Recent research demonstrate that prediction of time series by predictive recurrent neural networks based on the noisy input generates a smooth anticipated trajectory. We examine influence of the noise component in both the training data sets and the input sequences on network prediction quality. We propose and discuss an explanation of the observed noise compression in the predictive process. We also discuss importance of this property of recurrent networks in the neuroscience context for the evolution of living organisms.

翻译：近期研究表明，基于噪声输入的预测循环神经网络对时间序列的预测会生成平滑的期望轨迹。本文探究了噪声成分在训练数据集和输入序列中对网络预测质量的影响，并提出了对观察到的噪声压缩在预测过程中的解释。我们还讨论了循环网络这一特性在前景学中如何解释生物进化。