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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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Control of spiking by a background of randomly timed excitatory and inhibitory inputs. a–c The pattern of synaptic input consists of the random activation of excitatory inputs at 30 Hz and inhibitory inputs at 40 Hz (see Section 2) and is identical for all 3 simulations shown. The amplitude of unitary EPSC and IPSC is multiplied by a factor of 2 between low and intermediate and intermediate and high unitary synaptic conductances (Gsyn). For the lowest Gsyn values the GABAA peak conductance is 50 pS, the AMPA peak conductance is also 50 pS, and the NMDA peak conductance (slow + fast, see Section 2) is 43 pS. For this low Gsyn (a) spiking is nearly regular and only mildly increased in frequency (17.5 Hz) from the spontaneous firing at 12.3 Hz. As Gsyn increases (b, c) the spontaneous oscillatory cycle is disrupted, and spiking becomes more irregular (increasing CV). In addition, for this ratio of inhibition and excitation spiking speeds up with increasing Gsyn. The bar graphs in (a2–c2) show ISI histograms, while the grey line shows the autocorrelation plot of the spike train. (d, e) Dependence of spike rate (d) and CV (e) of the simulated spike trains on Gsyn. All plots are constructed from a total data segment of 3.2 s duration. A model with GNaP of 8, GHCN of 0.8, and GCaT of 2 S/m2 was used, however, these conductances have little influence on the response to background inputs (see Fig. 10)
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Fig9: Control of spiking by a background of randomly timed excitatory and inhibitory inputs. a–c The pattern of synaptic input consists of the random activation of excitatory inputs at 30 Hz and inhibitory inputs at 40 Hz (see Section 2) and is identical for all 3 simulations shown. The amplitude of unitary EPSC and IPSC is multiplied by a factor of 2 between low and intermediate and intermediate and high unitary synaptic conductances (Gsyn). For the lowest Gsyn values the GABAA peak conductance is 50 pS, the AMPA peak conductance is also 50 pS, and the NMDA peak conductance (slow + fast, see Section 2) is 43 pS. For this low Gsyn (a) spiking is nearly regular and only mildly increased in frequency (17.5 Hz) from the spontaneous firing at 12.3 Hz. As Gsyn increases (b, c) the spontaneous oscillatory cycle is disrupted, and spiking becomes more irregular (increasing CV). In addition, for this ratio of inhibition and excitation spiking speeds up with increasing Gsyn. The bar graphs in (a2–c2) show ISI histograms, while the grey line shows the autocorrelation plot of the spike train. (d, e) Dependence of spike rate (d) and CV (e) of the simulated spike trains on Gsyn. All plots are constructed from a total data segment of 3.2 s duration. A model with GNaP of 8, GHCN of 0.8, and GCaT of 2 S/m2 was used, however, these conductances have little influence on the response to background inputs (see Fig. 10)

Mentions: One mode of operation of DCN neurons in the cerebellar circuit could be that of a rate coding device, in which the relative input rates of excitatory inputs from mossy fibers and inhibitory inputs from Purkinje cells determine the output spike rate. We examined the dynamic range of rate coding and the effect of inhibition on excitatory input–output relationships in our DCN model. Some of the input conditions used were identical to previous stimuli we employed in dynamic current clamping (Gauck and Jaeger 2003), thus allowing a direct comparison with experimental data. Figure 9 shows the effect of random trains of excitatory and inhibitory inputs (see Section 2) on spike rate and coefficient of variation (CV) in the model for different levels of input conductance. For unrealistically small unitary conductance values of 50 pS for Purkinje cell (PC) inhibition and 50 pS AMPA + 43 pS NMDA for mossy fiber (MF) excitation, inputs showed only a small effect on the spontaneous spike cycle even at high input rates (Fig. 9(a)). At this low synaptic conductance level the oscillatory intrinsic spike cycle was preserved as shown by pronounced side-peaks in the autocorrelation function (Fig. 9(a2)). In agreement with our dynamic clamp results for excitatory input that exceeded inhibition (Gauck and Jaeger 2000, 2003), when the synaptic conductances were doubled we saw a reduction in regularity of spiking and an increase in spike rate (Fig. 9(b)). Finally, a further doubling of the synaptic conductance led to a complete loss of autocorrelation side-peaks, a further decrease in regularity, and a further increase in spike rate (Fig. 9(c)). At this strength of input, the timing of individual spikes was determined by transients in the synaptic conductance pattern, and the intrinsic oscillatory spike cycle was overridden (Fig. 9(c2)). The four-fold increase of synaptic conductance levels from 50 pS to 200 pS resulted in a spike rate increase from 17.4 Hz to 37.1 Hz (Fig. 9(d)) and an increase in CV from 0.12 to 0.39 (Fig. 9(e)).Fig. 9


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)

