  Recurrent Neural Networks (RNNs) have shown great success in modeling
time-dependent patterns, but there is limited research on their learned
representations of latent temporal features and the emergence of these
representations during training. To address this gap, we use timed automata
(TA) to introduce a family of supervised learning tasks modeling behavior
dependent on hidden temporal variables whose complexity is directly
controllable. Building upon past studies from the perspective of dynamical
systems, we train RNNs to emulate temporal flipflops, a new collection of TA
that emphasizes the need for time-awareness over long-term memory. We find that
these RNNs learn in phases: they quickly perfect any time-independent behavior,
but they initially struggle to discover the hidden time-dependent features. In
the case of periodic "time-of-day" aware automata, we show that the RNNs learn
to switch between periodic orbits that encode time modulo the period of the
transition rules. We subsequently apply fixed point stability analysis to
monitor changes in the RNN dynamics during training, and we observe that the
learning phases are separated by a bifurcation from which the periodic behavior
emerges. In this way, we demonstrate how dynamical systems theory can provide
insights into not only the learned representations of these models, but also
the dynamics of the learning process itself. We argue that this style of
analysis may provide insights into the training pathologies of recurrent
architectures in contexts outside of time-awareness.
