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dc.contributor.authorMahmud, Muftien_UK
dc.contributor.authorTravalin, Davideen_UK
dc.contributor.authorHussain, Amiren_UK
dc.contributor.editorHuang, Ten_UK
dc.contributor.editorZeng, Zen_UK
dc.contributor.editorLi, Cen_UK
dc.contributor.editorLeung, CSen_UK
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.relationMahmud M, Travalin D & Hussain A (2012) Decoding network activity from LFPS: A computational approach. In: Huang T, Zeng Z, Li C & Leung C (eds.) 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, 7663en_UK
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.en_UK
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.embargoreason[Decoding network activity from LFPS.pdf] The 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.en_UK
dc.type.statusVoR - Version of Recorden_UK
dc.citation.btitleNeural Information Processing: 19th International Conference, ICONIP 2012, Doha, Qatar, November 12-15, 2012, Proceedings, Part Ien_UK
dc.publisher.addressBerlin Heidelbergen_UK
dc.contributor.affiliationUniversity of Paduaen_UK
dc.contributor.affiliationSt Jude Medicalen_UK
dc.contributor.affiliationComputing Scienceen_UK
rioxxterms.typeBook chapteren_UK
local.rioxx.authorMahmud, Mufti|en_UK
local.rioxx.authorTravalin, Davide|en_UK
local.rioxx.authorHussain, Amir|0000-0002-8080-082Xen_UK
local.rioxx.projectInternal Project|University of Stirling|
local.rioxx.contributorHuang, T|en_UK
local.rioxx.contributorZeng, Z|en_UK
local.rioxx.contributorLi, C|en_UK
local.rioxx.contributorLeung, CS|en_UK
local.rioxx.filenameDecoding network activity from LFPS.pdfen_UK
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