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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 spike rate by varying mean rates of excitatory and inhibitory inputs. (a1) The DCN neuron shows a near-linear relationship between excitatory input rate and output spike rate for a low value of Gsyn. (a2) Inhibitory synaptic input performs an additive operation. This is shown by a nearly perfect overlay of input–output curves that have been shifted my multiples of −6.5 Hz along the input axis (shift = −6.5 n Hz, where n = inhibitory input rate/10 Hz). (b1, c1) At high Gsyn, the relationships between excitatory input rate and output rate become less linear. (b2, c2) Inhibition still performs an additive operation for higher values of Gsyn, but the addition is less clear than for low Gsyn values (shown by the lower quality of the overlay of input–output curves that have been shifted by multiples of −6.5 Hz along the input axis). (d1–2) The control of spiking by intermediate values of Gsyn for a different combination of rebound conductances. There is virtually no effect of rebound conductances on the control of output spiking by random background input (see Section 3)
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Fig10: Control of spike rate by varying mean rates of excitatory and inhibitory inputs. (a1) The DCN neuron shows a near-linear relationship between excitatory input rate and output spike rate for a low value of Gsyn. (a2) Inhibitory synaptic input performs an additive operation. This is shown by a nearly perfect overlay of input–output curves that have been shifted my multiples of −6.5 Hz along the input axis (shift = −6.5 n Hz, where n = inhibitory input rate/10 Hz). (b1, c1) At high Gsyn, the relationships between excitatory input rate and output rate become less linear. (b2, c2) Inhibition still performs an additive operation for higher values of Gsyn, but the addition is less clear than for low Gsyn values (shown by the lower quality of the overlay of input–output curves that have been shifted by multiples of −6.5 Hz along the input axis). (d1–2) The control of spiking by intermediate values of Gsyn for a different combination of rebound conductances. There is virtually no effect of rebound conductances on the control of output spiking by random background input (see Section 3)

Mentions: 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)


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 spike rate by varying mean rates of excitatory and inhibitory inputs. (a1) The DCN neuron shows a near-linear relationship between excitatory input rate and output spike rate for a low value of Gsyn. (a2) Inhibitory synaptic input performs an additive operation. This is shown by a nearly perfect overlay of input–output curves that have been shifted my multiples of −6.5 Hz along the input axis (shift = −6.5 n Hz, where n = inhibitory input rate/10 Hz). (b1, c1) At high Gsyn, the relationships between excitatory input rate and output rate become less linear. (b2, c2) Inhibition still performs an additive operation for higher values of Gsyn, but the addition is less clear than for low Gsyn values (shown by the lower quality of the overlay of input–output curves that have been shifted by multiples of −6.5 Hz along the input axis). (d1–2) The control of spiking by intermediate values of Gsyn for a different combination of rebound conductances. There is virtually no effect of rebound conductances on the control of output spiking by random background input (see Section 3)
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Related In: Results  -  Collection

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Fig10: Control of spike rate by varying mean rates of excitatory and inhibitory inputs. (a1) The DCN neuron shows a near-linear relationship between excitatory input rate and output spike rate for a low value of Gsyn. (a2) Inhibitory synaptic input performs an additive operation. This is shown by a nearly perfect overlay of input–output curves that have been shifted my multiples of −6.5 Hz along the input axis (shift = −6.5 n Hz, where n = inhibitory input rate/10 Hz). (b1, c1) At high Gsyn, the relationships between excitatory input rate and output rate become less linear. (b2, c2) Inhibition still performs an additive operation for higher values of Gsyn, but the addition is less clear than for low Gsyn values (shown by the lower quality of the overlay of input–output curves that have been shifted by multiples of −6.5 Hz along the input axis). (d1–2) The control of spiking by intermediate values of Gsyn for a different combination of rebound conductances. There is virtually no effect of rebound conductances on the control of output spiking by random background input (see Section 3)
Mentions: 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)

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