Kv1 K+ Channels Control Purkinje Cell Output to Facilitate Postsynaptic Rebound Discharge in Deep Cerebellar Neurons
Purkinje cells (PCs) generate the sole output of the cerebellar cortex and govern the timing of action potential discharge from neurons of the deep cerebellar nuclei (DCN). Here, we examine how voltage-gated Kv1 K+ channels shape intrinsically generated and synaptically controlled behaviors of PCs and address how the timing of DCN neuron output is modulated by manipulating PC Kv1 channels. Kv1 channels were studied in cerebellar slices at physiological temperatures with Kv1-specific toxins. Outside-out voltage-clamp recordings indicated that Kv1 channels are present in both somatic and dendritic membranes and are activated by Na+ spike-clamp commands. Whole-cell current-clamp recordings revealed that Kv1 K+ channels maintain low frequencies of Na+ spike and Ca-Na burst output, regulate the duration of plateau potentials, and set the threshold for Ca2+ spike discharge. Kv1 channels shaped the characteristics of climbing fiber (CF) responses evoked by extracellular stimulation or intracellular simulated EPSCs. In the presence of Kv1 toxins, CFs discharged spontaneously at approximately 1 Hz. Finally, "Kv1-intact" and "Kv1-deficient" PC tonic and burst outputs were converted to stimulus protocols and used as patterns to stimulate PC axons and synaptically activate DCN neurons. We found that the Kv1-intact patterns facilitated short-latency and high-frequency DCN neuron rebound discharges, whereas DCN neuron output timing was markedly disrupted by the Kv1-deficient stimulus protocols. Our results suggest that Kv1 K+ channels are critical for regulating the excitability of PCs and CFs and optimize the timing of PC outputs to generate appropriate discharge patterns in postsynaptic DCN neurons.
Available from: Henrik Jörntell
- "The parameters we tested for varied greatly within the same group of neurons (Figure 8), and the extent of the variability was overlapping between different types of neurons so that it was not possible to differentiate neuron types on basis of the model parameters. This was surprising, given that these types of neurons at least to some extent are known to vary with respect to the active conductances they express in their membranes (Mckay et al., 2005; Molineux et al., 2005; Forti et al., 2006; Zhong et al., 2010). One interpretation is that the spike generating mechanism relies on a limited set of ion channels (Schneidman et al., 1998; Fourcaud-Trocme et al., 2003; Naundorf et al., 2006; Saarinen et al., 2008; Stiefel et al., 2013), which is comparable across neuron types, but that the role of the bulk of the reactive membrane conductances is to shape the subthreshold responses of the membrane. "
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ABSTRACT: To any model of brain function, the variability of neuronal spike firing is a problem that needs to be taken into account. Whereas the synaptic integration can be described in terms of the original Hodgkin-Huxley (H-H) formulations of conductance-based electrical signaling, the transformation of the resulting membrane potential into patterns of spike output is subjected to stochasticity that may not be captured with standard single neuron H-H models. The dynamics of the spike output is dependent on the normal background synaptic noise present in vivo, but the neuronal spike firing variability in vivo is not well studied. In the present study, we made long-term whole cell patch clamp recordings of stationary spike firing states across a range of membrane potentials from a variety of subcortical neurons in the non-anesthetized, decerebrated state in vivo. Based on the data, we formulated a simple, phenomenological model of the properties of the spike generation in each neuron that accurately captured the stationary spike firing statistics across all membrane potentials. The model consists of a parametric relationship between the mean and standard deviation of the inter-spike intervals, where the parameter is linearly related to the injected current over the membrane. This enabled it to generate accurate approximations of spike firing also under inhomogeneous conditions with input that varies over time. The parameters describing the spike firing statistics for different neuron types overlapped extensively, suggesting that the spike generation had similar properties across neurons.
Frontiers in Cellular Neuroscience 07/2014; 8:199. DOI:10.3389/fncel.2014.00199 · 4.29 Impact Factor
Available from: Dirk J Snyders
- "Using this optimized technique we could detect Kv3.3b from p6. At this age the soma of the mouse PC reached its mature size, and growth of the neuron is restricted to the increase in size and branching complexity of the dendritic tree (Sadler and Berry, 1984; Scelfo et al., 2003; McKay and Turner, 2005). Therefore, the techniques used by Goldman- Wohl et al. (1994) may not have detected Kv3.3b in a fully-grown soma and the reported developmental up regulation could have represented dendritic growth as Kv3.3b is expressed throughout the entire cell (Rashid et al., 2001; McKay and Turner, 2004; Zagha et al., 2008). "
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ABSTRACT: The complex spike (CS) in cerebellar Purkinje Cells (PC) is not an all-or-nothing phenomena as originally proposed, but shows variability depending on the spiking behavior of the Inferior Olive and intrinsic variability in the number and shape of spikelets. The potassium channel Kv3.3b, which has been proposed to undergo developmental changes during the postnatal PC maturation, has been shown to be crucial for the repolarization of the spikelets in the CS. We address here the regulation of the intrinsic CS variability by the expression of inactivating Kv3.3 channels in PCs by combining patch-clamp recordings and single-cell PCR methods on the same neurons, using a technique that we recently optimized to correlate single cell transcription levels with membrane ion channel electrophysiology. We show that while the inactivating TEA sensitive Kv3.3 current peak intensity increases with postnatal age, the channel density does not, arguing against postnatal developmental changes of Kv3.3b expression. Real time PCR of Kv3.3b showed a high variability from cell to cell, correlated with the Kv3.3 current density, and suggesting that there are no mechanisms regulating these currents beyond the mRNA pool. We show a significant correlation between normalized quantity of Kv3.3b mRNA and both the number of CS spikelets and their rate of voltage fluctuation, linking the intrinsic CS shape directly to the Kv3.3b mRNA pool. Comparing the observed cell-to-cell variance with studies on transcriptional noise suggests that fluctuations of the Kv3.3b mRNA pool are possibly not regulated but represent merely transcriptional noise, resulting in intrinsic variability of the CS.
Frontiers in Cellular Neuroscience 11/2013; 7:205. DOI:10.3389/fncel.2013.00205 · 4.29 Impact Factor
Available from: Michael D Forrest
- "This aligns with in vitro experiments, where the firing of some Purkinje cells can be switched from an imposed bimodal pattern, to an intrinsic trimodal pattern, by pharmacological blocking of GABAergic synaptic inputs . The firing of some other Purkinje cells is intrinsically bimodal and remains bimodal when synaptic inputs are blocked . "
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ABSTRACT: In vitro, cerebellar Purkinje cells can intrinsically fire action potentials in a repeating trimodal or bimodal pattern. The trimodal pattern consists of tonic spiking, bursting, and quiescence. The bimodal pattern consists of tonic spiking and quiescence. It is unclear how these firing patterns are generated and what determines which firing pattern is selected. We have constructed a realistic biophysical Purkinje cell model that can replicate these patterns. In this model, Na(+)/K(+) pump activity sets the Purkinje cell's operating mode. From rat cerebellar slices we present Purkinje whole cell recordings in the presence of ouabain, which irreversibly blocks the Na(+)/K(+) pump. The model can replicate these recordings. We propose that Na(+)/K(+) pump activity controls the intrinsic firing mode of cerbellar Purkinje cells.
PLoS ONE 12/2012; 7(12):e51169. DOI:10.1371/journal.pone.0051169 · 3.23 Impact Factor
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