Do arbitrary input–output mappings in parallel distributed processing networks require localist coding?
The Parallel Distributed Processing (PDP) approach to cognitive modelling assumes that knowledge is distributed across multiple processing units. This view is typically justified on the basis of the computational advantages and biological plausibility of distributed representations. However, both th...
| Publicado en: | Language, Cognition & Neuroscience Vol. 32; no. 3; pp. 392 - 400 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Taylor & Francis Ltd
Apr2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=120999866&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 120999866 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23273798 IQ6T jtl: Language, Cognition & Neuroscience issn: 23273798 maglogo: N pubinfo: dt: Apr2017 vid: 32 iid: 3 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 120999866 10.1080/23273798.2016.1256490 120999866 ppf: 392 ppct: 8 formats: tig: atl: Do arbitrary input–output mappings in parallel distributed processing networks require localist coding? aug: au: Vankov, Ivan I. Bowers, Jeffrey S. affil: Department of Cognitive Science and Psychology, New Bulgarian University, Sofia, Bulgaria sug: ab: The Parallel Distributed Processing (PDP) approach to cognitive modelling assumes that knowledge is distributed across multiple processing units. This view is typically justified on the basis of the computational advantages and biological plausibility of distributed representations. However, both these assumptions have been challenged. First, there is growing evidence that some neurons respond to information in a highly selective manner. Second, it has been demonstrated that localist representations are better suited for certain computational tasks. In this paper, we continue this line of research by investigating whether localist representations are learned in tasks involving arbitrary input–output mappings. The results imply that the pressure to learn local codes in such tasks is weak, but still there are conditions under which feed-forward PDP networks learn localist representation. Our findings further challenge the assumption that PDP modelling always goes hand in hand with distributed representations and provide directions for future research. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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