The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Alan Rosen - One of the best experts on this subject based on the ideXlab platform.
-
IJCNN - A Robotic Neural Net Based Visual-sensory Motor Control System that Reverse Engineers the Motor Control Functions of the Human Brain
2007 International Joint Conference on Neural Networks, 2007Co-Authors: David B Rosen, Alan RosenAbstract:The design of the neural net based visual-robotic Controller, Controlling a tactile "itch-scratch" robotic sensory motor Control system is presented. The "itch-scratch" robotic motor Control system is described in referenced and linked publications. The design of the visual-robotic system is obtained by adding an obstacle avoiding visual system to the sensory motor Control functions of the tactile "itch-scratch" robotic system. The visual-robotic Controller is unique in that the coordinate frame in which the robot is operating, determined by the optical visual sensors, is reflected onto a neural network located within the robotic Controller. The associated visual and tactile sensory motor Control systems within the Controller may lead to insight into the biological pathways in the brain for 3D-optical imaging and sensory motor Control with feedback from the somatic body sensors.
-
An Electromechanical Neural Network Robotic Model of the Human Body and Brain : Sensory-Motor Control by Reverse Engineering Biological Somatic Sensors
Lecture Notes in Computer Science, 2006Co-Authors: Alan Rosen, David B RosenAbstract:This paper presents an electromechanical robotic model of the human body and brain. The model is designed to reverse engineer some biological functional aspects of the human body and brain. The functional aspects includes reverse engineering, a) biological perception by means of sensory monitoring of the external world, b) self awareness by means of monitoring the location and identification of all parts of the robotic body, and c) biological sensory motor Control by means of feedback monitoring of the internal reaction of the robotic body to external forces. The model consists of a mechanical robot body Controlled by a neural network based Controller.
-
ICONIP (1) - An electromechanical neural network robotic model of the human body and brain: Sensory-Motor Control by reverse engineering biological somatic sensors
Neural Information Processing, 2006Co-Authors: Alan Rosen, David B RosenAbstract:This paper presents an electromechanical robotic model of the human body and brain. The model is designed to reverse engineer some biological functional aspects of the human body and brain. The functional aspects includes reverse engineering, a) biological perception by means of “sensory monitoring” of the external world, b) “self awareness” by means of monitoring the location and identification of all parts of the robotic body, and c) “biological sensory motor Control” by means of feedback monitoring of the internal reaction of the robotic body to external forces. The model consists of a mechanical robot body Controlled by a neural network based Controller.
-
IJCNN - A Neural Network Model of the Connectivity of the Biological Somatic Sensors
The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006Co-Authors: Alan Rosen, D. RosenAbstract:The connectivity of a neural network model is designed to be similar to the biological connectivity of the somatic body sensors. The model consists of a mechanical robot Controlled by a neural network based Controller that adheres to three functional characteristics commonly associated with the subjective experience of sensory sensations (modalities of sensors): a) self knowledge, b) a "world space"-coordinate system in a Controller, and c) access to information. The robotic Controller, called a relational robotic Controller (RRC)-circuit, Controls the robotic body by reverse engineering the operation of the animal and human body and brain so that the functional operation adheres to those three functional characteristics. The RRC-circuit model may lead to a Sensory-Motor Control system of the somatic motor system and insight into the biological pathways in the brain and the overall functional operation of the human body and brain.
C. Darlot - One of the best experts on this subject based on the ideXlab platform.
-
ICRA - Biologically inspired sensory motor Control of a 2-link robotic arm actuated by McKibben muscles
2011 IEEE International Conference on Robotics and Automation, 2011Co-Authors: Sofiane Ouanezar, Frédéric Jean, Bertrand Tondu, Marc A. Maier, C. Darlot, Selim EskiizmirlilerAbstract:This study focuses on biomimetic sensory motor Control of a robotic arm. We have developed a command circuit that was mathematically deduced from physical and mathematical constraints describing the function of cerebellar pathways. The Control circuit contains an internal predictive model of the direct biomechanical function of the limb placed in a closed loop, so that the circuit computes an approximate inverse function. The structure of the model resembles the anatomic connectivity of the cerebellar pathways. In this paper, we present an application of this model to the Control of a 2-link robotic arm actuated by four single-joint McKibben muscles and report the results obtained by simulation and real-time learning of 2 degrees of freedom pointing movements.
