We propose a new parallel-hierarchical neural network model to enable motor learning for simultaneous control of both trajectory and force. by integrating Hogan's control method and our previous neural network control model using a feedback-error-learning scheme. Furthermore. two hierarchical control laws which apply to the model, are derived by using the Moore-Penrose pseudoinverse matrix. One...

The oculomotor neural integrator is a brainstem nucleus that stores the short term memory of eye position during gaze holding. This neural integrator is believed to perform temporal integration of transient eye velocity input using positive feedback. Such models of integration are known to suffer from a fine-tuning problem [9] since changes in the amount of feedback by less than 10% can de-tune...

This paper investigates a learning control using iterative error compensation for uncertain systems to enhance the precision of high speed, computer controlled machining process. It is especially useful in mass-produced parts produced by high speed machines tool system. This method uses iterative learning technique which adopts machine commands and cutting error experienced in previous maneuver...

The brain processes information through multiple layers of neurons. This deep architecture is representationally powerful, but complicates learning because it is difficult to identify the responsible neurons when a mistake is made. In machine learning, the backpropagation algorithm assigns blame by multiplying error signals with all the synaptic weights on each neuron's axon and further downstr...

This study examined motivational effects of feedback on motor learning. Specifically, we investigated the influence of social-comparative feedback on the learning of a balance task (stabilometer). In addition to veridical feedback (error scores reflecting deviation from the target horizontal platform position) about their own performance after each trial, two groups received false normative inf...

Cognitive Neuroscience studies have identified an early warning system in the human brain that can avoid to make past mistakes again. They have shown how the brain remembers details about past dangers [1]. An activity was found in the Anterior Cingulate Cortex (ACC) after making mistakes [2]. This cortex area works as an early warning system that adjusts its behaviour to avoid dangerous situati...