title: Analogue synaptic noise--implications and learning improvements.
abstract: We analyse the effects of analogue noise on the synaptic arithmetic during multilayer perceptron training by expanding the cost function to include noise-mediated penalty terms. Predictions are made in the light of these calculations which suggest that fault tolerance, generalisation ability and learning trajectory should be improved by such noise-injection. Extensive simulation experiments on two distinct classification problems substantiate the claims. The results appear to be perfectly general for all training schemes where weights are adjusted incrementally, and have wide-ranging implications for all applications, particularly those involving "inaccurate" analogue neural VLSI.
authors.affiliation: Department of Electrical Engineering, Edinburgh University, Scotland.
authors.forename: P J
authors.initials: PJ
authors.lastname: Edwards
authors.forename: A F
authors.initials: AF
authors.lastname: Murray
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003202:Computers, Analog; D016571:Neural Networks, Computer; D013569:Synapses