Control of spiking by a background of randomly timed excitatory and inhibitory inputs. a–c The pattern of synaptic input consists of the random activation of excitatory inputs at 30 Hz and inhibitory inputs at 40 Hz (see Section 2) and is identical for all 3 simulations shown. The amplitude of unitary EPSC and IPSC is multiplied by a factor of 2 between low and intermediate and intermediate and high unitary synaptic conductances (Gsyn). For the lowest Gsyn values the GABAA peak conductance is 50 pS, the AMPA peak conductance is also 50 pS, and the NMDA peak conductance (slow + fast, see Section 2) is 43 pS. For this low Gsyn (a) spiking is nearly regular and only mildly increased in frequency (17.5 Hz) from the spontaneous firing at 12.3 Hz. As Gsyn increases (b, c) the spontaneous oscillatory cycle is disrupted, and spiking becomes more irregular (increasing CV). In addition, for this ratio of inhibition and excitation spiking speeds up with increasing Gsyn. The bar graphs in (a2–c2) show ISI histograms, while the grey line shows the autocorrelation plot of the spike train. (d, e) Dependence of spike rate (d) and CV (e) of the simulated spike trains on Gsyn. All plots are constructed from a total data segment of 3.2 s duration. A model with GNaP of 8, GHCN of 0.8, and GCaT of 2 S/m2 was used, however, these conductances have little influence on the response to background inputs (see Fig. 10)
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Related In: Results  -  Collection

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Fig9: Control of spiking by a background of randomly timed excitatory and inhibitory inputs. a–c The pattern of synaptic input consists of the random activation of excitatory inputs at 30 Hz and inhibitory inputs at 40 Hz (see Section 2) and is identical for all 3 simulations shown. The amplitude of unitary EPSC and IPSC is multiplied by a factor of 2 between low and intermediate and intermediate and high unitary synaptic conductances (Gsyn). For the lowest Gsyn values the GABAA peak conductance is 50 pS, the AMPA peak conductance is also 50 pS, and the NMDA peak conductance (slow + fast, see Section 2) is 43 pS. For this low Gsyn (a) spiking is nearly regular and only mildly increased in frequency (17.5 Hz) from the spontaneous firing at 12.3 Hz. As Gsyn increases (b, c) the spontaneous oscillatory cycle is disrupted, and spiking becomes more irregular (increasing CV). In addition, for this ratio of inhibition and excitation spiking speeds up with increasing Gsyn. The bar graphs in (a2–c2) show ISI histograms, while the grey line shows the autocorrelation plot of the spike train. (d, e) Dependence of spike rate (d) and CV (e) of the simulated spike trains on Gsyn. All plots are constructed from a total data segment of 3.2 s duration. A model with GNaP of 8, GHCN of 0.8, and GCaT of 2 S/m2 was used, however, these conductances have little influence on the response to background inputs (see Fig. 10)
Mentions: One mode of operation of DCN neurons in the cerebellar circuit could be that of a rate coding device, in which the relative input rates of excitatory inputs from mossy fibers and inhibitory inputs from Purkinje cells determine the output spike rate. We examined the dynamic range of rate coding and the effect of inhibition on excitatory input–output relationships in our DCN model. Some of the input conditions used were identical to previous stimuli we employed in dynamic current clamping (Gauck and Jaeger 2003), thus allowing a direct comparison with experimental data. Figure 9 shows the effect of random trains of excitatory and inhibitory inputs (see Section 2) on spike rate and coefficient of variation (CV) in the model for different levels of input conductance. For unrealistically small unitary conductance values of 50 pS for Purkinje cell (PC) inhibition and 50 pS AMPA + 43 pS NMDA for mossy fiber (MF) excitation, inputs showed only a small effect on the spontaneous spike cycle even at high input rates (Fig. 9(a)). At this low synaptic conductance level the oscillatory intrinsic spike cycle was preserved as shown by pronounced side-peaks in the autocorrelation function (Fig. 9(a2)). In agreement with our dynamic clamp results for excitatory input that exceeded inhibition (Gauck and Jaeger 2000, 2003), when the synaptic conductances were doubled we saw a reduction in regularity of spiking and an increase in spike rate (Fig. 9(b)). Finally, a further doubling of the synaptic conductance led to a complete loss of autocorrelation side-peaks, a further decrease in regularity, and a further increase in spike rate (Fig. 9(c)). At this strength of input, the timing of individual spikes was determined by transients in the synaptic conductance pattern, and the intrinsic oscillatory spike cycle was overridden (Fig. 9(c2)). The four-fold increase of synaptic conductance levels from 50 pS to 200 pS resulted in a spike rate increase from 17.4 Hz to 37.1 Hz (Fig. 9(d)) and an increase in CV from 0.12 to 0.39 (Fig. 9(e)).Fig. 9

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