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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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Rebound spiking as a function of synaptic input rates and conductance amplitudes in simulations of Neuron 1. (a1–2) Simulated rebounds of Neuron 1 for low and high input Gsyn at different input rates (exr = excitatory input rate, inr = inhibitory GABA input rate in Hz;). The chloride reversal potential is −90 mV throughout. In each case rebounds are elicited by a 250 ms burst of 300 Hz inhibitory input (through all GABA synapses). Insets show an expansion of the fast rebound peak. Negative rates during the inhibitory burst result from the subtraction of the input-driven firing rate without inhibitory input burst from the pause of firing resulting in all cases from the burst of inhibition. (b1–2) Dependence of rebound spike rate on inhibitory input rate for low and high values of Gsyn and low (b1) and high (b2) excitatory input rates. (b1) Robust rebounds in the presence of low inhibitory and excitatory background rates. (b2) High background levels of inhibitory input result in disappearance of rebounds
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Fig12: Rebound spiking as a function of synaptic input rates and conductance amplitudes in simulations of Neuron 1. (a1–2) Simulated rebounds of Neuron 1 for low and high input Gsyn at different input rates (exr = excitatory input rate, inr = inhibitory GABA input rate in Hz;). The chloride reversal potential is −90 mV throughout. In each case rebounds are elicited by a 250 ms burst of 300 Hz inhibitory input (through all GABA synapses). Insets show an expansion of the fast rebound peak. Negative rates during the inhibitory burst result from the subtraction of the input-driven firing rate without inhibitory input burst from the pause of firing resulting in all cases from the burst of inhibition. (b1–2) Dependence of rebound spike rate on inhibitory input rate for low and high values of Gsyn and low (b1) and high (b2) excitatory input rates. (b1) Robust rebounds in the presence of low inhibitory and excitatory background rates. (b2) High background levels of inhibitory input result in disappearance of rebounds

Mentions: The question of whether the pronounced rebound behavior of DCN neurons plays a significant role for synaptic coding in vivo is currently under active debate (Alvina et al. 2008; Pedroarena 2010; Tadayonnejad et al. 2009). In slice experiments with intracellular current injection neurons are easily driven to −100 mV or below. In contrast, inhibitory synaptic input bursts can only drive neurons close to the reversal potential of chloride (ECl), which can show a wide range of values, but is generally more positive than −90 mV in neurons. This could limit the expression of rebound spiking in DCN neurons following inhibitory input since de-inactivation of rebound conductances might not occur much at ECl. In addition, there is generally a high conductance baseline generated by background synaptic input in vivo (Destexhe et al. 2003; Stern et al. 1998), which could further diminish rebound firing through synaptic shunting of rebound responses. Such a baseline is certainly expected in DCN neurons, since Purkinje cells have a high baseline of tonic activity, and it has been estimated that each DCN neuron receives inputs from over 800 Purkinje cells (Palkovits et al. 1977). Thus it is not at all clear whether the strong rebound behavior of DCN neurons elicited with current injection in slice recordings has much relevance for the dynamics of these neurons with synaptic input in vivo. As already described in the preceding text, a background of randomly timed inputs modulated spiking in our DCN neuron model without any discernible involvement of rebound conductances. To examine whether rebound spiking could be elicited with stronger bursts of inhibitory inputs we subjected our model with different combinations of realistic fast and slow rebound currents to strong increases in inhibitory input rate for 250 ms in the presence of an ongoing background of random input (Figs. 11, 12, and 13). First, we varied ECl between −70 mV and −90 mV to determine its role in eliciting rebound spiking in the presence of synaptic background inputs (Fig. 11(a, e)). We found that fast and prolonged rebound spiking could be generated in the presence of background inputs, but only if ECl was sufficiently negative. The dependence of fast rebound spike rate on ECl in Neurons 2 and 3 is summarized in Fig. 11(d, h). In both Neuron 2 and 3 models, raising ECl from −90 mV to −70 mV led to a reduction of the fast rebound. However, the relationship between ECl and fast rebound spike rate was nearly linear for Neuron 3 with a low GCaT density (1.5 S/m2), while Neuron 2 with a higher GCaT density (4.5 S/m2) was much more robust against raising ECl and showed fast rebound responses at more than 100 Hz above baseline for ECl values of −75 mV and below.Fig. 11


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)

