  Learning Classifier Systems (LCS) are population-based reinforcement learners
used in a wide variety of applications. This paper presents a LCS where each
traditional rule is represented by a spiking neural network, a type of network
with dynamic internal state. We employ a constructivist model of growth of both
neurons and dendrites that realise flexible learning by evolving structures of
sufficient complexity to solve a well-known problem involving continuous,
real-valued inputs. Additionally, we extend the system to enable temporal state
decomposition. By allowing our LCS to chain together sequences of heterogeneous
actions into macro-actions, it is shown to perform optimally in a problem where
traditional methods can fail to find a solution in a reasonable amount of time.
Our final system is tested on a simulated robotics platform.
