Neural Networks Learn Highly Selective Representations in Order to Overcome the Superposition Catastrophe.
A key insight from 50 years of neurophysiology is that some neurons in cortex respond to information in a highly selective manner. Why is this? We argue that selective representations support the coactivation of multiple "things" (e.g., words, objects, faces) in short-term memory, whereas nonselecti...
| Publicado en: | Psychological Review Vol. 121; no. 2; pp. 248 - 262 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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American Psychological Association
Apr2014
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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=ssf&AN=95587734&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 95587734 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0033295X PYV jtl: Psychological Review issn: 0033295X maglogo: N pubinfo: dt: Apr2014 vid: 121 iid: 2 pid: 34 pub: American Psychological Association artinfo: ui: 95587734 10.1037/a0035943 ppf: 248 ppct: 14 formats: tig: atl: Neural Networks Learn Highly Selective Representations in Order to Overcome the Superposition Catastrophe. aug: au: Bowers, Jeffrey S. Vankov, Ivan I. Damian, Markus F. Davis, Colin J. affil: University of Bristol su: Neurophysiology Neurons Short-term memory Cerebral cortex Biological neural networks sug: subj: Neurophysiology Neurons Short-term memory Cerebral cortex Biological neural networks keyword: distributed representations grandmother cells localist representations short-term memory superposition catastrophe distributed representations grandmother cells localist representations short-term memory superposition catastrophe ab: A key insight from 50 years of neurophysiology is that some neurons in cortex respond to information in a highly selective manner. Why is this? We argue that selective representations support the coactivation of multiple "things" (e.g., words, objects, faces) in short-term memory, whereas nonselective codes are often unsuitable for this purpose. That is, the coactivation of nonselective codes often results in a blend pattern that is ambiguous; the so-called superposition catastrophe. We show that a recurrent parallel distributed processing network trained to code for multiple words at the same time over the same set of units learns localist letter and word codes, and the number of localist codes scales with the level of the superposition. Given that many cortical systems are required to coactivate multiple things in short-term memory, we suggest that the superposition constraint plays a role in explaining the existence of selective codes in cortex. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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