Recent advances in efficient learning of recurrent networks

Recurrent neural networks (RNNs) carry the promise of implementing efficient and biologically plausible signal processing. They both are optimally suited for a wide area of applications when dealing with spatiotemporal data or causalities and provide explanation of cog- nitive phenomena of the human brain. Recently, a few new fundamental paradigms connected to RNNs have been developed which allow insights into their potential for information processing. They also pave the way towards new efficient training algorithms which overcome the well-known problem of long-term dependencies. This tutorial gives an overview of this recent developments in efficient, biologically plausible recurrent informa- tion processing.