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