The beneficial role of noise in learning is nowadays a consolidated concept in the field of artificial neural networks, suggesting that even biological systems might take advantage of similar mechanisms to maximize their performance. The training-with-noise algorithm proposed by Gardner and collaborators is an emblematic example of a noise injection procedure in recurrent networks, which are usually employed to model real neural systems. We show how adding structure into noisy training data can substantially improve the algorithm performance, allowing to approach perfect classification and maximal basins of attraction. We also prove that the so-called Hebbian unlearning rule coincides with the training-with-noise algorithm when noise is maximal and data are fixed points of the network dynamics. A sampling scheme for optimal noisy data is eventually proposed and implemented to outperform both the training-with-noise and the Hebbian unlearning procedures.

翻译：噪声在学习中的有益作用如今已成为人工神经网络领域的一个成熟概念，表明生物系统也可能利用类似机制来最大化其性能。Gardner及其合作者提出的带噪声训练算法是递归网络中噪声注入过程的典型示例，这类网络通常用于模拟真实神经系统。我们证明，在含噪训练数据中加入结构能显著提升算法性能，从而逼近完美分类与最大吸引域。我们还证实，所谓的Hebbian去学习规则在噪声最大且数据为网络动力学不动点时，与带噪声训练算法完全等价。最终，我们提出并实现了一种最优噪声数据采样方案，其表现优于带噪声训练与Hebbian去学习两种方法。