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Updated: Jun 5, 2026

Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro
Published on: August 28, 2019
Spontaneous activity signatures of morphologically identified interneurons in the vestibulocerebellum
Tom J H Ruigrok1, Robert A Hensbroek, John I Simpson
1Department of Neuroscience, Erasmus Medical Center Rotterdam, 3000 CA Rotterdam, The Netherlands. t.ruigrok@erasmusmc.nl
Abstract:
Cerebellar cortical interneurons such as Golgi cells, basket cells, stellate cells, unipolar brush cells, and granule cells play an essential role in the operations of the cerebellum. However, detailed functional studies of the activity of these cells in both anesthetized and behaving animals have been hampered by problems in recognizing their physiological signatures. We have extracellularly recorded the spontaneous activity of vestibulocerebellar interneurons in ketamine/xylazine-anesthetized rats and subsequently labeled them with Neurobiotin using the juxtacellular technique. After recovery and morphological identification of these cells, they were related to statistical measures of their spontaneous activity. Golgi cells display a somewhat irregular firing pattern with relatively low average frequencies. Unipolar brush cells are characterized by more regular firing at higher rates. Basket and stellate cells are alike in their firing characteristics, which mainly stand out by their irregularity; some of them are set apart by their very slow average rate. The spontaneous activity of interneurons examined in the ketamine/xylazine rabbit fit within this general pattern. In the rabbit, granule cells were identified by the spontaneous occurrence of extremely high-frequency bursts of action potentials, which were also recognized in the rat. On the basis of these observations, we devised an algorithm that reliably determined the identity of 75% of the cells with only 2% incorrect classifications. The remaining cells were placed into border categories within which no classification was attempted. We propose that this algorithm can be used to help classify vestibulocerebellar interneurons recorded in awake, behaving animals.
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