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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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The role of IHCN in controlling rebound spiking. (a) In the presence of a high density of 8 S/m2 GNaP and a low density of 1.5 S/m2 GCaT (Neuron 3) increasing the GHCN density from 0 to 2 S/m2 resulted in an advance of the fast rebound by 50 ms while having little influence on the rebound pattern. The stimulus here was a 500 ms voltage clamp to −90 mV. The current plots show that IHCN activated gradually over the course of the voltage-clamp hyperpolarization, and that following stimulus offset this led to a substantial inward current that sped up depolarization. Due to the reversal potential of IHCN at −45 mV this current was much diminished during rebound spiking and turned into a transient outward current during each spike. Using a voltage clamp stimulus isolated the effect of IHCN activation on rebound rates, since a current injection stimulus with varying GHCN densities would also lead to varying hyperpolarization potentials and thus would change the de-inactivation of GNaP and GCaT. (b) Rebound rate histograms show the graded dependence of rebound delay on the level of GHCN present. These histograms are constructed by calculating an instantaneous spiking rate trace for each simulation (see Section 2), and by subtracting the spike rate in a simulation without hyperpolarizing stimulus from the otherwise identical simulation containing this stimulus. Thus, the spike rate before the stimulus subtracts to 0, during the hyperpolarizing stimulus the difference between background and stimulated trace drops to the negative of the spontaneous firing rate, and after the stimulus the rebound spike rate shows an increase in spiking above baseline. Insets show the graded delay of the fast rebound at a higher temporal resolution. (c) Dependence of rebound latency after stimulus offset on GHCN. (d–f) The same effects of GHCN were seen for a reduced GNaP and increased GCaT (Neuron 1). Note that GHCN in this case also significantly shaped the rebound pattern in that it suppresses the oscillatory nature of the ICaT rebound (see Section 3). A density of 2 S/m2 GHCN led to a current of −220 pA at 150 ms following stimulus offset, which presented the critical time window to prevent the ISK induced pause
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Fig5: The role of IHCN in controlling rebound spiking. (a) In the presence of a high density of 8 S/m2 GNaP and a low density of 1.5 S/m2 GCaT (Neuron 3) increasing the GHCN density from 0 to 2 S/m2 resulted in an advance of the fast rebound by 50 ms while having little influence on the rebound pattern. The stimulus here was a 500 ms voltage clamp to −90 mV. The current plots show that IHCN activated gradually over the course of the voltage-clamp hyperpolarization, and that following stimulus offset this led to a substantial inward current that sped up depolarization. Due to the reversal potential of IHCN at −45 mV this current was much diminished during rebound spiking and turned into a transient outward current during each spike. Using a voltage clamp stimulus isolated the effect of IHCN activation on rebound rates, since a current injection stimulus with varying GHCN densities would also lead to varying hyperpolarization potentials and thus would change the de-inactivation of GNaP and GCaT. (b) Rebound rate histograms show the graded dependence of rebound delay on the level of GHCN present. These histograms are constructed by calculating an instantaneous spiking rate trace for each simulation (see Section 2), and by subtracting the spike rate in a simulation without hyperpolarizing stimulus from the otherwise identical simulation containing this stimulus. Thus, the spike rate before the stimulus subtracts to 0, during the hyperpolarizing stimulus the difference between background and stimulated trace drops to the negative of the spontaneous firing rate, and after the stimulus the rebound spike rate shows an increase in spiking above baseline. Insets show the graded delay of the fast rebound at a higher temporal resolution. (c) Dependence of rebound latency after stimulus offset on GHCN. (d–f) The same effects of GHCN were seen for a reduced GNaP and increased GCaT (Neuron 1). Note that GHCN in this case also significantly shaped the rebound pattern in that it suppresses the oscillatory nature of the ICaT rebound (see Section 3). A density of 2 S/m2 GHCN led to a current of −220 pA at 150 ms following stimulus offset, which presented the critical time window to prevent the ISK induced pause

Mentions: The HCN conductance has been suggested as a main contributing factor to rebound behavior in DCN neurons (Aizenman and Linden 1999) as it activates during hyperpolarization and can provide a remaining inward current following it. However, our simulations showed that the contribution of the HCN current to rebound spiking was limited by its small amplitude (Fig. 4) which was due to a small driving force during the rebound period when the membrane potential was close to the IHCN reversal potential of −45 mV. Nevertheless, IHCN had modulatory influences on the rebound properties in addition to diminishing hyperpolarization during negative current injection. To isolate effects of IHCN at a controlled level of hyperpolarization we simulated rebound responses induced by transient voltage clamp pulses to −90 mV for the different combinations of GNaP and GCaT present in Neurons 1 and 3 (Fig. 5). Using this protocol, we determined the effect of GHCN without modulating the de-inactivation of GCaT and GNaP. We found that the presence of GHCN had two additional influences on the rebound.Fig. 5


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)

