A Canonical Microfunction For Learning Perceptual Invariances

James V. Stone

An unsupervised method is presented which permits a set of model neurons, or a {\em 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 {\em 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 {\em canonical microfunction}, which can be used to learn many perceptually salient invariances.

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