Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

Spring 1989, Vol. 1, No. 1, Pages 133-142
(doi: 10.1162/neco.1989.1.1.133)
© 1989 Massachusetts Institute of Technology
Product Units: A Computationally Powerful and Biologically Plausible Extension to Backpropagation Networks
Article PDF (490.52 KB)
Abstract

We introduce a new form of computational unit for feedforward learning networks of the backpropagation type. Instead of calculating a weighted sum this unit calculates a weighted product, where each input is raised to a power determined by a variable weight. Such a unit can learn an arbitrary polynomial term, which would then feed into higher level standard summing units. We show how learning operates with product units, provide examples to show their efficiency for various types of problems, and argue that they naturally extend the family of theoretical feedforward net structures. There is a plausible neurobiological interpretation for one interesting configuration of product and summing units.