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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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Dependence of rebound strength on depth of preceding hyperpolarization. (a) The rebound for this combination of rebound conductances with a high GNaP density (simulating Neuron 3 in Fig. 3) shows a pronounced increase in firing rate during the prolonged rebound period when the negative current injection (250 ms duration) is increased from −100 pA to −250 pA. The fast rebound due to ICaT is also increased by increasing the negative current injection, but not as much. The peak hyperpolarization during the 250 ms −100 nA cip was −77 mV, whereas it was −97 mV for −250 pA. Note the increase in IHCN activation (blue current trace) with the −250 pA stimulus. (b) Rebound rate histograms show the graded dependence of rebound spiking on the preceding hyperpolarization. The peak rebound rate shown for the fast rebound depends on the shortest inter-spike interval (ISI) present, which lengthens for diminished stimulus amplitudes, but also depends on the number of spikes in the fast rebound (three spikes for −200 and −250 pA, 2 for −150 and −100 pA, 0 for −50 pA) because several spikes at short intervals increase the instantaneous spike rate through local averaging by the addition of Gaussians (see Section 2). (c) The same current injection stimuli as in (a) are shown for our simulation of Neuron 1 (Fig. 3). This simulation has a lower density of GNaP and GHCN, but a higher density of GCaT. Due the presence of less GHCN the hyperpolarization reached in this simulation is larger than that for Neuron 3 and reached −81 mV for −100 pA and −104 mV for −250 pA. (d) Rebound rate histograms showing a robust fast rebound for most current injection levels. There is a small persistent spike rate increase with large negative current injection, which amounts to an increase in spike rate from 12.1 Hz spontaneous activity to 18.8 Hz during the prolonged rebound period following −250 pA current injection (rate averaged over 500 ms). The fast rebound burst in the presence of high GCaT density is robust from −150 to −250 pA current injections. The number of spikes in the fast rebound was 6 for −250 and −200 pA, 5 for −200 pA, 3 for −150 pA, and 0 for −50 pA
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Fig6: Dependence of rebound strength on depth of preceding hyperpolarization. (a) The rebound for this combination of rebound conductances with a high GNaP density (simulating Neuron 3 in Fig. 3) shows a pronounced increase in firing rate during the prolonged rebound period when the negative current injection (250 ms duration) is increased from −100 pA to −250 pA. The fast rebound due to ICaT is also increased by increasing the negative current injection, but not as much. The peak hyperpolarization during the 250 ms −100 nA cip was −77 mV, whereas it was −97 mV for −250 pA. Note the increase in IHCN activation (blue current trace) with the −250 pA stimulus. (b) Rebound rate histograms show the graded dependence of rebound spiking on the preceding hyperpolarization. The peak rebound rate shown for the fast rebound depends on the shortest inter-spike interval (ISI) present, which lengthens for diminished stimulus amplitudes, but also depends on the number of spikes in the fast rebound (three spikes for −200 and −250 pA, 2 for −150 and −100 pA, 0 for −50 pA) because several spikes at short intervals increase the instantaneous spike rate through local averaging by the addition of Gaussians (see Section 2). (c) The same current injection stimuli as in (a) are shown for our simulation of Neuron 1 (Fig. 3). This simulation has a lower density of GNaP and GHCN, but a higher density of GCaT. Due the presence of less GHCN the hyperpolarization reached in this simulation is larger than that for Neuron 3 and reached −81 mV for −100 pA and −104 mV for −250 pA. (d) Rebound rate histograms showing a robust fast rebound for most current injection levels. There is a small persistent spike rate increase with large negative current injection, which amounts to an increase in spike rate from 12.1 Hz spontaneous activity to 18.8 Hz during the prolonged rebound period following −250 pA current injection (rate averaged over 500 ms). The fast rebound burst in the presence of high GCaT density is robust from −150 to −250 pA current injections. The number of spikes in the fast rebound was 6 for −250 and −200 pA, 5 for −200 pA, 3 for −150 pA, and 0 for −50 pA

Mentions: We found that for our default current injection period of 250 ms the prolonged rebound increased gradually with the amount of negative current injected and the resulting level of hyperpolarization (shown for Neuron 3 in Fig. 6(a, b)). The fast burst was also much increased for −250 pA compared to −100 pA current injection (Neuron 1, Fig. 6(c, d)), though it was already more robustly expressed at lower injection levels than the prolonged rebound (Fig. 6(b)).Fig. 6


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)

