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

Journal of Computational Neuroscience, Jun 2011

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.

A PDF file should load here. If you do not see its contents the file may be temporarily unavailable at the journal website or you do not have a PDF plug-in installed and enabled in your browser.

Alternatively, you can download the file locally and open with any standalone PDF reader:

http://link.springer.com/content/pdf/10.1007%2Fs10827-010-0282-z.pdf

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

Volker Steuber 0 2 3 Nathan W. Schultheiss 0 2 3 R. Angus Silver 0 2 3 Erik De Schutter 0 2 3 Dieter Jaeger 0 2 3 Action Editor: John Huguenard 0 V. Steuber Science and Technology Research Institute, University of Hertfordshire , Hatfield Herts AL10 9AB, UK 1 ) Department of Biology, Emory University , 1510 Clifton Rd., Atlanta, GA 30322, USA 2 E. De Schutter Computational Neuroscience Unit, Okinawa Institute of Science and Technology , Okinawa 904-0411, Japan 3 R. A. Silver Department of Neuroscience, Physiology and Pharmacology , UCL, London WC1E 6BT, UK 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 voltagegated 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. 1 Introduction The deep cerebellar nuclei (DCN) perform an important gateway function in the cerebellum, as they provide the sole source of cerebellar output to red nucleus, thalamus, and inferior olive after integrating inhibitory inputs from cerebellar cortical Purkinje cells with excitatory input from brain stem and cortical sources. DCN neurons projecting to the inferior olive are GABAergic (Fredette and Mugnaini 1991) while those projecting to red nucleus and thalamus are glutamatergic (Chan-Palay 1977; Daniel et al. 1987) and show a distinguishable morphology with a slightly larger size (Sultan et al. 2003). Nevertheless, both types of neurons have remarkably similar physiological properties characterized by spontaneous spiking in vitro and a robust postinhibitory rebound spike burst, but show a minor difference in the depth of fast spike afterhyperpolarization (Uusisaari et al. 2007). The hallmark behavior of strong post-hyperpolarization rebound spiking was identified already in early intracellular investigations of DCN intrinsic properties (Gardette et al. 1985a, b; Jahnsen 1986a, b). A T-type calcium current and a sodium plateau current were identified as important contributors to fast and slow components of depolarization underlying rebound spiking, respectively (Llinas and Muhlethaler 1988). The T-type current dependent fast rebound burst is particularly strong in DCN neurons with a high CaV3.1 expression, while it is much weaker in DCN neurons with a predominant expression of CaV3.3 channels (Molineux et al. 2006). Rebounds can also be elicited by strong inhibitory synaptic inputs in vitro (Aizenman and Linden 1999), suggesting that they may be triggered in vivo as well. A requirement of hyperpolarization and rebound spiking to elicit potentiation of mossy fiber synapses onto DCN neurons (Pugh and Raman 2006, 2008) suggests that rebounds might be directly linked to mechanisms of learning. Nevertheless, due to the limited level of hyperpolarization that is reached during GABAA mediated IPSPs and due to shunting of rebound currents in the presence of ongoing synaptic inputs, it is possible that in vivo conditions may not favor rebound behavior (Alvina et al. 2008) unless the inhibitory inputs are very intense (Tadayonnejad et al. 2009). Rebound behavior is certainly not the only possibility of transmitting information through the cerebellar output stage, as dynamic clamp studies (...truncated)


This is a preview of a remote PDF: http://link.springer.com/content/pdf/10.1007%2Fs10827-010-0282-z.pdf

Volker Steuber, Nathan W. Schultheiss, R. Angus Silver, Erik De Schutter, Dieter Jaeger. Determinants of synaptic integration and heterogeneity in rebound firing explored with data-driven models of deep cerebellar nucleus cells, Journal of Computational Neuroscience, 2011, pp. 633-658, Volume 30, Issue 3, DOI: 10.1007/s10827-010-0282-z