Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Feedback in multimodal self-organizing networks enhances perception of corrupted stimuli
LuleƄ University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
2006 (English)In: AI 2006: Advances in Artificial Intelligence. 19th Australian Joint Conference on Artificial Intelligence, Hobart, Australia, December 4-8, 2006. Proceedings / [ed] Abdul Sattar; Byeong-Ho Kang, Encyclopedia of Global Archaeology/Springer Verlag, 2006, 19-28 p.Conference paper, Published paper (Refereed)
Abstract [en]

It is known from psychology and neuroscience that multimodal integration of sensory information enhances the perception of stimuli that are corrupted in one or more modalities. A prominent example of this is that auditory perception of speech is enhanced when speech is bimodal, i.e. when it also has a visual modality. The function of the cortical network processing speech in auditory and visual cortices and in multimodal association areas, is modeled with a Multimodal Self-Organizing Network (MuSON), consisting of several Kohonen Self-Organizing Maps (SOM) with both feedforward and feedback connections. Simulations with heavily corrupted phonemes and uncorrupted letters as inputs to the MuSON demonstrate a strongly enhanced auditory perception. This is explained by feedback from the bimodal area into the auditory stream, as in cortical processing.

Place, publisher, year, edition, pages
Encyclopedia of Global Archaeology/Springer Verlag, 2006. 19-28 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 4304
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Industrial Electronics
Identifiers
URN: urn:nbn:se:ltu:diva-31745DOI: 10.1007/11941439_6Local ID: 601f6b00-960b-11db-8975-000ea68e967bISBN: 3-540-49787-0 (print)OAI: oai:DiVA.org:ltu-31745DiVA: diva2:1004979
Conference
Australian Joint Conference on Artificial Intelligence : 04/12/2006 - 08/12/2006
Note
Validerad; 2006; Bibliografisk uppgift: Lecture Notes in Artificial Intelligence; 20061228 (ysko)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2017-11-25Bibliographically approved

Open Access in DiVA

fulltext(177 kB)9 downloads
File information
File name FULLTEXT01.pdfFile size 177 kBChecksum SHA-512
650758120061ef0673af88ec8f3eb512d1cfdeabe9e749a9d9a8fe65eb02f4047bfb3c1b63741971326279d2af2b00f154b6a72f8a30b958a3a48dee05498324
Type fulltextMimetype application/pdf

Other links

Publisher's full texthttp://www.comp.utas.edu.au/ai06/

Search in DiVA

By author/editor
Gustafsson, LennartPaplinski, Andrew
By organisation
Embedded Internet Systems Lab
Other Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 9 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 22 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf