scholarly journals Cortex-independent open-loop control of a voluntary orofacial motor action

2021 ◽  
Author(s):  
Michael Elbaz ◽  
Maxime Demers ◽  
David Kleinfeld ◽  
Christian Ethier ◽  
Martin Deschenes
Keyword(s):  
Motor Cortex ◽  
Internal Model ◽  
Sensory Feedback ◽  
Red Nucleus ◽  
Open Loop ◽  
Open Loop Control ◽  
Motor Action ◽  
Loop Control ◽  
Perceptual Stability ◽  
Facial Motoneurons

Whether using our eyes or our hands, we interact with our environment through mobile sensors. The efficient use of these sensory organs implies the ability to track their position; otherwise, perceptual stability and prehension would be profoundly impeded. The nervous system may be informed about the position of a sensory organ via two complementary feedback mechanisms: peripheral reafference (external, sensory feedback) and efference copy (internal feedback). Yet, the potential contributions of these mechanisms remain largely unexplored. By training rats to place their vibrissae within a predetermined angular range without contact, a task that depends on knowledge of vibrissa position relative to their face, we found that peripheral reafference is not required. The presence of motor cortex is not required either, even in the absence of peripheral reafference. On the other hand, the red nucleus, which receives descending inputs from motor cortex and the cerebellum and projects to facial motoneurons, is critical for the execution of the vibrissa task. All told, our results demonstrate the existence of an open-loop control by an internal model that is sufficient to drive voluntary motion. The internal model is independent of motor cortex and likely contains the cerebellum and associated nuclei.

scholarly journals Cortex-independent open-loop control of a voluntary orofacial motor action

2021 ◽  
Author(s):  
Michael Elbaz ◽  
Maxime Demers ◽  
David Kleinfeld ◽  
Christian Ethier ◽  
Martin Deschenes
Keyword(s):  
Motor Cortex ◽  
Internal Model ◽  
Sensory Feedback ◽  
Red Nucleus ◽  
Open Loop ◽  
Open Loop Control ◽  
Motor Action ◽  
Loop Control ◽  
Perceptual Stability ◽  
Facial Motoneurons

Abstract Whether using our eyes or our hands, we interact with our environment through mobile sensors. The efficient use of these sensory organs implies the ability to track their position; otherwise, perceptual stability and prehension would be profoundly impeded. The nervous system may be informed about the position of a sensory organ via two complementary feedback mechanisms: peripheral reafference (external, sensory feedback) and efference copy (internal feedback). Yet, the potential contributions of these mechanisms remain largely unexplored. By training rats to place their vibrissae within a predetermined angular range without contact, a task that depends on knowledge of vibrissa position relative to their face, we found that peripheral reafference is not required. The presence of motor cortex is not required either, even in the absence of peripheral reafference. On the other hand, the red nucleus, which receives descending inputs from motor cortex and the cerebellum and projects to facial motoneurons, is critical for the execution of the vibrissa task. All told, our results demonstrate the existence of an open-loop control by an internal model that is sufficient to drive voluntary motion. The internal model is independent of motor cortex and likely contains the cerebellum and associated nuclei.

scholarly journals Stochastic optimal open-loop control as a theory of force and impedance planning via muscle co-contraction

2019 ◽  
Author(s):  
Bastien Berret ◽  
Frédéric Jean
Keyword(s):  
Motor Control ◽  
Sensory Feedback ◽  
Neural Control ◽  
Motor Planning ◽  
Mechanical Impedance ◽  
Open Loop ◽  
Open Loop Control ◽  
Loop Control ◽  
Motor Commands ◽  
Neural Control Of Movement

