Whole-Cell Properties of Cerebellar Nuclei Neurons In Vivo

Abstract

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<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<0.001), while capacitance increased with firing frequency (R2 = 0.29, P<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 μm soma diameter) and smaller (< 21.2 μm) 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 m...

In vivo analysis of synaptic activity in cerebellar nuclei neurons unravels the efficacy of excitatory inputs

“…The four groups of neurons were not significantly different from one another in mean frequency, CV, CV 2 and threshold, in line with a previous in vivo study (Canto et al . ).…”

“…To compare AP activity, the following parameters were calculated: mean frequency, CV, CV 2 , mean threshold and amplitude of AP and mean and SD of AP half width (see Methods). The four groups of neurons were not significantly different from one another in mean frequency, CV, CV 2 and threshold, in line with a previous in vivo study (Canto et al 2016).…”

“…We confirmed this classification by analysing an additional 56 neurons presented by Canto et al . () that showed an identical distribution. While neurons from groups I and II displayed an AHP composed of a fast and a slow component, the AHPs of groups III and IV had only one component.…”

“…The timing of the second minimum was significantly different between all groups (P < 0.01). This classification was further supported by analysing the data presented in Canto et al (2016). We downloaded the available data from the online database (https://doi.org/10.5061/dryad.31dj3) and extracted the AHP waveform parameters from 56 neurons that passed our criteria for neurons used for characterizing groups of neurons (see Inclusion and exclusion criteria for neurons).…”

“…C, AHP parameters plotted in the space of the first two eigenvectors. D, AHP parameters plotted in the space of the first two eigenvectors for neurons presented inCanto et al (2016). E, four examples of the mean AP shape taken from one neuron from each of the four group.…”