A Learning Rule for Extracting Spatio-Temporal Invariances

James V. Stone and Alistair Bray

The inputs to photoreceptors tend to change rapidly over time, whereas physical parameters (e.g. surface depth) underlying these changes vary more slowly. Accordingly, if a neuron codes for a physical parameter then its output should also change slowly, despite its rapidly fluctuating inputs. We demonstrate that a model neuron which adapts to make its output vary smoothly over time can learn to extract invariances implicit in its input. This learning consists of a linear combination of Hebbian and anti-Hebbian synaptic changes, operating simultaneously upon the {\em same} connection weights but at different time scales. This is shown to be sufficient for the unsupervised learning of simple spatio-temporal invariances.

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