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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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Different typical rebound patterns of DCN neurons recorded in brain slices (Neurons 1–3, left column) and matching rebound types in the model (right column). Note that the HCN current activates well above −90 mV in our physiological data (dashed lines). Insets show an expanded waveform of a single spontaneous spike (red) and the initial portion of rebounds (blue). A current injection of −150 pA for 1.5 s elicited the hyperpolarization and following rebound in both the physiological traces and the model. Models using the combinations of rebound conductances replicating the rebound patterns of Neurons 1, 2 and 3 are referred to as models of these specific neurons in the remainder of the paper
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Fig3: Different typical rebound patterns of DCN neurons recorded in brain slices (Neurons 1–3, left column) and matching rebound types in the model (right column). Note that the HCN current activates well above −90 mV in our physiological data (dashed lines). Insets show an expanded waveform of a single spontaneous spike (red) and the initial portion of rebounds (blue). A current injection of −150 pA for 1.5 s elicited the hyperpolarization and following rebound in both the physiological traces and the model. Models using the combinations of rebound conductances replicating the rebound patterns of Neurons 1, 2 and 3 are referred to as models of these specific neurons in the remainder of the paper

Mentions: Physiological results Because rebound behavior is a prominent feature of DCN current clamp recordings in cerebellar slices, we placed a special emphasis on reproducing heterogeneous rebound dynamics in our model. To accurately determine the experimental range of rebound behaviors, we acquired 129 recordings of DCN neurons in brain slices in the presence of the synaptic input blockers picrotoxin and CNQX and analyzed the data for rebound responses to negative current injection pulses. We found that rebound responses could be described by the combination of two prominent components, which we termed fast rebound burst and prolonged rebound period (Fig. 3). The fast burst consisted of two to six spikes with diminishing amplitude superposed on a depolarized plateau (Fig. 3, Neuron 1 and Neuron 2, blue inset). All fast bursts had an initial ISI below 7 ms. We found a fast burst for 91 of the 129 analyzed neurons (71%). The fast rebound burst was followed by a pause of an average duration of 106 ± 35.2 ms in 53 of the 91 neurons (58%), after which spiking resumed with an increased rate compared to baseline. The remaining 38 neurons with a fast burst directly transitioned into a prolonged rebound period without an intervening interval of increased duration. For these neurons the presence of a distinct fast burst was ascertained by the presence of an initial ISI of less than 7 ms and a clear transition to slower spiking after two to six spikes. In a large proportion of recorded neurons with a fast burst and a pause a second dampened fast burst occurred before the onset of a prolonged rebound period (Fig. 3, Neuron 1). For 39 neurons we quantified properties of the fast rebound further for a 1.5 s stimulus of −150 pA amplitude. We found that the mean number of fast rebound spikes was 4.2 ± 0.7, the mean latency from the offset of the negative current injection pulses to the first rebound spike was 29.5 ± 11 ms, and the peak rebound frequency (determined by the shortest ISI) was 274 ± 67 Hz.Fig. 3


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)

Different typical rebound patterns of DCN neurons recorded in brain slices (Neurons 1–3, left column) and matching rebound types in the model (right column). Note that the HCN current activates well above −90 mV in our physiological data (dashed lines). Insets show an expanded waveform of a single spontaneous spike (red) and the initial portion of rebounds (blue). A current injection of −150 pA for 1.5 s elicited the hyperpolarization and following rebound in both the physiological traces and the model. Models using the combinations of rebound conductances replicating the rebound patterns of Neurons 1, 2 and 3 are referred to as models of these specific neurons in the remainder of the paper
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3108018&req=5

Fig3: Different typical rebound patterns of DCN neurons recorded in brain slices (Neurons 1–3, left column) and matching rebound types in the model (right column). Note that the HCN current activates well above −90 mV in our physiological data (dashed lines). Insets show an expanded waveform of a single spontaneous spike (red) and the initial portion of rebounds (blue). A current injection of −150 pA for 1.5 s elicited the hyperpolarization and following rebound in both the physiological traces and the model. Models using the combinations of rebound conductances replicating the rebound patterns of Neurons 1, 2 and 3 are referred to as models of these specific neurons in the remainder of the paper
Mentions: Physiological results Because rebound behavior is a prominent feature of DCN current clamp recordings in cerebellar slices, we placed a special emphasis on reproducing heterogeneous rebound dynamics in our model. To accurately determine the experimental range of rebound behaviors, we acquired 129 recordings of DCN neurons in brain slices in the presence of the synaptic input blockers picrotoxin and CNQX and analyzed the data for rebound responses to negative current injection pulses. We found that rebound responses could be described by the combination of two prominent components, which we termed fast rebound burst and prolonged rebound period (Fig. 3). The fast burst consisted of two to six spikes with diminishing amplitude superposed on a depolarized plateau (Fig. 3, Neuron 1 and Neuron 2, blue inset). All fast bursts had an initial ISI below 7 ms. We found a fast burst for 91 of the 129 analyzed neurons (71%). The fast rebound burst was followed by a pause of an average duration of 106 ± 35.2 ms in 53 of the 91 neurons (58%), after which spiking resumed with an increased rate compared to baseline. The remaining 38 neurons with a fast burst directly transitioned into a prolonged rebound period without an intervening interval of increased duration. For these neurons the presence of a distinct fast burst was ascertained by the presence of an initial ISI of less than 7 ms and a clear transition to slower spiking after two to six spikes. In a large proportion of recorded neurons with a fast burst and a pause a second dampened fast burst occurred before the onset of a prolonged rebound period (Fig. 3, Neuron 1). For 39 neurons we quantified properties of the fast rebound further for a 1.5 s stimulus of −150 pA amplitude. We found that the mean number of fast rebound spikes was 4.2 ± 0.7, the mean latency from the offset of the negative current injection pulses to the first rebound spike was 29.5 ± 11 ms, and the peak rebound frequency (determined by the shortest ISI) was 274 ± 67 Hz.Fig. 3

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