scholarly journals Muscle coactivation: definitions, mechanisms, and functions

2018 ◽  
Vol 120 (1) ◽  
pp. 88-104 ◽  
Author(s):  
Mark L. Latash
Keyword(s):  
Neural Control ◽  
Hierarchical Control ◽  
Kinematic Chain ◽  
Level Control ◽  
Antagonist Muscle ◽  
Apparent Stiffness ◽  
Challenging Tasks ◽  
Movement Stability ◽  
Movement Mechanics ◽  
Motor Actions

The phenomenon of agonist-antagonist muscle coactivation is discussed with respect to its consequences for movement mechanics (such as increasing joint apparent stiffness, facilitating faster movements, and effects on action stability), implication for movement optimization, and involvement of different neurophysiological structures. Effects of coactivation on movement stability are ambiguous and depend on the effector representing a kinematic chain with a fixed origin or free origin. Furthermore, coactivation is discussed within the framework of the equilibrium-point hypothesis and the idea of hierarchical control with spatial referent coordinates. Relations of muscle coactivation to changes in one of the basic commands, the c-command, are discussed and illustrated. A hypothesis is suggested that agonist-antagonist coactivation reflects a deliberate neural control strategy to preserve effector-level control and avoid making it degenerate and facing the necessity to control at the level of signals to individual muscles. This strategy, in particular, allows stabilizing motor actions by covaried adjustments in spaces of control variables. This hypothesis is able to account for higher levels of coactivation in young healthy persons performing challenging tasks and across various populations with movement impairments.

scholarly journals Stability of hand force production. II. Ascending and descending synergies

2018 ◽  
Vol 120 (3) ◽  
pp. 1045-1060 ◽  
Author(s):  
Sasha Reschechtko ◽  
Mark L. Latash
Keyword(s):  
Visual Feedback ◽  
Neural Control ◽  
Uncontrolled Manifold ◽  
Total Force ◽  
Level Control ◽  
Control Variables ◽  
Specific Level ◽  
Referent Coordinate ◽  
Apparent Stiffness ◽  
Different Levels

We combined the theory of neural control of movement with referent coordinates and the uncontrolled manifold hypothesis to investigate multifinger coordination. We tested hypotheses related to stabilization of performance by covarying control variables, translated into apparent stiffness and referent coordinate, at different levels of an assumed hierarchy of control. Subjects produced an accurate combination of total force and total moment of force with the four fingers under visual feedback on both variables and after feedback was partly or completely removed. The “inverse piano” device was used to estimate control variables. We observed strong synergies in the space of hypothetical control variables that stabilized total force and moment of force, as well as weaker synergies stabilizing individual finger forces; whereas the former were attenuated by alteration of visual feedback, the latter were much less affected. In addition, we investigated the organization of “ascending synergies” stabilizing task-level control variables by covaried adjustments of finger-level control variables. We observed intertrial covariation of individual fingers’ referent coordinates that stabilized hand-level referent coordinate, but we observed no such covariation for apparent stiffness. The observations suggest the existence of both descending and ascending synergies in a hierarchical control system. They confirm a trade-off between synergies at different levels of control and corroborate the hypothesis on specialization of different fingers for the control of force and moment. The results provide strong evidence for the importance of central back-coupling loops in ensuring stability of action.NEW & NOTEWORTHY We expand analysis of action in the space of hypothetical control variables to hierarchically organized multieffector systems. We also introduce the novel concept of ascending synergies, which reflect covariation of control variables to individual effectors (fingers) that stabilize task-specific control variables at a hierarchically higher, task-specific level (hand).

Neural network modeling of the efficiency of response to emergency situations in a multi-level control system

2021 ◽  
Vol 92 ◽  
pp. 79-93
Author(s):  
N. G. Topolsky ◽  
◽  
S. Y. Butuzov ◽  
V. Y. Vilisov ◽  
V. L. Semikov ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Control Systems ◽  
Hierarchical Control ◽  
Hierarchical Systems ◽  
Level Control ◽  
Emergency Situations ◽  
Expert Assessments ◽  
Multi Level ◽  
The Relationship

