  The extension of deep learning towards temporal data processing is gaining an
increasing research interest. In this paper we investigate the properties of
state dynamics developed in successive levels of deep recurrent neural networks
(RNNs) in terms of short-term memory abilities. Our results reveal interesting
insights that shed light on the nature of layering as a factor of RNN design.
Noticeably, higher layers in a hierarchically organized RNN architecture
results to be inherently biased towards longer memory spans even prior to
training of the recurrent connections. Moreover, in the context of Reservoir
Computing framework, our analysis also points out the benefit of a layered
recurrent organization as an efficient approach to improve the memory skills of
reservoir models.