-
A model of the cerebellar sensory--motor Control applied to fast human forearm movements.
Journal of integrative neuroscience, 2008Co-Authors: Selim Eskiizmirliler, Charalambos Papaxanthis, Thierry Pozzo, C. DarlotAbstract:To address the problem of how the cerebellum processes the premotor orders that Control fast movements of the forearm, a model of the cerebellar Control is proposed: a cybernetic circuit composed of a model of the cerebellar premotor pathways driving a biomechanical model of the human forearm. Experiments consist of recording electromyographic (EMG) activities and cinematic variables of the human forearm during fast, single joint, point-to-point movements performed in horizontal and vertical directions with and without mass. The biomechanical model of the forearm is first validated by comparing actual movements and movements simulated by using, as inputs to this model, the synthesized EMG signals and of real EMG activities recorded during the experiments. Then the entire Control model is validated by comparing actual movements to the desired ones simulated by the model of the cerebellar pathways whose inputs are velocity signals with Gaussian time-courses. The results show that approximate inverse functions can be computed by means of inner models of direct functions placed in feedback loops, and suggest that the orientation of any member segment with respect to gravity is computed as a cinematic variable in the Central Nervous System (CNS).
-
WITHDRAWN: A model of the cerebellar Sensory-Motor Control applied to the fast human forearm movements.
Neuroscience, 2007Co-Authors: S. Eskiizmirliler, Thierry Pozzo, Charalambos Papaxanthis, C. DarlotAbstract:This article has been withdrawn consistent with Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). The Publisher apologizes for any inconvenience this may cause.
David B Rosen - One of the best experts on this subject based on the ideXlab platform.
-
IJCNN - A Robotic Neural Net Based Visual-sensory Motor Control System that Reverse Engineers the Motor Control Functions of the Human Brain
2007 International Joint Conference on Neural Networks, 2007Co-Authors: David B Rosen, Alan RosenAbstract:The design of the neural net based visual-robotic Controller, Controlling a tactile "itch-scratch" robotic sensory motor Control system is presented. The "itch-scratch" robotic motor Control system is described in referenced and linked publications. The design of the visual-robotic system is obtained by adding an obstacle avoiding visual system to the sensory motor Control functions of the tactile "itch-scratch" robotic system. The visual-robotic Controller is unique in that the coordinate frame in which the robot is operating, determined by the optical visual sensors, is reflected onto a neural network located within the robotic Controller. The associated visual and tactile sensory motor Control systems within the Controller may lead to insight into the biological pathways in the brain for 3D-optical imaging and sensory motor Control with feedback from the somatic body sensors.
-
An Electromechanical Neural Network Robotic Model of the Human Body and Brain : Sensory-Motor Control by Reverse Engineering Biological Somatic Sensors
Lecture Notes in Computer Science, 2006Co-Authors: Alan Rosen, David B RosenAbstract:This paper presents an electromechanical robotic model of the human body and brain. The model is designed to reverse engineer some biological functional aspects of the human body and brain. The functional aspects includes reverse engineering, a) biological perception by means of sensory monitoring of the external world, b) self awareness by means of monitoring the location and identification of all parts of the robotic body, and c) biological sensory motor Control by means of feedback monitoring of the internal reaction of the robotic body to external forces. The model consists of a mechanical robot body Controlled by a neural network based Controller.
