Neural responses in cortex exhibit substantial trial-to-trial variability in response to repeated stimuli, while peripheral sensory neurons respond far more consistently, leading many to wonder whether stochasticity may carry meaning. Existing work has argued that noise and signal correlations may be optimized for discrimination in animals, whereas artificial neural network (ANN) studies have shown similar benefits of noise in machine learning tasks, although most ANN work has neglected the effects of correlations. Here we investigate whether correlated noise improves the robustness of artificial neural networks to adversarial attacks and naturalistic image modifications. Using the covariance of activations under modified versus clean inputs, we find that structured noise may significantly improve network robustness. Robustness to naturalistic image modifications benefits most from structure, but this structure transfers poorly across modification types. In contrast, noise structure from adversarial attacks can generalize to other kinds of attacks. These results suggest that structured noise in ANN activations generally improves robustness, establishing a biologically plausible strategy for creating robust artificial neural networks that only relies on local information.

翻译：大脑皮层中的神经响应在重复刺激下表现出显著的试次间变异性，而外周感觉神经元的响应则更为一致，这使许多研究者思考随机性是否可能携带意义。已有研究指出，动物的噪声与信号相关性可能被优化以提升判别能力，而人工神经网络研究也表明噪声在机器学习任务中具有类似益处，但多数人工神经网络研究忽视了相关性的影响。本文探讨了相关性噪声能否增强人工神经网络对对抗攻击及自然图像修改的鲁棒性。通过比较修改与干净输入下的激活协方差，我们发现结构化噪声能显著提升网络鲁棒性。自然图像修改的鲁棒性从结构化噪声中获益最大，但这种结构在不同修改类型间的迁移效果较差。相比之下，对抗攻击产生的噪声结构可泛化至其他攻击类型。这些结果表明，人工神经网络激活中的结构化噪声通常能增强鲁棒性，从而建立一种仅依赖局部信息的、具备生物学合理性的鲁棒人工神经网络构建策略。