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

Steuber V, Schultheiss NW, Silver RA, De Schutter E, Jaeger D - J Comput Neurosci (2010)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Science and Technology Research Institute, University of Hertfordshire, Hatfield Herts, AL10 9AB, UK.

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.

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Currents underlying rebound patterns in the model of Neuron 1 and Neuron 3. The T-type calcium current (ICaT, green) underlies the fast burst after the offset of hyperpolarization, while the persistent sodium current (INaP, red) underlies the subsequent prolonged rebound period of spiking. Depending on the relative density of GCaT and GNaP present, each component of the rebound can be more or less pronounced. Note that IHCN (blue) only makes a minor contribution to the rebound depolarization. This is due to the small driving force of IHCN (reversal potential of −45 mV) during the rebound depolarization. (b) A pronounced fast spike burst is present with high GCaT density. The peak of ICaT reaches −1.85 nA, and is shown truncated to allow visualization of smaller currents
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Fig4: Currents underlying rebound patterns in the model of Neuron 1 and Neuron 3. The T-type calcium current (ICaT, green) underlies the fast burst after the offset of hyperpolarization, while the persistent sodium current (INaP, red) underlies the subsequent prolonged rebound period of spiking. Depending on the relative density of GCaT and GNaP present, each component of the rebound can be more or less pronounced. Note that IHCN (blue) only makes a minor contribution to the rebound depolarization. This is due to the small driving force of IHCN (reversal potential of −45 mV) during the rebound depolarization. (b) A pronounced fast spike burst is present with high GCaT density. The peak of ICaT reaches −1.85 nA, and is shown truncated to allow visualization of smaller currents

Mentions: The pronounced variability in rebounds between neurons can be understood most easily by examining differences in the strength of rebound currents for variable densities of the underlying rebound conductances (Fig. 4). In all cases, during baseline firing, the CaT and NaP rebound conductances were predominantly inactivated and they had only a small influence on baseline firing rate. Following the de-inactivation of ICaT and INaP during negative current injection, they each activated with a characteristic profile after the offset of hyperpolarization, and the strength of the respective rebound component was determined by the amount of conductance present (Fig. 4). ICaT provided a fast current of substantial amplitude (peak current of −1.85 nA for a GCaT density of 3.5 S/m2, Fig. 4b), which then rapidly inactivated and thus led to the transient fast rebound spike burst. INaP activated more slowly and gave rise to a much smaller peak current of 190 pA for a conductance density of 8 S/m2 (Fig. 4a). Due to its slow inactivation time constant this current persisted for over 1 s. Despite its small amplitude it led to a pronounced increase of spiking from the 11.9 Hz baseline to 32.1 Hz at 300 ms following the offset of the current injection (with 1.5 S/m2 GCaT and 2.0 S/m2 GHCN also present, Fig. 4a). This is due to the fact that the spike rate of the model neuron is sensitive to small currents as noted above.Fig. 4


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

Steuber V, Schultheiss NW, Silver RA, De Schutter E, Jaeger D - J Comput Neurosci (2010)

Currents underlying rebound patterns in the model of Neuron 1 and Neuron 3. The T-type calcium current (ICaT, green) underlies the fast burst after the offset of hyperpolarization, while the persistent sodium current (INaP, red) underlies the subsequent prolonged rebound period of spiking. Depending on the relative density of GCaT and GNaP present, each component of the rebound can be more or less pronounced. Note that IHCN (blue) only makes a minor contribution to the rebound depolarization. This is due to the small driving force of IHCN (reversal potential of −45 mV) during the rebound depolarization. (b) A pronounced fast spike burst is present with high GCaT density. The peak of ICaT reaches −1.85 nA, and is shown truncated to allow visualization of smaller currents
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3108018&req=5

Fig4: Currents underlying rebound patterns in the model of Neuron 1 and Neuron 3. The T-type calcium current (ICaT, green) underlies the fast burst after the offset of hyperpolarization, while the persistent sodium current (INaP, red) underlies the subsequent prolonged rebound period of spiking. Depending on the relative density of GCaT and GNaP present, each component of the rebound can be more or less pronounced. Note that IHCN (blue) only makes a minor contribution to the rebound depolarization. This is due to the small driving force of IHCN (reversal potential of −45 mV) during the rebound depolarization. (b) A pronounced fast spike burst is present with high GCaT density. The peak of ICaT reaches −1.85 nA, and is shown truncated to allow visualization of smaller currents
Mentions: The pronounced variability in rebounds between neurons can be understood most easily by examining differences in the strength of rebound currents for variable densities of the underlying rebound conductances (Fig. 4). In all cases, during baseline firing, the CaT and NaP rebound conductances were predominantly inactivated and they had only a small influence on baseline firing rate. Following the de-inactivation of ICaT and INaP during negative current injection, they each activated with a characteristic profile after the offset of hyperpolarization, and the strength of the respective rebound component was determined by the amount of conductance present (Fig. 4). ICaT provided a fast current of substantial amplitude (peak current of −1.85 nA for a GCaT density of 3.5 S/m2, Fig. 4b), which then rapidly inactivated and thus led to the transient fast rebound spike burst. INaP activated more slowly and gave rise to a much smaller peak current of 190 pA for a conductance density of 8 S/m2 (Fig. 4a). Due to its slow inactivation time constant this current persisted for over 1 s. Despite its small amplitude it led to a pronounced increase of spiking from the 11.9 Hz baseline to 32.1 Hz at 300 ms following the offset of the current injection (with 1.5 S/m2 GCaT and 2.0 S/m2 GHCN also present, Fig. 4a). This is due to the fact that the spike rate of the model neuron is sensitive to small currents as noted above.Fig. 4

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Science and Technology Research Institute, University of Hertfordshire, Hatfield Herts, AL10 9AB, UK.

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.

Show MeSH
Related in: MedlinePlus