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Conference Paper: Self-organized learning of receptive fields in multi-resolution

TitleSelf-organized learning of receptive fields in multi-resolution
Authors
KeywordsComputers
Artificial intelligence
Issue Date1995
PublisherIEEE.
Citation
The 1995 IEEE International Conference on Neural Networks, Perth, WA., Australia, 27 November-1 December 1995. In IEEE International Conference on Neural Networks Proceedings, 1995, v. 5, p. 2831-2834 How to Cite?
AbstractThe statistical likelihood of Gabor filters and primary visual cortex has been of interest for years, yet learning mechanisms proposed did not generate satisfactory Gabor-like receptive fields. In this paper, a new computational model of self-organised Hebbian learning (SOHL) is proposed to work on a multi-resolution image pyramid for the problem of visual receptive field learning. Receptive fields of both orientation and spatial frequency selectivity are observed in the authors' simulation result.
Persistent Identifierhttp://hdl.handle.net/10722/45564
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorDeng, Den_HK
dc.contributor.authorChan, KPen_HK
dc.contributor.authorYu, YLen_HK
dc.date.accessioned2007-10-30T06:29:16Z-
dc.date.available2007-10-30T06:29:16Z-
dc.date.issued1995en_HK
dc.identifier.citationThe 1995 IEEE International Conference on Neural Networks, Perth, WA., Australia, 27 November-1 December 1995. In IEEE International Conference on Neural Networks Proceedings, 1995, v. 5, p. 2831-2834en_HK
dc.identifier.issn1098-7576en_HK
dc.identifier.urihttp://hdl.handle.net/10722/45564-
dc.description.abstractThe statistical likelihood of Gabor filters and primary visual cortex has been of interest for years, yet learning mechanisms proposed did not generate satisfactory Gabor-like receptive fields. In this paper, a new computational model of self-organised Hebbian learning (SOHL) is proposed to work on a multi-resolution image pyramid for the problem of visual receptive field learning. Receptive fields of both orientation and spatial frequency selectivity are observed in the authors' simulation result.en_HK
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE International Conference on Neural Networks Proceedings-
dc.rights©1995 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectComputersen_HK
dc.subjectArtificial intelligenceen_HK
dc.titleSelf-organized learning of receptive fields in multi-resolutionen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1098-7576&volume=5&spage=2831&epage=2834&date=1995&atitle=Self-organized+learning+of+receptive+fields+in+multi-resolutionen_HK
dc.identifier.emailChan, KP:kpchan@cs.hku.hk-
dc.identifier.authorityChan, KP=rp00092-
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ICNN.1995.488182en_HK
dc.identifier.scopuseid_2-s2.0-0029463043-
dc.identifier.hkuros14158-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0029463043&selection=ref&src=s&origin=recordpage-
dc.identifier.volume5-
dc.identifier.spage2831-
dc.identifier.epage2834-
dc.identifier.scopusauthoridDeng, D=36903862600-
dc.identifier.scopusauthoridChan, KP=7406032820-
dc.identifier.scopusauthoridYu, YL=8609055300-
dc.customcontrol.immutablesml 151016 - merged-

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