A canonical microfunction for learning perceptual invariances /
Stone, James V.
Brighton, [East Sussex] : University of Sussex at Brighton,
[1995]
Abstract: "An unsupervised method is presented which permits a set of model neurons, or a microcircuit, to learn low level vision tasks, such as the extraction of surface depth.  Each microcircuit implements a simple, generic strategy which is based on a key assumption: Perceptually salient visual invariances, such as surface depth, vary smoothly over time.   In the process of learning to extract smoothly varying invariances, each microcircuit maximises a microfunction.  This is achieved using a learning rule which maximises the long-term variance of each unit's output, whilst simultaneously minimising its short-term variance.  The learning rule involves a linear combination of anti-Hebbian and Hebbian weight changes, over short and long time scales, respectively.  The method is demonstrated on a hyper-acuity task; estimating sub-pixel stereo disparity from a temporal sequence of random-dot stereograms. After learning, the microcircuit generalises, without additional learning, to previously unseen  image sequences.  It is proposed that the approach adopted here may be used to define a canonical microfunction, which can be used to learn many perceptually salient invariances."
"Accepted for publication in Perception."
"December 13, 1995."
Includes bibliographical references.
Abstract: "An unsupervised method is presented which permits a set of model neurons, or a microcircuit, to learn low level vision tasks, such as the extraction of surface depth.  Each microcircuit implements a simple, generic strategy which is based on a key assumption: Perceptually salient visual invariances, such as surface depth, vary smoothly over time.   In the process of learning to extract smoothly varying invariances, each microcircuit maximises a microfunction.  This is achieved using a learning rule which maximises the long-term variance of each unit's output, whilst simultaneously minimising its short-term variance.  The learning rule involves a linear combination of anti-Hebbian and Hebbian weight changes, over short and long time scales, respectively.  The method is demonstrated on a hyper-acuity task; estimating sub-pixel stereo disparity from a temporal sequence of random-dot stereograms. After learning, the microcircuit generalises, without additional learning, to previously unseen  image sequences.  It is proposed that the approach adopted here may be used to define a canonical microfunction, which can be used to learn many perceptually salient invariances."
Supported in part by a Joint Council Initiative grant
Machine learning
Smoothness of functions.
Computer vision.
Integrated circuits.