  The nervous system encodes continuous information from the environment in the
form of discrete spikes, and then decodes these to produce smooth motor
actions. Understanding how spikes integrate, represent, and process information
to produce behavior is one of the greatest challenges in neuroscience.
Information theory has the potential to help us address this challenge.
Informational analyses of deep and feed-forward artificial neural networks
solving static input-output tasks, have led to the proposal of the
\emph{Information Bottleneck} principle, which states that deeper layers encode
more relevant yet minimal information about the inputs. Such an analyses on
networks that are recurrent, spiking, and perform control tasks is relatively
unexplored. Here, we present results from a Mutual Information analysis of a
recurrent spiking neural network that was evolved to perform the classic
pole-balancing task. Our results show that these networks deviate from the
\emph{Information Bottleneck} principle prescribed for feed-forward networks.
