution ◽  
Correlation Technique ◽  
Two Dimensional ◽  
Dimensional Approximation ◽  
Low Dimensional

A spiking neuron “computes” by transforming a complex dynamical input into a train of action potentials, or spikes. The computation performed by the neuron can be formulated as dimensional reduction, or feature detection, followed by a nonlinear decision function over the low-dimensional space. Generalizations of the reverse correlation technique with white noise input provide a numerical strategy for extracting the relevant low-dimensional features from experimental data, and information theory can be used to evaluate the quality of the low-dimensional approximation. We apply these methods to analyze the simplest biophysically realistic model neuron, the Hodgkin-Huxley (HH) model, using this system to illustrate the general methodological issues. We focus on the features in the stimulus that trigger a spike, explicitly eliminating the effects of interactions between spikes. One can approximate this triggering “feature space” as a two-dimensional linear subspace in the high-dimensional space of input histories, capturing in this way a substantial fraction of the mutual information between inputs and spike time. We find that an even better approximation, however, is to describe the relevant subspace as two dimensional but curved; in this way, we can capture 90% of the mutual information even at high time resolution. Our analysis provides a new understanding of the computational properties of the HH model. While it is common to approximate neural behavior as “integrate and fire,” the HH model is not an integrator nor is it well described by a single threshold.

scholarly journals Toward a Computational Model of the Upper Extremity

2017 ◽  
Vol 7 (2) ◽  
pp. 40 ◽  
Author(s):  
Hooshang Hemami
Keyword(s):  
Rigid Body ◽  
Upper Extremity ◽  
Soft Tissues ◽  
Euler Method ◽  
Co Activation ◽  
Computational Testing ◽  
The Central Nervous System ◽  
Space Formulation ◽  
Circular Surface ◽  
Multi Body

A basic 22-segment model of the upper extremity is formulated that can allow computational testing of hypotheses about the control and coordination of the upper extremity by the central nervous system. The formulation allows for further analytical, anatomical, physiological, and bio-mechanical expansion and improvement of the model. It allows for inclusion of all passive structures: ligaments, membranes, soft tissues, and cartilages. The formulation is based on the state space formulation of the Newton-Euler method applied to multi-body systems. Extensive use is made of three-segment rigid body modules, constraints, reduction of dimensionality, projection, and matrices of large dimensions.An example, gliding motion of a rigid body on a circular surface (as in wiping a dish with a pre-specified force of contact) shows the application of some of the concepts and feasibility of the developed routines. The control is based on analogous strategies in living systems where co-activation of agonist-antagonist muscular systems and precise reference inputs implement the desirable trajectories of motion and where an integral feedback of the force implements the desired forces of contact.

Changes in muscle coordination with training

2006 ◽  
Vol 101 (5) ◽  
pp. 1506-1513 ◽  
Author(s):  
Richard G. Carson
Keyword(s):  
Control Strategies ◽  
Muscle Synergies ◽  
Muscle Coordination ◽  
Muscle Recruitment ◽  
Core Concepts ◽  
The Central Nervous System ◽  
Recruitment Patterns ◽  
The Stability ◽  
Motor Actions ◽  
Activity Dependent

Three core concepts, activity-dependent coupling, the composition of muscle synergies, and Hebbian adaptation, are discussed with a view to illustrating the nature of the constraints imposed by the organization of the central nervous system on the changes in muscle coordination induced by training. It is argued that training invoked variations in the efficiency with which motor actions can be generated influence the stability of coordination by altering the potential for activity-dependent coupling between the cortical representations of the focal muscles recruited in a movement task and brain circuits that do not contribute directly to the required behavior. The behaviors that can be generated during training are also constrained by the composition of existing intrinsic muscle synergies. In circumstances in which attempts to produce forceful or high velocity movements would otherwise result in the generation of inappropriate actions, training designed to promote the development of control strategies specific to the desired movement outcome may be necessary to compensate for protogenic muscle recruitment patterns. Hebbian adaptation refers to processes whereby, for neurons that release action potentials at the same time, there is an increased probability that synaptic connections will be formed. Neural connectivity induced by the repetition of specific muscle recruitment patterns during training may, however, inhibit the subsequent acquisition of new skills. Consideration is given to the possibility that, in the presence of the appropriate sensory guidance, it is possible to gate Hebbian plasticity and to promote greater subsequent flexibility in the recruitment of the trained muscles in other task contexts.

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