Visualization techniques for the analysis of neurophysiological data
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.