  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.
