ABSTRACT
There are many appealing aspects of self-organizing learning rules, among them the notion that they are more “biologically plausible” than supervised learning algorithms. This plausibility usually derives from the ability to compute the algorithm with variables available locally to the unit or “neuron”, typically using some variant of a Hebbian learning rule. Ironically, however, the locality in time of the variables that determine the learning is often ignored. This temporal non-locality presents a problem both from a biological and a psychological standpoint. In this paper, I present an alternative objective function for self-organizing algorithms that is local in both space and time, and results in a simple learning rule that can be implemented with properties of neuronal synaptic modification.