Introduction. It is important to have models that adequately describe the relationship between the integral indicators of the functioning of the system with the particular indicators of the lower levels of management in complex control systems, in particular in RSChS. Traditional approaches based on normative models often turn out to be untenable due to the impossibility of covering all aspects of the functioning of such systems, as well as due to the high variability of the environment and the values of the set of target indicators. Recently, adaptive machine-learning models have proven to be productive, allowing build stable and adequate models, one of the variants of which is artificial neural networks (ANN), based on the solution of inverse problems using expert estimates. The relevance of the study lies in the development of compact models that allow assessing the effectiveness of the functioning of complex multi-level control systems (RSChS) in emergency situations, developing according to complex scenarios, in which emergencies of various types can occur simultaneously. Goals and objectives. The purpose of the article is to build and test the technology for creating compact models that are adequate to the system of indicators of the functioning of hierarchically organized control systems. This goal gives rise to the task of choosing tools for constructing the necessary models and sources of initial data. Methods. The research tools include methods for analyzing hierarchical systems, mathematical statistics, machine learning methods of ANN, simulation modeling, expert assessment methods, software systems for processing statistical data. The research is based on materials from domestic and foreign publications. Results and discussion. The proposed technology for constructing a neural network model of the effectiveness of the functioning of complex hierarchical systems provides a basis for constructing dynamic models of this type, which make it possible to distribute limited financial and other resources during the operation of the system according to a complex scenario of emergency response. Conclusion. The paper presents the results of solving the problem of constructing an ANN and its corresponding nonlinear function, reflecting the relationship between the performance indicators of the lower levels of the hierarchical control system (RSChS) with the upper level. The neural network model constructed in this way can be used in the decision support system for resource management in the context of complex scenarios for the development of emergency situations. The use of expert assessments as an information basis makes it possible to take into account numerous target indicators, which are extremely difficult to take into account in other ways. Keywords: emergency situations, hierarchical control system, efficiency, artificial neural network, expert assessments

Hierarchical Control Co-Design Using a Model Fidelity-Based Decomposition Framework

2020 ◽  
Vol 143 (1) ◽  
Author(s):  
Austin L. Nash ◽  
Neera Jain
Keyword(s):  
Hierarchical Control ◽  
Coupled Systems ◽  
System Level ◽  
Design Stage ◽  
System Component ◽  
Model Parameters ◽  
Level Control ◽  
Efficient Manner ◽  
Model Fidelity ◽  
Time Operation

Abstract Increasing performance demands and constraints are necessitating the design of highly complex, integrated systems across multiple sectors, including transportation and energy. However, conventional design approaches for such systems are largely siloed and focused on steady-state operation. To accommodate tightening operating envelopes, new design paradigms are needed that explicitly consider system-component interactions and their implications on transient performance at the system design stage. In this work, we present a model fidelity-based decomposition (MFBD) hierarchical control co-design (HCCD) algorithm designed to optimize system performance characteristics, with an emphasis on robustness to transient disturbances during real-time operation. Our framework integrates system level control co-design (CCD) with high-fidelity component design optimization in a computationally efficient manner for classes of highly coupled systems in which the coupling between subproblems cannot be fully captured using existing analytical relationships. Our algorithm permits scalable decomposition of computationally intensive component models and addresses coupling issues between subproblems in part by introducing an intermediate optimization procedure to solve for reduced-order model parameters that maximize the accuracy of the lumped-parameter control model required in the CCD algorithm. We demonstrate the merits of the MFBD HCCD algorithm, in comparison to an all-at-once (AAO) CCD approach, through a case study on aircraft dynamic thermal management. Our results show that our decomposition-based solution matches the AAO optimal cost to within 2.5% with a 54% reduction in computation time.

Gastropod Feeding Systems: Evolution of Neural Circuits that Generate Diverse Behaviors

2020 ◽  
Author(s):  
Paul Benjamin ◽  
Michael Crossley
Keyword(s):  
Neural Control ◽  
Evolutionary Relationship ◽  
Neural Circuitry ◽  
Evolutionary Development ◽  
Feeding Behaviors ◽  
Behavioral Switching ◽  
Evolutionary Changes ◽  
Species Specific ◽  
Motor Actions ◽  
Feeding Systems

It is conceptually reasonable to explore how the evolution of behavior involves changes in neural circuitry. Progress in determining this evolutionary relationship has been limited in neuroscience because of difficulties in identifying individual neurons that contribute to the evolutionary development of behaviors across species. However, the results from the feeding systems of gastropod mollusks provide evidence for this concept of co-evolution because the evolution of different types of feeding behaviors in this diverse group of mollusks is mirrored by species-specific changes in neural circuitry. The evolution of feeding behaviors involves changes in the motor actions that allow diverse food items to be acquired and ingested. The evolution in neural control accompanies this variation in food and the associated changes in flexibility of feeding behaviors. This is present in components of the feeding network that are involved in decision making, rhythm generation, and behavioral switching but is absent in background mechanisms that are conserved across species, such as those controlling arousal state. These findings show how evolutionary changes, even at the single neuron level, closely reflect the details of behavioral evolution.

scholarly journals Emergent modular neural control drives coordinated motor actions

2019 ◽  
Vol 22 (7) ◽  
pp. 1122-1131 ◽  
Author(s):  
Stefan M. Lemke ◽  
Dhakshin S. Ramanathan ◽  
Ling Guo ◽  
Seok Joon Won ◽  
Karunesh Ganguly
Keyword(s):  
Neural Control ◽  
Motor Actions

A Research Facility for the Investigation of Computer Control of Large-Scale Industrial Processes

1974 ◽  
Vol 7 (8) ◽  
pp. 295-299
Author(s):  
M. J. H. Sterling ◽  
S. Bennett
Keyword(s):  
Data Transmission ◽  
Large Scale ◽  
Computer Control ◽  
Hierarchical Control ◽  
Industrial Processes ◽  
Level Control ◽  
Research Facility ◽  
Data Link ◽  
Multi Level ◽  
Diverse Data

The implementation of multi-level control schemes for large industrial processes is discussed, with particular emphasis on the problems of data transmission between computers operating at various levels in the system. The operation of one type of inter-processor data link is considered in detail and its relevance as a research facility for hierarchical control systems is demonstrated. Applications which make use of this facility are presented illustrating the diverse data transmission requirements of multi-level systems.