Rebound spiking as a function of synaptic input rates and conductance amplitudes in simulations of Neuron 1. (a1–2) Simulated rebounds of Neuron 1 for low and high input Gsyn at different input rates (exr = excitatory input rate, inr = inhibitory GABA input rate in Hz;). The chloride reversal potential is −90 mV throughout. In each case rebounds are elicited by a 250 ms burst of 300 Hz inhibitory input (through all GABA synapses). Insets show an expansion of the fast rebound peak. Negative rates during the inhibitory burst result from the subtraction of the input-driven firing rate without inhibitory input burst from the pause of firing resulting in all cases from the burst of inhibition. (b1–2) Dependence of rebound spike rate on inhibitory input rate for low and high values of Gsyn and low (b1) and high (b2) excitatory input rates. (b1) Robust rebounds in the presence of low inhibitory and excitatory background rates. (b2) High background levels of inhibitory input result in disappearance of rebounds
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3108018&req=5

Fig12: Rebound spiking as a function of synaptic input rates and conductance amplitudes in simulations of Neuron 1. (a1–2) Simulated rebounds of Neuron 1 for low and high input Gsyn at different input rates (exr = excitatory input rate, inr = inhibitory GABA input rate in Hz;). The chloride reversal potential is −90 mV throughout. In each case rebounds are elicited by a 250 ms burst of 300 Hz inhibitory input (through all GABA synapses). Insets show an expansion of the fast rebound peak. Negative rates during the inhibitory burst result from the subtraction of the input-driven firing rate without inhibitory input burst from the pause of firing resulting in all cases from the burst of inhibition. (b1–2) Dependence of rebound spike rate on inhibitory input rate for low and high values of Gsyn and low (b1) and high (b2) excitatory input rates. (b1) Robust rebounds in the presence of low inhibitory and excitatory background rates. (b2) High background levels of inhibitory input result in disappearance of rebounds
Mentions: The question of whether the pronounced rebound behavior of DCN neurons plays a significant role for synaptic coding in vivo is currently under active debate (Alvina et al. 2008; Pedroarena 2010; Tadayonnejad et al. 2009). In slice experiments with intracellular current injection neurons are easily driven to −100 mV or below. In contrast, inhibitory synaptic input bursts can only drive neurons close to the reversal potential of chloride (ECl), which can show a wide range of values, but is generally more positive than −90 mV in neurons. This could limit the expression of rebound spiking in DCN neurons following inhibitory input since de-inactivation of rebound conductances might not occur much at ECl. In addition, there is generally a high conductance baseline generated by background synaptic input in vivo (Destexhe et al. 2003; Stern et al. 1998), which could further diminish rebound firing through synaptic shunting of rebound responses. Such a baseline is certainly expected in DCN neurons, since Purkinje cells have a high baseline of tonic activity, and it has been estimated that each DCN neuron receives inputs from over 800 Purkinje cells (Palkovits et al. 1977). Thus it is not at all clear whether the strong rebound behavior of DCN neurons elicited with current injection in slice recordings has much relevance for the dynamics of these neurons with synaptic input in vivo. As already described in the preceding text, a background of randomly timed inputs modulated spiking in our DCN neuron model without any discernible involvement of rebound conductances. To examine whether rebound spiking could be elicited with stronger bursts of inhibitory inputs we subjected our model with different combinations of realistic fast and slow rebound currents to strong increases in inhibitory input rate for 250 ms in the presence of an ongoing background of random input (Figs. 11, 12, and 13). First, we varied ECl between −70 mV and −90 mV to determine its role in eliciting rebound spiking in the presence of synaptic background inputs (Fig. 11(a, e)). We found that fast and prolonged rebound spiking could be generated in the presence of background inputs, but only if ECl was sufficiently negative. The dependence of fast rebound spike rate on ECl in Neurons 2 and 3 is summarized in Fig. 11(d, h). In both Neuron 2 and 3 models, raising ECl from −90 mV to −70 mV led to a reduction of the fast rebound. However, the relationship between ECl and fast rebound spike rate was nearly linear for Neuron 3 with a low GCaT density (1.5 S/m2), while Neuron 2 with a higher GCaT density (4.5 S/m2) was much more robust against raising ECl and showed fast rebound responses at more than 100 Hz above baseline for ECl values of −75 mV and below.Fig. 11

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