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dc.contributor.authorStuart, EJ
dc.date.accessioned2017-06-21T07:40:00Z
dc.date.accessioned2017-06-21T10:30:21Z
dc.date.available2017-06-21T07:40:00Z
dc.date.available2017-06-21T10:30:21Z
dc.date.issued2004-06-10
dc.identifier.issn1473-8724
dc.identifier.issn1473-8724
dc.identifier.urihttp://hdl.handle.net/10026.1/9509
dc.description.abstract

<jats:p>Currently, the focus of research within Information Visualization is steering towards genomic data visualization due to the level of activity that the Human Genome Project has generated. However, the Human Brain project, renowned within Neuroinformatics, is equally challenging and exciting. Its main aim is to increase current understanding of brain function such as memory, learning, attention, emotions and consciousness. It is understood that this task will require the ‘integration of information from the level of the gene to the level of behaviour'. The work presented in this paper focuses on the visualization of neural data. More specifically, the data being analysed is multi-dimensional spike train data. Traditional methods, such as the ‘raster plot’ and the ‘cross-correlogram', are still useful but they do not scale up for larger assemblies of neurons. In this paper, a new innovative method called the Tunnel is defined. Its design is based on the principles of Information Visualization; overview the data, zoom and filter data, data details on demand. The features of this visualization environment are described. This includes data filtering, navigation and a ‘flat map’ overview facility. Additionally, a ‘coincidence overlay map’ is presented. This map washes the Tunnel with colour, which encodes the coincidence of spikes.</jats:p>

dc.format.extent245-256
dc.languageen
dc.language.isoen
dc.publisherSAGE Publications
dc.relation.replaceshttp://hdl.handle.net/10026.1/9505
dc.relation.replaces10026.1/9505
dc.relation.replaces10026.1/8546
dc.relation.replaceshttp://hdl.handle.net/10026.1/8546
dc.subject46 Information and Computing Sciences
dc.subject4607 Graphics, Augmented Reality and Games
dc.subject4611 Machine Learning
dc.subjectNeurosciences
dc.subject1.2 Psychological and socioeconomic processes
dc.subject1 Underpinning research
dc.subjectNeurological
dc.titleThe Representation of Neural Data using Visualization
dc.typejournal-article
dc.typeArticle
plymouth.edition2004
plymouth.issue4
plymouth.volume3 (4)
plymouth.publisher-urlhttp://dx.doi.org/10.1057/palgrave.ivs.9500071
plymouth.publication-statusPublished
plymouth.journalInformation Visualization
dc.identifier.doi10.1057/palgrave.ivs.9500071
pubs.merge-from10026.1/8546
pubs.merge-fromhttp://hdl.handle.net/10026.1/8546
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
plymouth.organisational-group/Plymouth/Users by role/Researchers in ResearchFish submission
dc.rights.embargodate2005-06-10
dc.identifier.eissn1473-8724
dc.rights.embargoperiod12 months
rioxxterms.versionofrecord10.1057/palgrave.ivs.9500071
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/under-embargo-all-rights-reserved
rioxxterms.typeJournal Article/Review


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