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Neural Computation

Fall 1991, Vol. 3, No. 3, Pages 312-320
(doi: 10.1162/neco.1991.3.3.312)
© 1991 Massachusetts Institute of Technology
Removing Time Variation with the Anti-Hebbian Differential Synapse
Article PDF (482.67 KB)

I describe a local synaptic learning rule that can be used to remove the effects of certain types of systematic temporal variation in the inputs to a unit. According to this rule, changes in synaptic weight result from a conjunction of short-term temporal changes in the inputs and the output. Formally,

This is like the differential rule proposed by Klopf (1986) and Kosko (1986), except for a change of sign, which gives it an anti-Hebbian character. By itself this rule is insufficient. A weight conservation condition is needed to prevent the weights from collapsing to zero, and some further constraint — implemented here by a biasing term — to select particular sets of weights from the subspace of those which give minimal variation. As an example, I show that this rule will generate center-surround receptive fields that remove temporally varying linear gradients from the inputs.