Storage Capacity of Associative Neural Networks: Simulations Across four Learning Rules

This project investigates the storage capacity and recall performance of associative memory networks using different biologically inspired learning rules. The aim is to investigate how different local learning rules influence the storage capacity and recall performance of attractor neural networks under varying conditions. These conditions include pattern sparsity, pattern correlation, and input noise. A discrete Hopfield network with 100 fully connected neurons was implemented in Python and pattern recall was tested using both sparse (10\%) and dense (50\%) binary and bipolar patterns with and without correlation and noise. Results show that the Storkey rule is most effective for dense, correlated patterns, while Willshaw and BCPNN perform best with sparse, uncorrelated inputs. The Hebbian learning rule demonstrated limited storage capacity in all scenarios. The findings highlight the trade-offs between learning rules and offer practical guidance for selecting memory models based on statistical properties of the data.