Decision-tree algorithm performance with identified neurones across mice, rats and cats.

<p>Following the format used by Ruigrok <i>et al.</i> (2011), <b>A</b> compares the classifications our of GPC model with those of the decision-tree algorithm. The <i>left</i> and <i>middle</i> columns show the outcomes of our GPC classification taking all cells without probability-thresholding (<i>left</i>) or with an arbitrarily chosen probability threshold of (p>0.7; <i>middle</i> column) thus some cells were classified as 'unknown' cells (c.f. the Ruigrok decision-tree outputs). The <i>right</i> column shows the results of our datasets when classified using the Ruigrok decision-tree. The numbers within the pies indicate the number of cells that were classified correctly, incorrectly and as 'unknown' cells (i.e. cells for which no decision is taken). In general, the decision-tree algorithm was less accurate than our GPC model, although note that neither mossy fibres nor Purkinje cells were built in to the decision-tree algorithm. <b>B</b> shows data from a small set of identified granule cells recorded in the anaesthetised mouse, leading to 80% classification accuracy. <b>C</b> shows similar data for identified granule cells (inset shows confocal reconstructions of juxtacellularly labelled granule cells) and Golgi cells recorded in the decerebrate cat, leading to 100% classification accuracy. Following the same format as in <b>A</b>, we compare mouse and cat data using our GPC model and the decision-tree algorithm. Generally lower levels of accuracy were achieved by the decision-tree algorithm.