Identifying Purkinje cells using only their spontaneous simple spike activity
Journal of Neuroscience Methods , Volume 232 p. 173- 180
Background: We have extended our cerebellar cortical interneuron classification algorithm that uses statistics of spontaneous activity (Ruigrok et al., 2011) to include Purkinje cells. Purkinje cells were added because they do not always show a detectable complex spike, which is the accepted identification. The statistical measures used in the present study were obtained from morphologically identified interneurons and complex spike identified Purkinje cells, recorded from ketamine-xylazine anesthetized rats and rabbits, and from awake rabbits. New method: The new algorithm has an added decision step that classifies Purkinje cells using a combination of the median absolute difference from the median interspike interval (MAD) and the mean of the relative differences of successive interspike intervals (CV2). These measures reflect the high firing rate and intermediate regularity of Purkinje cell simple spike activity. Results: Of 86 juxtacellularly labeled interneurons and 110 complex spike-identified Purkinje cells, 61 interneurons and 95 Purkinje cells were correctly classified, 22 interneurons and 13 Purkinje cells were deemed unclassifiable, and 3 interneurons and 2 Purkinje cells were incorrectly classified. Comparison with existing methods: The new algorithm improves on our previous algorithm because it includes Purkinje cells. This algorithm is the only one for the cerebellum that does not presume anatomical knowledge of whether the cells are in the molecular layer or the granular layer. Conclusions: These results strengthen the view that the new decision algorithm is useful for identifying neurons recorded at all cerebellar depths, particularly those neurons recorded in the rabbit vestibulocerebellum.
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Hensbroek, R.A, Belton, T, van Beugen, B.J, Maruta, J, Ruigrok, T.J.H, & Simpson, J.I. (2014). Identifying Purkinje cells using only their spontaneous simple spike activity. Journal of Neuroscience Methods, 232, 173–180. doi:10.1016/j.jneumeth.2014.04.031