-
ICONIP (1) - An electromechanical neural network robotic model of the human body and brain: Sensory-Motor Control by reverse engineering biological somatic sensors
Neural Information Processing, 2006Co-Authors: Alan Rosen, David B RosenAbstract:This paper presents an electromechanical robotic model of the human body and brain. The model is designed to reverse engineer some biological functional aspects of the human body and brain. The functional aspects includes reverse engineering, a) biological perception by means of “sensory monitoring” of the external world, b) “self awareness” by means of monitoring the location and identification of all parts of the robotic body, and c) “biological sensory motor Control” by means of feedback monitoring of the internal reaction of the robotic body to external forces. The model consists of a mechanical robot body Controlled by a neural network based Controller.
Patrick Lichtsteiner - One of the best experts on this subject based on the ideXlab platform.
-
fast sensory motor Control based on event based hybrid neuromorphic procedural system
International Symposium on Circuits and Systems, 2007Co-Authors: Tobi Delbruck, Patrick LichtsteinerAbstract:Fast Sensory-Motor processing is challenging when using traditional frame-based cameras and computers. Here the authors show how a hybrid neuromorphic-procedural system consisting of an address-event silicon retina, a computer, and a servo motor can be used to implement a fast Sensory-Motor reactive Controller to track and block balls shot at a goal. The system consists of a 128times128 retina that asynchronously reports scene reflectance changes, a laptop PC, and a servo motor Controller. Components are interconnected by USB. The retina looks down onto the field in front of the goal. Moving objects are tracked by an event-driven cluster tracker algorithm that detects the ball as the nearest object that is approaching the goal. The ball's position and velocity are used to Control the servo motor. Running under Windows XP, the reaction latency is 2.8plusmn0.5 ms at a CPU load of 1 million events per second (Meps), although fast balls only create ~30 keps. This system demonstrates the advantages of hybrid event-based sensory motor processing
-
ISCAS - Fast sensory motor Control based on event-based hybrid neuromorphic-procedural system
2007 IEEE International Symposium on Circuits and Systems, 2007Co-Authors: Tobi Delbruck, Patrick LichtsteinerAbstract:Fast Sensory-Motor processing is challenging when using traditional frame-based cameras and computers. Here the authors show how a hybrid neuromorphic-procedural system consisting of an address-event silicon retina, a computer, and a servo motor can be used to implement a fast Sensory-Motor reactive Controller to track and block balls shot at a goal. The system consists of a 128times128 retina that asynchronously reports scene reflectance changes, a laptop PC, and a servo motor Controller. Components are interconnected by USB. The retina looks down onto the field in front of the goal. Moving objects are tracked by an event-driven cluster tracker algorithm that detects the ball as the nearest object that is approaching the goal. The ball's position and velocity are used to Control the servo motor. Running under Windows XP, the reaction latency is 2.8plusmn0.5 ms at a CPU load of 1 million events per second (Meps), although fast balls only create ~30 keps. This system demonstrates the advantages of hybrid event-based sensory motor processing
Paul Van De Heyning - One of the best experts on this subject based on the ideXlab platform.
-
The assessment of cervical sensory motor Control: A systematic review focusing on measuring methods and their clinimetric characteristics
Gait & posture, 2012Co-Authors: Sarah Michiels, Willem De Hertogh, Steven Truijen, Danny November, Floris L. Wuyts, Paul Van De HeyningAbstract:Abstract Background Cervical sensorimotor Control (CSMC) becomes increasingly important in the assessment and treatment of patients with neck pain. This review aims to compare commonly used CSMC measuring methods in terms of required tasks, measuring device and clinimetric properties. Search methods A systematic review of two databases, followed by methodological quality assessment (CBO guidelines). Results The methodological quality of 34 included articles was generally good (five to seven out of eight), the inter-rater agreement was excellent ( κ w = 0.966, p Conclusions The dynamic method The Fly™ appears to be more reliable than the HRA-to-NHP and is able to discriminate between different patient populations. The diagnostic potential is to be confirmed in future research.
