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性循环神经网络进行时间序列预测时，会生成平滑的预期轨迹。我们考察了训练数据集与输入序列中噪声分量对网络预测质量的影响。提出并讨论了预测过程中观测到的噪声压缩现象的解释机制，同时探讨了循环网络这一特性在神经科学背景下对生物体进化的意义。