AbstractUnderstanding the underpinnings of biological motor control is an important issue in movement neuroscience. Optimal control theory is a leading framework to rationalize this problem in computational terms. Previously, optimal control models have been devised either in deterministic or in stochastic settings to account for different aspects of motor control (e.g. average behavior versus trial-to-trial variability). While these approaches have yielded valuable insights about motor control, they typically fail explain a common phenomenon known as muscle co-contraction. Co-contraction of agonist and antagonist muscles contributes to modulate the mechanical impedance of the neuromusculoskeletal system (e.g. joint stiffness) and is thought to be mainly under the influence of descending signals from the brain. Here we present a theory suggesting that one primary goal of motor planning may be to issue feedforward (open-loop) motor commands that optimally specify both force and impedance, according to the noisy neuromusculoskeletal dynamics and to optimality criteria based on effort and variance. We show that the proposed framework naturally accounts for several previous experimental findings regarding the regulation of force and impedance via muscle co-contraction in the upper-limb. Optimal feedback (closedloop) control, preprogramming feedback gains but requiring on-line state estimation processes through long-latency sensory feedback loops, may then complement this nominal feedforward motor command to fully determine the limb’s mechanical impedance. The stochastic optimal open-loop control theory may provide new insights about the general articulation of feedforward/feedback control mechanisms and justify the occurrence of muscle co-contraction in the neural control of movement.Author summaryThis study presents a novel computational theory to explain the planning of force and impedance (e.g. stiffness) in the neural control of movement. It assumes that one main goal of motor planning is to elaborate feedforward motor commands that determine both the force and the impedance required for the task at hand. These feedforward motor commands (i.e. that are defined prior to movement execution) are designed to minimize effort and variance costs considering the uncertainty arising from sensorimotor noise. A major outcome of this mathematical framework is the explanation of a long-known phenomenon called muscle co-contraction (i.e. the concurrent contraction of opposing muscles). Muscle co-contraction has been shown to occur in many situations but previous modeling works struggled to account for it. Although effortful, co-contraction contributes to increase the robustness of motor behavior (e.g. small variance) upstream of sophisticated optimal feedback control processes that require state estimation from delayed sensory feedback to function. This work may have implications regarding our understanding of the neural control of movement in computational terms. It also provides a theoretical ground to explain how to optimally plan force and impedance within a general and versatile framework.

Open-loop control of compensation for optical propagation through a turbulent shear flow

1998 ◽  
Author(s):  
C. Truman ◽  
Lenore McMackin ◽  
Robert Pierson ◽  
Kenneth Bishop ◽  
Ellen Chen
Keyword(s):  
Shear Flow ◽  
Open Loop ◽  
Turbulent Shear ◽  
Turbulent Shear Flow ◽  
Open Loop Control ◽  
Loop Control ◽  
Optical Propagation

Precise open-loop control of MEMS deformable mirror shape

2008 ◽  
Author(s):  
Thomas Bifano ◽  
Jason Stewart ◽  
Alioune Diouf
Keyword(s):  
Open Loop ◽  
Deformable Mirror ◽  
Open Loop Control ◽  
Loop Control

Responses and Optimization of a PID-Based Speed Regulating System of the Planetary Mixer

2011 ◽  
Vol 418-420 ◽  
pp. 1865-1868
Author(s):  
Ming Jin Yang ◽  
Xi Wen Li ◽  
Zhi Gang Wang ◽  
Tie Lin Shi
Keyword(s):  
Control System ◽  
Open Loop ◽  
Open Loop Control ◽  
Efficient Operation ◽  
Loop Control ◽  
Loop Control System ◽  
Response Optimization ◽  
Close Loop Control ◽  
Close Loop Control System

The performance of speed regulating is very important to the mixing process with safe, efficient operation and high quality of production. Strategies and practices of responses and optimization of a PID-based speed regulating system of a planetary mixer were presented in this paper. Research results show that: by means of the signal constraint function presented by Simulink Response Optimization, optimization PID parameters of the 2-DOF-PID controller can be obtained, and the response of close-loop control system has quite good performance of overshoot, response time, and stability compared with an open-loop control system.

Experimental Verification of Open-loop Control for an Underwater Eel-like Robot

2002 ◽  
Vol 21 (10-11) ◽  
pp. 849-859 ◽  
Author(s):  
Kenneth A. Mcisaac ◽  
James P. Ostrowski
Keyword(s):  
Experimental Work ◽  
Experimental Verification ◽  
Vision System ◽  
Open Loop ◽  
Open Loop Control ◽  
Loop Control ◽  
Position Sensing ◽  
Theoretical Predictions ◽  
Approximate Expressions ◽  
Good Agreement

In this paper, we describe experimental work using an underwater, biomimetic, eel-like robot to verify a simplified dynamic model and open-loop control routines. We compare experimental results to previous analytically derived, but approximate expressions for proposed gaits for forward/backward swimming, circular swimming, sideways swimming and turning in place. We have developed a five-link, underwater eel-like robot, focusing on modularity, reliability and rapid prototyping, to verify our theoretical predictions. Results from open-loop experiments performed with this robot in an aquatic environment using an off-line vision system for position sensing show good agreement with theory.

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