DocumentCode :
3262466
Title :
Brainchild: a fault tolerant neural network
Author :
Kidwell
Author_Institution :
AT&T Bell Lab., Naperville, IL, USA
fYear :
1989
fDate :
0-0 1989
Abstract :
Summary form only given, as follows. Brainchild is a neural network designed to bridge the gap between current neural models and the brain. It models the physical organization of neurons by using both feedforward and lateral connections. It also has a high degree of fault tolerance in keeping with neural connections. A series of tests were run on both Brainchild and a Hopfield model network to compare fault tolerance. Both hard and soft faults were used, as well as combinations of the two. Brainchild proved to be the more fault tolerant of the two.<>
Keywords :
brain models; fault tolerant computing; neural nets; parallel architectures; Brainchild; Hopfield model network; brain; fault tolerant neural network; feedforward; hard faults; lateral connections; neural models; physical organization; soft faults; Brain modeling; Computer fault tolerance; Neural networks; Parallel architectures;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1989. IJCNN., International Joint Conference on
Conference_Location :
Washington, DC, USA
Type :
conf
DOI :
10.1109/IJCNN.1989.118474
Filename :
118474
Link To Document :
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