It's a super deal -- train recurrent network on noisy data and get smooth prediction free

Recent research demonstrate that prediction of time series by predictive recurrent neural networks based on the noisy input generates a {\it 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.