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Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2016

01.08.2016

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

verfasst von: David Howard, Larry Bull, Pier-Luca Lanzi

Erschienen in: Neural Processing Letters | Ausgabe 1/2016

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Abstract

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 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.
Vorheriger Artikel Computation by Time
Nächster Artikel A Network of Neural Oscillators for Fractal Pattern Recognition

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Metadaten
Titel
A Cognitive Architecture Based on a Learning Classifier System with Spiking Classifiers
verfasst von
David Howard
Larry Bull
Pier-Luca Lanzi
Publikationsdatum
01.08.2016
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2016
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-015-9451-4

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