New Insights on Learning Rules for Hopfield Networks: Memory and Objective Function Minimisation
Year: 2020
Type: conference-paper
Abstract: Hopfield neural networks are a possible basis for modelling associative memory in living organisms. After summarising previous studies in the field, we take a new look at learning rules, exhibiting them as descent-type algorithms for various cost functions. We also propose several new cost functions suitable for learning. We discuss the role of biases - the external inputs - in the learning process in Hopfield networks. Furthermore, we apply Newton's method for learning memories, and experimentally compare the performances of various learning rules. Finally, to add to the debate whether allowing connections of a neuron to itself enhances memory capacity, we numerically investigate the effects of self-coupling.
Authors: Pavel Tolmachev, Jonathan H. Manton
Institution: The University of Melbourne
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3090051662