DocumentCode :
285265
Title :
An examination of real-time neuronal models in a classical conditioning framework
Author :
Cheung, John Y. ; Chance, David C. ; Lawton, Asa
Author_Institution :
Oklahoma Univ., Norman, OK, USA
Volume :
3
fYear :
1992
fDate :
7-11 Jun 1992
Firstpage :
504
Abstract :
The focus is on the unsupervised spatiotemporal single-neuron models of R.S. Sutton and A.G. Barto (1981, 1982), R.A. Rescorla and A.R. Wagner (1972), and A. Klopf. The preliminary results of the learning and activation rules of these and other single-neuron models are presented. Computer simulations of the models were studied within the framework of modern Pavlovian associative learning. The simulation environment was a set of C programs representing the models and an interactive environment which allowed the user to select one of seven different learning strategies
Keywords :
neural nets; unsupervised learning; activation rules; classical conditioning framework; modern Pavlovian associative learning; real-time neuronal models; unsupervised spatiotemporal single-neuron models; Biological system modeling; Computer simulation; Context modeling; Current measurement; Frequency measurement; Mathematical model; Modems; Neurons; Predictive models; Time measurement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location :
Baltimore, MD
Print_ISBN :
0-7803-0559-0
Type :
conf
DOI :
10.1109/IJCNN.1992.227124
Filename :
227124
Link To Document :
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