Dependence of rebound strength on depth of preceding hyperpolarization. (a) The rebound for this combination of rebound conductances with a high GNaP density (simulating Neuron 3 in Fig. 3) shows a pronounced increase in firing rate during the prolonged rebound period when the negative current injection (250 ms duration) is increased from −100 pA to −250 pA. The fast rebound due to ICaT is also increased by increasing the negative current injection, but not as much. The peak hyperpolarization during the 250 ms −100 nA cip was −77 mV, whereas it was −97 mV for −250 pA. Note the increase in IHCN activation (blue current trace) with the −250 pA stimulus. (b) Rebound rate histograms show the graded dependence of rebound spiking on the preceding hyperpolarization. The peak rebound rate shown for the fast rebound depends on the shortest inter-spike interval (ISI) present, which lengthens for diminished stimulus amplitudes, but also depends on the number of spikes in the fast rebound (three spikes for −200 and −250 pA, 2 for −150 and −100 pA, 0 for −50 pA) because several spikes at short intervals increase the instantaneous spike rate through local averaging by the addition of Gaussians (see Section 2). (c) The same current injection stimuli as in (a) are shown for our simulation of Neuron 1 (Fig. 3). This simulation has a lower density of GNaP and GHCN, but a higher density of GCaT. Due the presence of less GHCN the hyperpolarization reached in this simulation is larger than that for Neuron 3 and reached −81 mV for −100 pA and −104 mV for −250 pA. (d) Rebound rate histograms showing a robust fast rebound for most current injection levels. There is a small persistent spike rate increase with large negative current injection, which amounts to an increase in spike rate from 12.1 Hz spontaneous activity to 18.8 Hz during the prolonged rebound period following −250 pA current injection (rate averaged over 500 ms). The fast rebound burst in the presence of high GCaT density is robust from −150 to −250 pA current injections. The number of spikes in the fast rebound was 6 for −250 and −200 pA, 5 for −200 pA, 3 for −150 pA, and 0 for −50 pA
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Related In: Results  -  Collection

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Fig6: Dependence of rebound strength on depth of preceding hyperpolarization. (a) The rebound for this combination of rebound conductances with a high GNaP density (simulating Neuron 3 in Fig. 3) shows a pronounced increase in firing rate during the prolonged rebound period when the negative current injection (250 ms duration) is increased from −100 pA to −250 pA. The fast rebound due to ICaT is also increased by increasing the negative current injection, but not as much. The peak hyperpolarization during the 250 ms −100 nA cip was −77 mV, whereas it was −97 mV for −250 pA. Note the increase in IHCN activation (blue current trace) with the −250 pA stimulus. (b) Rebound rate histograms show the graded dependence of rebound spiking on the preceding hyperpolarization. The peak rebound rate shown for the fast rebound depends on the shortest inter-spike interval (ISI) present, which lengthens for diminished stimulus amplitudes, but also depends on the number of spikes in the fast rebound (three spikes for −200 and −250 pA, 2 for −150 and −100 pA, 0 for −50 pA) because several spikes at short intervals increase the instantaneous spike rate through local averaging by the addition of Gaussians (see Section 2). (c) The same current injection stimuli as in (a) are shown for our simulation of Neuron 1 (Fig. 3). This simulation has a lower density of GNaP and GHCN, but a higher density of GCaT. Due the presence of less GHCN the hyperpolarization reached in this simulation is larger than that for Neuron 3 and reached −81 mV for −100 pA and −104 mV for −250 pA. (d) Rebound rate histograms showing a robust fast rebound for most current injection levels. There is a small persistent spike rate increase with large negative current injection, which amounts to an increase in spike rate from 12.1 Hz spontaneous activity to 18.8 Hz during the prolonged rebound period following −250 pA current injection (rate averaged over 500 ms). The fast rebound burst in the presence of high GCaT density is robust from −150 to −250 pA current injections. The number of spikes in the fast rebound was 6 for −250 and −200 pA, 5 for −200 pA, 3 for −150 pA, and 0 for −50 pA
Mentions: We found that for our default current injection period of 250 ms the prolonged rebound increased gradually with the amount of negative current injected and the resulting level of hyperpolarization (shown for Neuron 3 in Fig. 6(a, b)). The fast burst was also much increased for −250 pA compared to −100 pA current injection (Neuron 1, Fig. 6(c, d)), though it was already more robustly expressed at lower injection levels than the prolonged rebound (Fig. 6(b)).Fig. 6

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