  Using the reservoir computing framework, we demonstrate how a simple model
can solve an alternation task without an explicit working memory. To do so, a
simple bot equipped with sensors navigates inside a 8-shaped maze and turns
alternatively right and left at the same intersection in the maze. The analysis
of the model's internal activity reveals that the memory is actually encoded
inside the dynamics of the network. However, such dynamic working memory is not
accessible such as to bias the behavior into one of the two attractors (left
and right). To do so, external cues are fed to the bot such that it can follow
arbitrary sequences, instructed by the cue. This model highlights the idea that
procedural learning and its internal representation can be dissociated. If the
former allows to produce behavior, it is not sufficient to allow for an
explicit and fine-grained manipulation.
