Paper Info
Title
Pseudo-inverse control in biological systems: a learning mechanism for fixation stability.
Abstract
The problem of redundancy in motor control is common to both robotics and biology. Pseudo-inverse control has been proposed as a solution in robotics and appears to be used by the oculomotor system for eye position. Learning mechanisms for implementing pseudo-inverse control using a distributed system of ocular motor units were investigated by modelling integrator calibration for horizontal eye movements. Ocular motoneuron (OMN) input weights were adjusted with a gradient-descent learning rule, using a retinal-slip estimate as an error signal. Firing-rate threshold only became related to motor-unit strength when a noise term was added to OMN firing rates. The learning rule suppressed those units making the largest contribution to the noise-related error, causing the strongest units to have the highest thresholds (size principle). Because the size principle and pseudo-inverse control are related, the trained system approximated pseudo-inverse control over the central +/-35 degrees of the oculomotor range.
Year
DOI
Venue
1998
10.1016/S0893-6080(98)00072-0
Neural Networks
Keywords
Field
DocType
optimisation,pseudo-inverse control,fixation stability,recruitment,oculomotor integrator,distributed model,generalised inverse,biological system,size principle,redundancy,gradient descent,biological systems,eye movement,distributed system,motor control
Motion control,Control theory,Integrator,Eye movement,Redundancy (engineering),Artificial intelligence,Artificial neural network,Robotics,Mathematical optimization,Simulation,Motor control,Learning rule,Mathematics
Journal
Volume
Issue
ISSN
11
7-8
1879-2782
Citations 
PageRank 
References 
5
0.70
3
Authors
2
Name
Order
Citations
PageRank
Paul Dean19310.90
John Porrill235285.11