Determinants of synaptic integration and heterogeneity in rebound firing explored with data-driven models of deep cerebellar nucleus cells

Abstract

Significant inroads have been made to understand cerebellar cortical processing but neural coding at the output stage of the cerebellum in the deep cerebellar nuclei (DCN) remains poorly understood. The DCN are unlikely to just present a relay nucleus because Purkinje cell inhibition has to be turned into an excitatory output signal, and DCN neurons exhibit complex intrinsic properties. In particular, DCN neurons exhibit a range of rebound spiking properties following hyperpolarizing current injection, raising the question how this could contribute to signal processing in behaving animals. Computer modeling presents an ideal tool to investigate how intrinsic voltage-gated conductances in DCN neurons could generate the heterogeneous firing behavior observed, and what input conditions could result in rebound responses. To enable such an investigation we built a compartmental DCN neuron model with a full dendritic morphology and appropriate active conductances. We generated a good match of our simulations with DCN current clamp data we recorded in acute slices, including the heterogeneity in the rebound responses. We then examined how inhibitory and excitatory synaptic input interacted with these intrinsic conductances to control DCN firing. We found that the output spiking of the model reflected the ongoing balance of excitatory and inhibitory input rates and that changing the level of inhibition performed an additive operation. Rebound firing following strong Purkinje cell input bursts was also possible, but only if the chloride reversal potential was more negative than −70 mV to allow de-inactivation of rebound currents. Fast rebound bursts due to T-type calcium current and slow rebounds due to persistent sodium current could be differentially regulated by synaptic input, and the pattern of these rebounds was further influenced by HCN current. Our findings suggest that active properties of DCN neurons could play a crucial role for signal processing in the cerebellum.Electronic supplementary materialThe online version of this article (doi:10.1007/s10827-010-0282-z) contains supplementary material, which is available to authorized users.

Whole-Cell Properties of Cerebellar Nuclei Neurons In Vivo

“…We calculated the increase in firing frequency 50, 100 and 250 ms after current offset compared to 100 ms before current onset (Fig 1G), because previous work revealed differences in short and long bursts of rebound firing in CNNs [58–61]. When analyzing the firing rate increase over 50 ms versus 100 and 250 ms, many neurons showed short or long rebounds [58,61–63]. Neurons with a higher resistance showed a significant trend for shorter and more intense rebounds compared to neurons with a lower resistance (50 ms rebound R 2 = 0.103, P = 0.011; 100 ms rebound R 2 = 0.1503, P = 0.0011; and 250 ms rebound R 2 = 0.048, P = 0.068).…”

Rebound excitation triggered by synaptic inhibition in cerebellar nuclear neurons is suppressed by selective T-type calcium channel block

“…Several mechanisms have been proposed to explain the differences in RE, which could also explain the differential ability to fire LTSs. These explanations include the differential expression of Cav3 channel isoforms with different voltage dependency or kinetics (Molineux et al 2006), the different amplitudes of the total T-type calcium current (Molineux et al 2006;Steuber et al 2011), and the modulation exerted by other inward or outward coactivated currents (Molineux et al 2008;Steuber et al 2011). Clarification of whether the latter mechanisms alone or cooperatively explain the differences in rebounds and ability to fire LTSs requires further specific studies.…”

The Neuronal Codes of the Cerebellum

“…This suggests that joint rate change functions across populations of Purkinje cells could be a significant driver of rate changes in the deep nuclei. A biophysically detailed model of a cerebellar nucleus neuron (Steuber et al, 2011) was used to determine how CN neuron spiking would be affected by population Purkinje cell input with different degrees of rate correlation. Indeed, rate correlations between Purkinje cells turned out to be a strong determinant of CN spike modulation, and the level of rate comodulation seen between Purkinje cells could account for the depth of rate modulation observed in CN recordings (D.J.…”