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iRaster: A novel information visualization tool to explore spatiotemporal patterns in multiple spike trains

Lookup NU author(s): Professor Evelyne SernagorORCiD


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Over the last few years, simultaneous recordings of multiple spike trains have become widely used by neuroscientists. Therefore, it is important to develop new tools for analysing multiple spike trains in order to gain new insight into the function of neural systems. This paper describes how techniques from the field of visual analytics can be used to reveal specific patterns of neural activity. An interactive raster plot called iRaster has been developed. This software incorporates a selection of statistical procedures for visualization and flexible manipulations with multiple spike trains. For example, there are several procedures for the re-ordering of spike trains which can be used to unmask activity propagation, spiking synchronization, and many other important features of multiple spike train activity. Additionally, iRaster includes a rate representation of neural activity, a combined representation of rate and spikes, spike train removal and time interval removal. Furthermore, it provides multiple coordinated views, time and spike train zooming windows, a fisheye lens distortion, and dissemination facilities. iRaster is a user friendly, interactive, flexible tool which supports a broad range of visual representations. This tool has been successfully used to analyse both synthetic and experimentally recorded datasets. In this paper, the main features of iRaster are described and its performance and effectiveness are demonstrated using various types of data including experimental multi-electrode array recordings from the ganglion cell layer in mouse retina. iRaster is part of an ongoing research project called VISA (Visualization of Inter-Spike Associations) at the Visualization Lab in the University of Plymouth. The overall aim of the VISA project is to provide neuroscientists with the ability to freely explore and analyse their data. The software is freely available from the Visualization Lab website (see © 2010 Elsevier B.V.

Publication metadata

Author(s): Somerville J, Stuart L, Sernagor E, Borisyuk R

Publication type: Article

Publication status: Published

Journal: Journal of Neuroscience Methods

Year: 2010

Volume: 194

Issue: 1

Pages: 158-171

Print publication date: 25/09/2010

ISSN (print): 0165-0270

ISSN (electronic): 1872-678X

Publisher: Elsevier BV


DOI: 10.1016/j.jneumeth.2010.09.009


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