Kinematic Modelling and VR Simulation of a 3DOF Medical Parallel Robot with One Decoupled Motion

2013 ◽  
Vol 837 ◽  
pp. 567-572
Author(s):  
Nadia Cretescu ◽  
Mircea Neagoe ◽  
Radu Saulescu
Keyword(s):  
Kinematic Model ◽  
Kinematic Chain ◽  
Parallel Robot ◽  
Parallel Structure ◽  
Level Control ◽  
Kinematic Chains ◽  
Fixed Base ◽  
Decoupled Motion ◽  
Kinematic Modelling ◽  
High Level

The robot studied in the paper has a 3DOF parallel structure of type 1PRRR+2PRPaR, with two coupled motions and one decoupled motion, composed by a mobile platform connected to the fixed base by three kinematic chains (one open kinematic chain of Prismatic Revolute Revolute Revolute type and two kinematic chains of Prismatic Revolute Parallelogram Revolute type). An analytical kinematic modelling of the parallel robot of type 1PRRR+2PRPaR is firstly presented in this paper, followed by a numerical simulation of the closed-form kinematic model and by a Virtual Reality (VR) application with control aspects. An innovative user interface for high-level control of the parallel 1PRRR+2PRPaR type robot is developed in MATLAB - Simulink and SimMechanics environment.

Neural control hierarchy of the heart has not evolved to deal with myocardial ischemia

2013 ◽  
Vol 45 (15) ◽  
pp. 638-644 ◽  
Author(s):  
G. Kember ◽  
J. A. Armour ◽  
M. Zamir
Keyword(s):  
Heart Rate ◽  
Control System ◽  
Myocardial Ischemia ◽  
Neural Control ◽  
Hierarchical Control ◽  
Central Command ◽  
Ischemic Event ◽  
Cardiac Ganglia ◽  
Control Hierarchy

The consequences of myocardial ischemia are examined from the standpoint of the neural control system of the heart, a hierarchy of three neuronal centers residing in central command, intrathoracic ganglia, and intrinsic cardiac ganglia. The basis of the investigation is the premise that while this hierarchical control system has evolved to deal with “normal” physiological circumstances, its response in the event of myocardial ischemia is unpredictable because the singular circumstances of this event are as yet not part of its evolutionary repertoire. The results indicate that the harmonious relationship between the three levels of control breaks down, because of a conflict between the priorities that they have evolved to deal with. Essentially, while the main priority in central command is blood demand, the priority at the intrathoracic and cardiac levels is heart rate. As a result of this breakdown, heart rate becomes less predictable and therefore less reliable as a diagnostic guide as to the traumatic state of the heart, which it is commonly used as such following an ischemic event. On the basis of these results it is proposed that under the singular conditions of myocardial ischemia a determination of neural control indexes in addition to cardiovascular indexes has the potential of enhancing clinical outcome.

scholarly journals The role of social cognition in decision making

2008 ◽  
Vol 363 (1511) ◽  
pp. 3875-3886 ◽  
Author(s):  
Chris D Frith ◽  
Tania Singer
Keyword(s):  
Decision Making ◽  
Brain Activity ◽  
Mental States ◽  
Social Setting ◽  
Abstract Concepts ◽  
Level Control ◽  
Altruistic Punishment ◽  
Social Motivations ◽  
Shared Networks ◽  
Motor Actions

Successful decision making in a social setting depends on our ability to understand the intentions, emotions and beliefs of others. The mirror system allows us to understand other people's motor actions and action intentions. ‘Empathy’ allows us to understand and share emotions and sensations with others. ‘Theory of mind’ allows us to understand more abstract concepts such as beliefs or wishes in others. In all these cases, evidence has accumulated that we use the specific neural networks engaged in processing mental states in ourselves to understand the same mental states in others. However, the magnitude of the brain activity in these shared networks is modulated by contextual appraisal of the situation or the other person. An important feature of decision making in a social setting concerns the interaction of reason and emotion. We consider four domains where such interactions occur: our sense of fairness, altruistic punishment, trust and framing effects. In these cases, social motivations and emotions compete with each other, while higher-level control processes modulate the interactions of these low-level biases.

Sign in / Sign up

Export Citation Format

Share Document