An objective function for self-limiting neural plasticity rules

TL;DR

This paper introduces an objective function-based approach to derive self-limiting Hebbian plasticity rules for neural networks, demonstrating their effectiveness on the non-linear bars problem.

Contribution

It presents a novel derivation of self-limiting Hebbian rules from an objective function within a self-organization framework.

Findings

Derived Hebbian rules from an objective function

Applied rules to the non-linear bars problem

Showed effectiveness of the rules in unsupervised learning

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.