  A learning algorithm for multilayer neural networks based on biologically
plausible mechanisms is studied. Motivated by findings in experimental
neurobiology, we consider synaptic averaging in the induction of plasticity
changes, which happen on a slower time scale than firing dynamics. This
mechanism is shown to enable learning of the exclusive-OR (XOR) problem without
the aid of error back-propagation, as well as to increase robustness of
learning in the presence of noise.
