Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/16500
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dc.contributor.authorMahmud, Mufti-
dc.contributor.authorTravalin, Davide-
dc.contributor.authorHussain, Amir-
dc.contributor.editorHuang, T-
dc.contributor.editorZeng, Z-
dc.contributor.editorLi, C-
dc.contributor.editorLeung, CS-
dc.date.accessioned2017-08-25T23:37:24Z-
dc.date.issued2012-
dc.identifier.urihttp://hdl.handle.net/1893/16500-
dc.description.abstractCognition is one of the main capabilities of mammal brain and understanding it thoroughly requires decoding brain's information processing pathways which are composed of networks formed by complex connectivity between neurons. Mostly, scientists rely on local field potentials (LFPs) averaged over a number of trials to study the effect of stimuli on brain regions under investigation. However, this may not be the right approach when trying to understand the exact neuronal network underlying the neuronal signals. As the LFPs are lumped activity of populations of neurons, their shapes provide fingerprints of the underlying networks. The method presented in this paper extracts shape information of the LFPs, calculate the corresponding current source density (CSD) from the LFPs and decode the underlying network activity. Through simulated LFPs it has been found that differences in LFP shapes lead to different network activity.en_UK
dc.language.isoen-
dc.publisherSpringer-
dc.relationMahmud M, Travalin D & Hussain A (2012) Decoding network activity from LFPS: A computational approach. In: Huang T, Zeng Z, Li C, Leung CS (ed.). Neural Information Processing: 19th International Conference, ICONIP 2012, Doha, Qatar, November 12-15, 2012, Proceedings, Part I. Lecture Notes in Computer Science, 7663, Berlin Heidelberg: Springer, pp. 584-591.-
dc.relation.ispartofseriesLecture Notes in Computer Science, 7663-
dc.rightsThe publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.-
dc.subjectLocal field potentialsen_UK
dc.subjectcurrent source densityen_UK
dc.subjectbrain activityen_UK
dc.subjectneuronal signalen_UK
dc.subjectneuronal signal analysisen_UK
dc.titleDecoding network activity from LFPS: A computational approachen_UK
dc.typePart of book or chapter of booken_UK
dc.rights.embargodate2999-12-31T00:00:00Z-
dc.rights.embargoreasonThe publisher does not allow this work to be made publicly available in this Repository therefore there is an embargo on the full text of the work.-
dc.identifier.doihttp://dx.doi.org/10.1007/978-3-642-34475-6_70-
dc.citation.issn0302-9743-
dc.citation.spage584-
dc.citation.epage591-
dc.citation.publicationstatusPublished-
dc.type.statusBook Chapter: publisher version-
dc.identifier.urlhttp://link.springer.com/chapter/10.1007/978-3-642-34475-6_70#-
dc.author.emailamir.hussain@stir.ac.uk-
dc.citation.btitleNeural Information Processing: 19th International Conference, ICONIP 2012, Doha, Qatar, November 12-15, 2012, Proceedings, Part I-
dc.citation.conferencedates2012-11-12T00:00:00Z-
dc.citation.conferencelocationDoha, Qatar-
dc.citation.conferencename19th International Conference on Neural Information Processing, ICONIP 2012-
dc.citation.date11/2012-
dc.citation.isbn978-3-642-34474-9-
dc.publisher.addressBerlin Heidelberg-
dc.contributor.affiliationUniversity of Padua-
dc.contributor.affiliationSt Jude Medical-
dc.contributor.affiliationComputing Science - CSM Dept-
dc.rights.embargoterms2999-12-31-
dc.rights.embargoliftdate2999-12-31-
Appears in Collections:Computing Science and Mathematics Book Chapters and Sections

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