In order to understand the diverse and complex functions of the Human brain, the temporal relationships
of vast quantities of multi-dimensional spike train data must be analysed. A number of statistical
methods already exist to analyse these relationships. However, as a result of expansions in recording
capability hundreds of spike trains must now be analysed simultaneously.
In addition to the requirements for new statistical analysis methods, the need for more efficient data
representation is paramount. The computer science field of Information Visualization is specifically
aimed at producing effective representations of large and complex datasets. This thesis is based on
the assumption that data analysis can be significantly improved by the application of Information
Visualization principles and techniques.
This thesis discusses the discipline of Information Visualization, within the wider context of visualization.
It also presents some introductory neurophysiology focusing on the analysis of multidimensional
spike train data and software currently available to support this problem. Following this,
the Toolbox developed to support the analysis of these datasets is presented. Subsequently, three case
studies using the Toolbox are described. The first case study was conducted on a known dataset in
order to gain experience of using these methods. The second and third case studies were conducted
on blind datasets and both of these yielded compelling results
