Comparing immune and neural networks
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...
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| creator | de Castro, L.N. |
| description | 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. |
| doi_str_mv | 10.1109/SBRN.2002.1181486 |
| format | Conference Proceeding |
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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. 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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.</description><subject>Artificial neural networks</subject><subject>Biological neural networks</subject><subject>Biological system modeling</subject><subject>Computer networks</subject><subject>Immune system</subject><subject>Nervous system</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>Pattern recognition</subject><subject>Viruses (medical)</subject><isbn>9780769517094</isbn><isbn>0769517099</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2002</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj0tLw0AUhQeKYKn5AeImC7epc-fOc6lBrVAUfKzL7eSmTG3SMmkR_70BezYfZ_NxjhDXIOcAMtx9PLy_zpWUaqwetLcTUQTnpbPBgJNBX4piGLZyjDZgrJmK23rfHSinflOmrjv1XFLflD2fMu1GHH_2-Xu4Ehct7QYuzpyJr6fHz3pRLd-eX-r7ZZXA4bHyOtqGY6sCNaxRGYYgo_aIpnXkxhnroO2aHdkGjXUKkTB6phgwOAs4Ezf_3sTMq0NOHeXf1fkL_gHRFj29</recordid><startdate>2002</startdate><enddate>2002</enddate><creator>de Castro, L.N.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2002</creationdate><title>Comparing immune and neural networks</title><author>de Castro, L.N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i173t-84c6decf29ade4325e190c48335f7a7076b946be7a6d3567233a3c8eac9397613</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2002</creationdate><topic>Artificial neural networks</topic><topic>Biological neural networks</topic><topic>Biological system modeling</topic><topic>Computer networks</topic><topic>Immune system</topic><topic>Nervous system</topic><topic>Neural networks</topic><topic>Neurons</topic><topic>Pattern recognition</topic><topic>Viruses (medical)</topic><toplevel>online_resources</toplevel><creatorcontrib>de Castro, L.N.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>de Castro, L.N.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Comparing immune and neural networks</atitle><btitle>VII Brazilian Symposium on Neural Networks, 2002. SBRN 2002. Proceedings</btitle><stitle>SBRN</stitle><date>2002</date><risdate>2002</risdate><spage>250</spage><epage>255</epage><pages>250-255</pages><isbn>9780769517094</isbn><isbn>0769517099</isbn><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.</abstract><pub>IEEE</pub><doi>10.1109/SBRN.2002.1181486</doi><tpages>6</tpages></addata></record> |
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| ispartof | VII Brazilian Symposium on Neural Networks, 2002. SBRN 2002. Proceedings, 2002, p.250-255 |
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| language | eng |
| recordid | cdi_ieee_primary_1181486 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial neural networks Biological neural networks Biological system modeling Computer networks Immune system Nervous system Neural networks Neurons Pattern recognition Viruses (medical) |
| title | Comparing immune and neural networks |
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