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A Cognitive Architecture Based on a Learning Classifier System with Spiking Classifiers
Authors
AG Pipe
AL Hodgkin
+33 more
AW Churchill
C Fernando
C Fernando
D Floreano
D Rumelhart
David Howard
G Buzsaki
H Hagras
H Shouval
I Rechenberg
J Hurst
J Hurst
JA Boyan
JH Holland
JH Holland
JH Holland
JY Donnart
L Bull
Larry Bull
M Dorigo
O Michel
Pier-Luca Lanzi
R Preen
RD Beer
RS Sutton
RS Sutton
S Fauer
SR Quartz
SW Wilson
W Gerstner
W Maass
W Schultz
WM Kistler
Publication date
1 January 2015
Publisher
'Springer Science and Business Media LLC'
Doi
Cite
Abstract
© 2015, Springer Science+Business Media New York. 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
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UWE Bristol Research Repository
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Archivio istituzionale della ricerca - Politecnico di Milano
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