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Article: A neurocomputational model of early psychosis
Title | A neurocomputational model of early psychosis |
---|---|
Authors | |
Issue Date | 2003 |
Publisher | Springer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/ |
Citation | Lecture Notes In Artificial Intelligence (Subseries Of Lecture Notes In Computer Science), 2003, v. 2774 PART 2, p. 1149-1155 How to Cite? |
Abstract | A neurocomputation model for early psychosis is implemented with an associative network using the Hebbian learning algorithm. The model emphasizes on longitudinal evolution of the weight matrix from the pre-psychotic phase through the psychotic phase to the recovery phase. Enhanced activity-dependent synaptic plasticity is postulated to underlie formation of spurious associative connections during psychosis. This parsimonious longitudinal model is capable of addressing the presence of low- grade psychotic symptoms in the prepsychotic phase, the formation of spurious associations during psychosis, as well as the persistence of some of these associations into the recovery process with encapsulation of psychotic features. |
Persistent Identifier | http://hdl.handle.net/10722/120129 |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
References |
DC Field | Value | Language |
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dc.contributor.author | Chen, EYH | en_HK |
dc.date.accessioned | 2010-09-26T09:25:31Z | - |
dc.date.available | 2010-09-26T09:25:31Z | - |
dc.date.issued | 2003 | en_HK |
dc.identifier.citation | Lecture Notes In Artificial Intelligence (Subseries Of Lecture Notes In Computer Science), 2003, v. 2774 PART 2, p. 1149-1155 | en_HK |
dc.identifier.issn | 0302-9743 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/120129 | - |
dc.description.abstract | A neurocomputation model for early psychosis is implemented with an associative network using the Hebbian learning algorithm. The model emphasizes on longitudinal evolution of the weight matrix from the pre-psychotic phase through the psychotic phase to the recovery phase. Enhanced activity-dependent synaptic plasticity is postulated to underlie formation of spurious associative connections during psychosis. This parsimonious longitudinal model is capable of addressing the presence of low- grade psychotic symptoms in the prepsychotic phase, the formation of spurious associations during psychosis, as well as the persistence of some of these associations into the recovery process with encapsulation of psychotic features. | en_HK |
dc.language | eng | en_HK |
dc.publisher | Springer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/ | en_HK |
dc.relation.ispartof | Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) | en_HK |
dc.title | A neurocomputational model of early psychosis | en_HK |
dc.type | Article | en_HK |
dc.identifier.email | Chen, EYH: eyhchen@hku.hk | en_HK |
dc.identifier.authority | Chen, EYH=rp00392 | en_HK |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.scopus | eid_2-s2.0-8344225227 | en_HK |
dc.identifier.hkuros | 82816 | en_HK |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-8344225227&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 2774 PART 2 | en_HK |
dc.identifier.spage | 1149 | en_HK |
dc.identifier.epage | 1155 | en_HK |
dc.publisher.place | Germany | en_HK |
dc.identifier.scopusauthorid | Chen, EYH=7402315729 | en_HK |
dc.identifier.issnl | 0302-9743 | - |