Luttrell S P, April 1998, A Bayesian analysis of complementary approaches to training self-organising stochastic networks, DERA technical report (Malvern, UK), DERA/CIS/CIS5/TR97165
It is shown that two apparently different approaches to the optimisation of unsupervised neural networks both emerge from the same theory of network optimisation. The objective function which achieves this unification measures the average Euclidean reconstruction distortion that occurs when the network encodes its input, and then subsequently attempts to reconstruct its input. The two approaches then emerge as different ways of encoding the input using the same network: either a fixed number of neural firing events is used as the coded version of the input, or all the firing events that occur during a fixed time interval are used.