DOI: 10.5061/dryad.31dj3
title: Data from: Whole-cell properties of cerebellar nuclei neurons in vivo

description: Cerebellar nuclei neurons integrate sensorimotor information and form the
 final output of the cerebellum, projecting to premotor brainstem targets.
 This implies that, in contrast to specialized neurons and interneurons in
 cortical regions, neurons within the nuclei encode and integrate complex
 information that is most likely reflected in a large variation of
 intrinsic membrane properties and integrative capacities of individual
 neurons. Yet, whether this large variation in properties is reflected in a
 heterogeneous physiological cell population of cerebellar nuclei neurons
 with well or poorly defined cell types remains to be determined. Indeed,
 the cell electrophysiological properties of cerebellar nuclei neurons have
 been identified in vitro in young rodents, but whether these properties
 are similar to the in vivo adult situation has not been shown. In this
 comprehensive study we present and compare the in vivo properties of 144
 cerebellar nuclei neurons in adult ketamine-xylazine anesthetized mice. We
 found regularly firing (N=88) and spontaneously bursting (N=56) neurons.
 Membrane-resistance, capacitance, spike half-width and firing frequency
 all widely varied as a continuum, ranging from 9.63 to 3352.1 MΩ, from 6.7
 to 772.57 pF, from 0.178 to 1.98 ms, and from 0 to 176.6 Hz, respectively.
 At the same time, several of these parameters were correlated with each
 other. Capacitance decreased with membrane resistance (R2=0.12,
 P&lt;0.001), intensity of rebound spiking increased with membrane
 resistance (for 100 ms duration R2=0.1503, P=0.0011), membrane resistance
 decreased with membrane time constant (R2=0.045, P=0.031) and increased
 with spike half-width (R2=0.023, P&lt;0.001), while capacitance
 increased with firing frequency (R2=0.29, P&lt;0.001). However,
 classes of neuron subtypes could not be identified using merely
 k-clustering of their intrinsic firing properties and/or integrative
 properties following activation of their Purkinje cell input. Instead,
 using whole-cell parameters in combination with morphological criteria
 revealed by intracellular labelling with Neurobiotin (N=18) allowed for
 electrophysiological identification of larger (29.3–50 m soma diameter)
 and smaller (&lt; 21.2 m) cerebellar nuclei neurons with significant
 differences in membrane properties. Larger cells had a lower membrane
 resistance and a shorter spike, with a tendency for higher capacitance.
 Thus, in general cerebellar nuclei neurons appear to offer a rich and wide
 continuum of physiological properties that stand in contrast to neurons in
 most cortical regions such as those of the cerebral and cerebellar cortex,
 in which different classes of neurons operate in a narrower territory of
 electrophysiological parameter space. The current dataset will help
 computational modelers of the cerebellar nuclei to update and improve
 their cerebellar motor learning and performance models by incorporating
 the large variation of the in vivo properties of cerebellar nuclei
 neurons. The cellular complexity of cerebellar nuclei neurons may endow
 the nuclei to perform the intricate computations required for sensorimotor
 coordination.
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