DocumentCode
1749074
Title
Recurrent neural networks and symbol grounding
Author
Spiegel, Rainer ; McLaren, IPL
Author_Institution
Dept. of Exp. Psychol., Cambridge Univ., UK
Volume
1
fYear
2001
fDate
2001
Firstpage
320
Abstract
It is demonstrated that a recurrent neural network relying on statistics alone is able to differentiate between the classical Aristotelian categories odd and even number. This finding overlaps with the associative part of the hybrid associative/cognitive learning system in humans who sometimes differentiate between both categories unknowingly, i.e. without explicit rules
Keywords
psychology; recurrent neural nets; classical Aristotelian categories; even number; humans; hybrid associative/cognitive learning system; odd number; recurrent neural networks; symbol grounding; Cognition; Cognitive science; Grounding; Humans; Knowledge based systems; Learning systems; Psychology; Recurrent neural networks; Statistics; Switches;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-7044-9
Type
conf
DOI
10.1109/IJCNN.2001.939039
Filename
939039
Link To Document