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Winter 2004, Volume 3, Number 4, Pages 245-256
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| Original Article |
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| The representation of neural data using visualization |
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| Martin Walter1, Liz Stuart1 and Roman Borisyuk2,3 |
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1School of Computing, Communications and Electronics, University of Plymouth, Plymouth, Devon, U.K.
2Plymouth Institute of Neuroscience, University of Plymouth, Plymouth, Devon, U.K.
3Institute of Mathematical Problems in Biology, Russian Academy of Sciences, Pushchino, Moscow Region 142 290, Russia
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Correspondence to: Martin Walter, Room B310, Portland Square, School of Computing, Communications and Electronics, University of Plymouth, Plymouth, Devon PL4 8AA, U.K. Tel: +44 1752 23 2620; Fax: +44 1752 23 2540; E-mail: mwalter@plymouth.ac.uk |
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| Abstract |
| 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.
Information Visualization (2004) 3, 245-256. doi:10.1057/palgrave.ivs.9500071
Published online 10 June 2004 |
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| Keywords |
| Neural data; spikes; spike trains; analysis; visualization environment |
| Received 5 September 2003; revised 15 February 2004; accepted 17 March 2004; published online 10 June 2004 |
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