  Learning Classifier Systems (LCS) are population-based reinforcement learners
that were originally designed to model various cognitive phenomena. This paper
presents an explicitly cognitive LCS by using spiking neural networks as
classifiers, providing each classifier with a measure of temporal dynamism. We
employ a constructivist model of growth of both neurons and synaptic
connections, which permits a Genetic Algorithm (GA) to automatically evolve
sufficiently-complex neural structures. The spiking classifiers are coupled
with a temporally-sensitive reinforcement learning algorithm, which allows the
system to perform temporal state decomposition by appropriately rewarding
"macro-actions," created by chaining together multiple atomic actions. The
combination of temporal reinforcement learning and neural information
processing is shown to outperform benchmark neural classifier systems, and
successfully solve a robotic navigation task.
