| Summary: | Neuroscientists are increasingly overwhelmed by new recordings of the nervous system. These recordings are significantly increasing in size due to new electrophysiological techniques, such as multi-electrode arrays. These techniques can simultaneously record the electrical activity (or spike trains) from thousands of neurons. These new datasets are larger than the traditional datasets recorded from single electrodes where fewer than ten spike trains are usually recorded. Consequently, new tools are now required to effectively analyse these new datasets. This thesis describes how techniques from the field of Visual Analytics can be applied to detect specific patterns in spike train data. These techniques are realised in a software tool called Neurigma. Neurigma is a collection of visual representations of spike train data that are unified to provide a coordinated representation of the data. The visual representations within Neurigma include: an interactive raster plot, an improved correlation grid, a novel representation called the correlation plot (which includes a novel coupling estimation algorithm), and a novel network diagram. These views provide insight into spike train data, and particularly, they identify correlated patterns, called functional connectivity. Within this thesis Neurigma is used to analyse synthetically generated datasets and experimental recordings. Three main findings are presented. First, propagating spiral patterns are identified within recordings from the neonatal mouse retina. Second, functional connectivity is identified within the cat visual cortex. Finally, the functional connectivity of a large synthetic dataset, of 1000 spike trains, is accurately classified into direct, indirect and common input coupling.
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