DocumentCode :
3208805
Title :
Comparing immune and neural networks
Author :
De Castro, Leandro Nunes
Author_Institution :
Dept. of Comput. Eng. & Ind. Autom., State Univ. of Campinas, Brazil
fYear :
2002
fDate :
2002
Firstpage :
250
Lastpage :
255
Abstract :
The complexity of the immune system is sometimes compared to that of the brain. Both systems can be viewed as composed of networks of elements, which endow them with interesting features for the development of computational tools with potentialities for problem solving. This paper has two main goals: 1) to introduce the general features of immune networks to the artificial neural network (ANN) community; and 2) to present a theoretical comparison between an ANN and a standard immune network. The comparison is highly simplified and general, taking into account how each network is structured, their basic components and mechanisms of adaptation, and information processing capabilities.
Keywords :
learning (artificial intelligence); neural nets; pattern recognition; antigenic recognition; differential equations; immune networks; immune system; information processing; learning algorithms; neural network; pattern recognition; Artificial neural networks; Biological neural networks; Biological system modeling; Computer networks; Immune system; Nervous system; Neural networks; Neurons; Pattern recognition; Viruses (medical);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2002. SBRN 2002. Proceedings. VII Brazilian Symposium on
Print_ISBN :
0-7695-1709-9
Type :
conf
DOI :
10.1109/SBRN.2002.1181486
Filename :
1181486
Link To Document :
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