An objective function for self-limiting neural plasticity rules

Abstract

Self-organization provides a framework for the study of systems in which complex patterns emerge from simple rules, without the guidance of external agents or fine tuning of parameters. Within this framework, one can formulate a guiding principle for plasticity in the context of unsupervised learning, in terms of an objective function. In this work we derive Hebbian, self-limiting synaptic plasticity rules from such an objective function and then apply the rules to the non-linear bars problem.

To appear in the Proceedings of ESANN 2015