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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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Match of DCN model neuron to physiological properties. (a) Morphology of reconstructed neuron used for our model. The artificially created axon attached to the soma is not shown. (b) (left column) Slice recording of typical DCN neuron. Voltages shown are junction potential corrected (see Section 2). (right column) Sample simulation traces show good match in spike shape and afterhyperpolarization properties with physiology. (c) Spike rates as a function of injected current for three recorded neurons (dotted lines), and the model (solid line). The f-I curve for the model was obtained with rebound conductance densities GNaP of 8 S/m2, GHCN of 1 S/m2, and GGCaT of 2 S/m2. These conductances, however, had little influence on spontaneous activity or on the f-I curve
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Fig1: Match of DCN model neuron to physiological properties. (a) Morphology of reconstructed neuron used for our model. The artificially created axon attached to the soma is not shown. (b) (left column) Slice recording of typical DCN neuron. Voltages shown are junction potential corrected (see Section 2). (right column) Sample simulation traces show good match in spike shape and afterhyperpolarization properties with physiology. (c) Spike rates as a function of injected current for three recorded neurons (dotted lines), and the model (solid line). The f-I curve for the model was obtained with rebound conductance densities GNaP of 8 S/m2, GHCN of 1 S/m2, and GGCaT of 2 S/m2. These conductances, however, had little influence on spontaneous activity or on the f-I curve

Mentions: Morphology and passive model properties The morphological reconstruction and fitting of passive parameters for this model were described in a previous publication (Steuber et al. 2004). Briefly, the DCN neuron on which the model morphology is based (Fig. 1(a)) was recorded for passive responses to positive and negative current injection pulses in the presence of blockers for voltage-gated ion channels (in mM: TTX 0.001, TEA 10, 4-AP 2, Cd2+ 0.2, Ni2+ 2, Cs+ 5, amiloride 0.5) and synaptic inputs (in mM: CNQX 0.01 and picrotoxin 0.02). After morphological reconstruction of the recorded neuron and preparation of a GENESIS morphology file a genetic algorithm was used to find the passive parameters of the model that provided an optimal fit to the voltage responses to current injection pulses injected into the original neuron. The passive parameter values we used for the present study were derived from this fitting procedure, and are given by uniform specific RM of 3.56 Ωm2 , RA of 2.35 Ωm, and CM of 0.0156 F/m2. We applied a small hyperpolarizing current pulse to the soma of this passive model to simulate the method by which membrane capacitance has been estimated for DCN neurons in slice experiments (Uusisaari et al. 2007). Fitting an exponential to the observed voltage decay and using the relationship that Cm = τm / RN, our model showed an apparent membrane capacitance (Cm) of 203 pF, which is in good agreement with the apparent Cm of glutamate decarboxylase (GAD) negative DCN neurons (Uusisaari et al. 2007). While remaining errors in the morphological reconstruction and passive parameters can not be excluded, the performance of the active model to moderate variations in passive parameters was quite stable, since the conductance amplitudes of voltage-gated currents dominated over the leak conductance. Using the parameters described here our model (soma and 485 dendritic compartments) had a total membrane resistance of 271 MΩ, a maximal electrotonic length of 0.53 λ from the soma to the most distal dendritic tip, and a mean dendritic electrotonic distance from the soma of 0.2 λ. Thus this model should be considered moderately electrotonically compact, as a voltage step applied to the soma will not decay much in the dendrites (Rall 1959). The axon of the original neuron was not reconstructed, but an artificial 30 compartment axon with nodal and internodal segments was pasted onto our reconstructed cell following a published method (Shen et al. 1999) to allow for an axonal spike initiation zone.Fig. 1


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)

Match of DCN model neuron to physiological properties. (a) Morphology of reconstructed neuron used for our model. The artificially created axon attached to the soma is not shown. (b) (left column) Slice recording of typical DCN neuron. Voltages shown are junction potential corrected (see Section 2). (right column) Sample simulation traces show good match in spike shape and afterhyperpolarization properties with physiology. (c) Spike rates as a function of injected current for three recorded neurons (dotted lines), and the model (solid line). The f-I curve for the model was obtained with rebound conductance densities GNaP of 8 S/m2, GHCN of 1 S/m2, and GGCaT of 2 S/m2. These conductances, however, had little influence on spontaneous activity or on the f-I curve
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3108018&req=5

Fig1: Match of DCN model neuron to physiological properties. (a) Morphology of reconstructed neuron used for our model. The artificially created axon attached to the soma is not shown. (b) (left column) Slice recording of typical DCN neuron. Voltages shown are junction potential corrected (see Section 2). (right column) Sample simulation traces show good match in spike shape and afterhyperpolarization properties with physiology. (c) Spike rates as a function of injected current for three recorded neurons (dotted lines), and the model (solid line). The f-I curve for the model was obtained with rebound conductance densities GNaP of 8 S/m2, GHCN of 1 S/m2, and GGCaT of 2 S/m2. These conductances, however, had little influence on spontaneous activity or on the f-I curve
Mentions: Morphology and passive model properties The morphological reconstruction and fitting of passive parameters for this model were described in a previous publication (Steuber et al. 2004). Briefly, the DCN neuron on which the model morphology is based (Fig. 1(a)) was recorded for passive responses to positive and negative current injection pulses in the presence of blockers for voltage-gated ion channels (in mM: TTX 0.001, TEA 10, 4-AP 2, Cd2+ 0.2, Ni2+ 2, Cs+ 5, amiloride 0.5) and synaptic inputs (in mM: CNQX 0.01 and picrotoxin 0.02). After morphological reconstruction of the recorded neuron and preparation of a GENESIS morphology file a genetic algorithm was used to find the passive parameters of the model that provided an optimal fit to the voltage responses to current injection pulses injected into the original neuron. The passive parameter values we used for the present study were derived from this fitting procedure, and are given by uniform specific RM of 3.56 Ωm2 , RA of 2.35 Ωm, and CM of 0.0156 F/m2. We applied a small hyperpolarizing current pulse to the soma of this passive model to simulate the method by which membrane capacitance has been estimated for DCN neurons in slice experiments (Uusisaari et al. 2007). Fitting an exponential to the observed voltage decay and using the relationship that Cm = τm / RN, our model showed an apparent membrane capacitance (Cm) of 203 pF, which is in good agreement with the apparent Cm of glutamate decarboxylase (GAD) negative DCN neurons (Uusisaari et al. 2007). While remaining errors in the morphological reconstruction and passive parameters can not be excluded, the performance of the active model to moderate variations in passive parameters was quite stable, since the conductance amplitudes of voltage-gated currents dominated over the leak conductance. Using the parameters described here our model (soma and 485 dendritic compartments) had a total membrane resistance of 271 MΩ, a maximal electrotonic length of 0.53 λ from the soma to the most distal dendritic tip, and a mean dendritic electrotonic distance from the soma of 0.2 λ. Thus this model should be considered moderately electrotonically compact, as a voltage step applied to the soma will not decay much in the dendrites (Rall 1959). The axon of the original neuron was not reconstructed, but an artificial 30 compartment axon with nodal and internodal segments was pasted onto our reconstructed cell following a published method (Shen et al. 1999) to allow for an axonal spike initiation zone.Fig. 1

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