Scaling the iGrid Visualisation

Addressing the pure computational challenges to cross-correlate spike train data using a cross platform program that scales to use all available resources is challenging. These issues are however primarily technical in nature and can be overcome with time and effort. The final goal however is to make the data available to the user to explore using visual analytics where the majority of the “heavy lifting” is performed by the users’ visual systems with only limited cognitive effort. In iGrid’s case the primary barrier to this is one of information overload. This is easily demonstrated by taking the 2000 spike train recording used in testing the cross-correlation algorithm and considering the iGrid display that would result from its visualisation.

Representing the 2,000 spike train data set as a cross-correlation grid would require the display of 4,000,000 individual grid cells. Typically a modern high resolution monitor operates at resolutions of 1920 x 1080 pixels. This provides a display of 2,073,600 pixels or 51.84% of a 4,000,000 cell grid. While this comparison clearly shows that such a grid cannot be displayed the selection of a single pixel to represent a data point is inappropriate because:

1. The modern high resolution monitor deliberately employs a resolution were the individual pixels cannot be appreciated as separate entities in the human visual system.

2. It would be impractical to dedicate the entire screen to the grid display as this would leave no room for the provision of interactive controls that would facilitate user control to overview, filter and extract selected detail from the visualisation.

To successfully allow the visual exploration of a spike train cross correlation dataset these issues must be addressed. The approach adopted in the implemented solution is to demote the grid representation from its position of providing the overview, filtering and detailed data delivery. Instead the grid will focus on the delivery of detailed data a role in which it excels. Responsibility for the provision of the overview and filtering of data will however be removed from the grid and re-allocated to a series of dendrogram visualisations. Each cluster generated in the data analysis phase will form a dendrogram providing the user with an overview of the identified clusters within the data set. Spike trains that exhibited no significant cross-correlation (and which are therefore not clustered) will be reported separately. The individual nodes of the dendrogram will be interactive allowing the user to expand or collapse individual dendrograms or sub-sections / sub-trees of a dendrogram. By this means filtering of the spike trains included in the iGrid cross-correlation plot will be achieved.

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Despite the re-allocation of the overview and filtering roles to the dendrogram visualisation it is anticipated that the number of spike trains displayed will remain quite large in any significant sized data set. Instead of limiting the iGrid display to the available screen space a “viewport” approach was adopted where the grid visualisation is generated on a virtual screen space of potentially infinite extent (within the constraints of available system memory). This allows the visualisation to ensure that each cell of the cross correlation grid is rendered at a size that allows the human visual system to effectively process its relationship to other nearby spike trains. On screen presentation is via a viewport that shows a conveniently sized section of the rendered grid. Scroll bars and in viewport labelling is used to maintain the users overall awareness of position within the data set.

While iGrid itself provides a detailed visual representation of the relationships between the spike trains and spike train clusters it does not represent the “most detailed view available”. That of course is the cross correlation data itself that was used to generate the grid. This data is traditionally represented as a histogram plot of the cross-correlation bins generated from the raw spike train data. Previous implementations have provided these as pop-up graphs or via replacement of the iGrid cell with a glyph representing the major peaks of the histogram (those surpassing the Brillinger threshold). In each case the resulting histogram is visually small and difficult for the user to process without significant cognitive effort. The new implementation extends the viewport concept to the cross-correlation histogram. The iGrid viewport is dynamically resizable by the user allowing them to control the screen space dedicated to the iGrid representation. The remaining screen space is dedicated to the presentation of the cross-correlation data in its most basic form – the cross correlation histogram. This allows the user to directly control the display space for iGrid and the cross-correlation histogram. As the user explores the data set they will at different times assign different levels of importance to the iGrid vs histogram representations. The ability to visually resize the two viewports allows the user to visually place greater importance on one or both of the visualisations. Taken together these features implement the Visual Information-Seeking Mantra of overview first, zoom and filter with details on demand. Figure 10-6 and Figure 10-7 show the visual effect of this approach. In Figure 10-6 a data set of 20 spike trains has been rendered as a cross correlation grid with a supporting histogram. Greater visual emphasis has been placed on the entire data set as represented by the grid but the histogram shows an interesting feature – two significant cross correlation peaks. Figure 10-7 presents the same data but this time greater emphasis has been placed on the cross correlation histogram by enlarging the viewport to half the screen size. Despite this the correlation grid is still clearly visible, a vertical scroll bar has been added avoiding compressing the grid display to an unusable degree but still showing the interesting cross- correlation and the cluster of spike trains that it is part of.

Chapter 10: The i-Grid Visualisation

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Figure 10-6: iGrid visualisation with cross correlation histogram for neuron spike trains 1 and 7. The red bar denotes the peak cross-correlation value used for the iGrid representation.

Figure 10-7: Resized iGrid visualisation & cross correlation histogram placing greater emphasis on the detailed histogram while retaining awareness of its place in the correlation grid. The red bar denotes the

peak cross-correlation value used for the iGrid representation.