Neural network approach to computing matrix inversion
Applied Mathematics and Computation
Neural networks for control systems: a survey
Automatica (Journal of IFAC)
A neural model of the saccade generator in the reticular formation
Neural Networks - Special issue on neural control and robotics: biology and technology
Development in a biologically inspired spinal neural network for movement control
Neural Networks - Special issue on neural control and robotics: biology and technology
The handbook of brain theory and neural networks
Motor control, biological and theoretical
The handbook of brain theory and neural networks
Introduction to Feedback Control Theory
Introduction to Feedback Control Theory
The Handbook of Brain Theory and Neural Networks
The Handbook of Brain Theory and Neural Networks
Rocking, Tapping and Stepping: A Prelude to Dance
Autonomous Robots
Anticipation Model for Sequential Learning of Complex Sequences
Sequence Learning - Paradigms, Algorithms, and Applications
On the Need for a Neural Abstract Machine
Sequence Learning - Paradigms, Algorithms, and Applications
Formulation of dynamics, actuation, and inversion of a three-dimensional two-link rigid body system
Journal of Robotic Systems
Coordinated three-dimensional motion of the head and torso bydynamic neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Anatomical, physiological and experimental research on the human body can be supplemented by computational synthesis of the human body for all movement: routine daily activities, sports, dancing, and artistic and exploratory involvements. The synthesis requires thorough knowledge about all subsystems of the human body and their interactions, and allows for integration of known knowledge in working modules. It also affords confirmation and/or verification of scientific hypotheses about workings of the central nervous system (CNS). A simple step in this direction is explored here for controlling the forces of constraint. It requires co-activation of agonist-antagonist musculature. The desired trajectories of motion and the force of contact have to be provided by the CNS. The spinal control involves projection onto a muscular subset that induces the force of contact. The projection of force in the sensory motor cortex is implemented via a well-defined neural population unit, and is executed in the spinal cord by a standard integral controller requiring input from tendon organs. The sensory motor cortex structure is extended to the case for directing motion via two neural population units with vision input and spindle efferents. Digital computer simulations show the feasibility of the system. The formulation is modular and can be extended to multi-link limbs, robot and humanoid systems with many pairs of actuators or muscles. It can be expanded to include reticular activating structures and learning.