DocumentCode
1682440
Title
Implementing position-invariant detection of feature-conjunctions in a network of spiking neurons
Author
Bohte, Sander M. ; Kok, Joost N. ; La Poutré, Han
Author_Institution
CWI, Amsterdam, Netherlands
Volume
2
fYear
2002
fDate
6/24/1905 12:00:00 AM
Firstpage
1097
Lastpage
1102
Abstract
The design of neural networks that are able to efficiently detect conjunctions of features is an important open challenge. We develop a feedforward spiking neural network that requires a constant number of neurons for detecting a conjunction irrespective of the size of the retinal input field, and for up to four simultaneously present feature-conjunctions
Keywords
data structures; feature extraction; feedforward neural nets; learning (artificial intelligence); context dependent thinning; data-structure; feature conjunction; feature extraction; feedforward neural networks; neural network architecture; position-invariant detection; relative proximity; spiking neurons; unsupervised learning; Computer vision; Detectors; Encoding; Feedforward neural networks; Feedforward systems; Intelligent networks; Neural networks; Neurons; Retina; Technology management;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location
Honolulu, HI
ISSN
1098-7576
Print_ISBN
0-7803-7278-6
Type
conf
DOI
10.1109/IJCNN.2002.1007647
Filename
1007647
Link To Document