The role of IHCN in controlling rebound spiking. (a) In the presence of a high density of 8 S/m2 GNaP and a low density of 1.5 S/m2 GCaT (Neuron 3) increasing the GHCN density from 0 to 2 S/m2 resulted in an advance of the fast rebound by 50 ms while having little influence on the rebound pattern. The stimulus here was a 500 ms voltage clamp to −90 mV. The current plots show that IHCN activated gradually over the course of the voltage-clamp hyperpolarization, and that following stimulus offset this led to a substantial inward current that sped up depolarization. Due to the reversal potential of IHCN at −45 mV this current was much diminished during rebound spiking and turned into a transient outward current during each spike. Using a voltage clamp stimulus isolated the effect of IHCN activation on rebound rates, since a current injection stimulus with varying GHCN densities would also lead to varying hyperpolarization potentials and thus would change the de-inactivation of GNaP and GCaT. (b) Rebound rate histograms show the graded dependence of rebound delay on the level of GHCN present. These histograms are constructed by calculating an instantaneous spiking rate trace for each simulation (see Section 2), and by subtracting the spike rate in a simulation without hyperpolarizing stimulus from the otherwise identical simulation containing this stimulus. Thus, the spike rate before the stimulus subtracts to 0, during the hyperpolarizing stimulus the difference between background and stimulated trace drops to the negative of the spontaneous firing rate, and after the stimulus the rebound spike rate shows an increase in spiking above baseline. Insets show the graded delay of the fast rebound at a higher temporal resolution. (c) Dependence of rebound latency after stimulus offset on GHCN. (d–f) The same effects of GHCN were seen for a reduced GNaP and increased GCaT (Neuron 1). Note that GHCN in this case also significantly shaped the rebound pattern in that it suppresses the oscillatory nature of the ICaT rebound (see Section 3). A density of 2 S/m2 GHCN led to a current of −220 pA at 150 ms following stimulus offset, which presented the critical time window to prevent the ISK induced pause
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Related In: Results  -  Collection

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Fig5: The role of IHCN in controlling rebound spiking. (a) In the presence of a high density of 8 S/m2 GNaP and a low density of 1.5 S/m2 GCaT (Neuron 3) increasing the GHCN density from 0 to 2 S/m2 resulted in an advance of the fast rebound by 50 ms while having little influence on the rebound pattern. The stimulus here was a 500 ms voltage clamp to −90 mV. The current plots show that IHCN activated gradually over the course of the voltage-clamp hyperpolarization, and that following stimulus offset this led to a substantial inward current that sped up depolarization. Due to the reversal potential of IHCN at −45 mV this current was much diminished during rebound spiking and turned into a transient outward current during each spike. Using a voltage clamp stimulus isolated the effect of IHCN activation on rebound rates, since a current injection stimulus with varying GHCN densities would also lead to varying hyperpolarization potentials and thus would change the de-inactivation of GNaP and GCaT. (b) Rebound rate histograms show the graded dependence of rebound delay on the level of GHCN present. These histograms are constructed by calculating an instantaneous spiking rate trace for each simulation (see Section 2), and by subtracting the spike rate in a simulation without hyperpolarizing stimulus from the otherwise identical simulation containing this stimulus. Thus, the spike rate before the stimulus subtracts to 0, during the hyperpolarizing stimulus the difference between background and stimulated trace drops to the negative of the spontaneous firing rate, and after the stimulus the rebound spike rate shows an increase in spiking above baseline. Insets show the graded delay of the fast rebound at a higher temporal resolution. (c) Dependence of rebound latency after stimulus offset on GHCN. (d–f) The same effects of GHCN were seen for a reduced GNaP and increased GCaT (Neuron 1). Note that GHCN in this case also significantly shaped the rebound pattern in that it suppresses the oscillatory nature of the ICaT rebound (see Section 3). A density of 2 S/m2 GHCN led to a current of −220 pA at 150 ms following stimulus offset, which presented the critical time window to prevent the ISK induced pause
Mentions: The HCN conductance has been suggested as a main contributing factor to rebound behavior in DCN neurons (Aizenman and Linden 1999) as it activates during hyperpolarization and can provide a remaining inward current following it. However, our simulations showed that the contribution of the HCN current to rebound spiking was limited by its small amplitude (Fig. 4) which was due to a small driving force during the rebound period when the membrane potential was close to the IHCN reversal potential of −45 mV. Nevertheless, IHCN had modulatory influences on the rebound properties in addition to diminishing hyperpolarization during negative current injection. To isolate effects of IHCN at a controlled level of hyperpolarization we simulated rebound responses induced by transient voltage clamp pulses to −90 mV for the different combinations of GNaP and GCaT present in Neurons 1 and 3 (Fig. 5). Using this protocol, we determined the effect of GHCN without modulating the de-inactivation of GCaT and GNaP. We found that the presence of GHCN had two additional influences on the rebound.Fig. 5

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