J
John Porrill
Researcher at University of Sheffield
Publications - 104
Citations - 3538
John Porrill is an academic researcher from University of Sheffield. The author has contributed to research in topics: Adaptive control & Motor learning. The author has an hindex of 31, co-authored 104 publications receiving 3377 citations.
Papers
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Journal ArticleDOI
Response linearity determined by recruitment strategy in detailed model of nictitating membrane control
TL;DR: The most detailed model available of the rabbit nictitating membrane response is implemented, in which each motor unit of the retractor bulbi muscle is represented by a Hill-type model, driven by a non-linear activation mechanism designed to reproduce the isometric force measurements of Lennerstrand.
Journal ArticleDOI
Kinematic coordination of reach and balance.
TL;DR: Two particular techniques are described: the Berkinblit algorithm, used to model the leg-wiping reflexes of the frog, and the parallel control scheme of Hinton, in which simple stick-man simulations maintain a balanced posture while reaching.
Journal ArticleDOI
An orientation anisotropy in the effects of scaling vertical disparities
John P. Frisby,David Buckley,Helen Grant,Jonas Gårding,Janet Horsman,Stephen D Hippisley-Cox,John Porrill +6 more
TL;DR: Two psychophysical experiments tested the prediction that scaling vertical disparities to simulate different viewing distances to the fixation point should affect the perceived amplitudes of vertically but not horizontally oriented ridges and broadly confirmed the anisotropy prediction by finding that large scalings of vertical disparities simulating near distances had a strong effect on the perceived Amplitudes of the vertically oriented stimuli but little impact on the horizontal ones.
Journal ArticleDOI
Recovering partial 3D wire frames descriptions from stereo data
TL;DR: The design of modules in the current version of the TINA stereo based 3D vision system responsible for the recovery of geometric descriptions and their subsequent integration into partial wire frame models are described.
Book ChapterDOI
Oculomotor anatomy and the motor-error problem: the role of the paramedian tract nuclei.
Paul Dean,John Porrill +1 more
TL;DR: By modelling the flocculus as an adaptive filter using a covariance learning rule, it is shown that in simulation the cerebellar cortex can in fact learn to decorrelate efference copy from motor command, and thereby compensate for changes to the oculomotor plant.