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Learning Classifier Systems (LCS) are population-based reinforcement learnersthat were originally designed to model various cognitive phenomena. This paperpresents an explicitly cognitive LCS by using spiking neural networks asclassifiers, providing each classifier with a measure of temporal dynamism. Weemploy a constructivist model of growth of both neurons and synapticconnections, which permits a Genetic Algorithm (GA) to automatically evolvesufficiently-complex neural structures. The spiking classifiers are coupledwith a temporally-sensitive reinforcement learning algorithm, which allows thesystem to perform temporal state decomposition by appropriately rewarding"macro-actions," created by chaining together multiple atomic actions. Thecombination of temporal reinforcement learning and neural informationprocessing is shown to outperform benchmark neural classifier systems, andsuccessfully solve a robotic navigation task.

A Cognitive Architecture Based on a Learning Classifier System with Spiking Classifiers