Inclusion of novelties into AI systems is challenging. At present, a real efficient online training algorithm for deep learning systems has still not been introduced. We propose a general approach to train different temporal sequences based on Markov chains by exploiting the ability of neural networks to share the same weight parameter region among different temporal data. Some results will be shown concerning training meta-parameters and relative entropy between sequences.