Skip to Main Content
The current ability to record neural activity within the brains of mammals has led to the production of a large body of experimental data. The analysis and comprehension of this data is key to the understanding of many basic brains functions, for example learning and memory. The main constituent of this data is multidimensional spike train recordings. As the analysis of these datasets by traditional means becomes more complex and time consuming, the need for better methods of data analysis increases. We present an innovative method for analysis of the relationships within large multidimensional spike train datasets. This method, called the 'correlation grid', is based on the information visualisation principles; overview the data, filter and zoom the data and obtain details-on-demand (B. Shneiderman, (1996). The features of the correlation grid are described, including filtering and statistical sorting methods.