Climbing Fiber Burst Size and Olivary Sub-threshold Oscillations in a Network Setting

The inferior olivary nucleus provides one of the two main inputs to the cerebellum: the so-called climbing fibers. Activation of climbing fibers is generally believed to be related to timing of motor commands and/or motor learning. Climbing fiber spikes lead to large all-or-none action potentials in cerebellar Purkinje cells, overriding any other ongoing activity and silencing these cells for a brief period of time afterwards. Empirical evidence shows that the climbing fiber can transmit a short burst of spikes as a result of an olivary cell somatic spike, potentially increasing the information being transferred to the cerebellum per climbing fiber activation. Previously reported results from in vitro studies suggested that the information encoded in the climbing fiber burst is related to the occurrence of the spike relative to the ongoing sub-threshold membrane potential oscillation of the olivary cell, i.e. that the phase of the oscillation is reflected in the size of the climbing fiber burst. We used a detailed three-compartmental model of an inferior olivary cell to further investigate the possible factors determining the size of the climbing fiber burst. Our findings suggest that the phase-dependency of the burst size is present but limited and that charge flow between soma and dendrite is a major determinant of the climbing fiber burst. From our findings it follows that phenomena such as cell ensemble synchrony can have a big effect on the climbing fiber burst size through dendrodendritic gap-junctional coupling between olivary cells

Behavioral correlates of complex spike synchrony in cerebellar microzones

The olivo-cerebellar system is crucial for smooth and well timed execution of movements based on sensory and proprioceptive cues. The inferior olive (IO) plays a pivotal role in this process by synchronizing its activity across neurons internally through connexin36 gap junctions and providing a timing and/or learning signal to the cerebellum. Even though synchrony achieved through electrical coupling in IO cells is generally thought to be important in timing motor output, a direct relation between timing of movement and synchrony of olivary discharges has never been demonstrated within functional microcomplexes using transgenics. Here we combined in vivo, two-photon calcium imaging of complex spikes in microcomplexes of Purkinje cell (PC) dendrites with high-speed filming of tail, trunk, and limb movements in awake wild-type and connexin36-deficient mice. In wild types at rest, functional clusters of PCs were poorly defined with synchrony correlations that were relatively small and spatially limited to mediolateral distances of ∼50 μm, whereas during locomotion synchrony of the same PCs increased in strength and extended over distances spanning multiple microzones that could be correlated to specific components of sharp and well bounded movements. Instead, connexin36-deficient mice exhibited prolonged and desynchronized complex spike activity within PC microcomplexes both at rest and during behavior. Importantly, the mutants also showed concomitant abnormalities in the execution of spinocerebellar reflexes, which were significantly slower and more gradual than in wild-type littermates, particularly following sensory perturbations. Our results highlight the importance of modulation of synchronous activity within and between cerebellar microcomplexes in on-line temporal processing of motor output

Phase dependency of AP spikelet counts.

<p>Phase dependency of IO cell AP spikelet count under natural (intrinsic STO) conditions and when a sinusoidal 5 Hz STO is imposed through current injection. Left panels show the results for full network stimulation, corresponding with e.g. stimulating a fiber beam, whereas the right panels show the results for single-cell stimulation. Under all four conditions, the STO establishes a firing window outside of which the cell does not fire action potentials (the phase range where spikes were not generated is indicated in red), in concordance with earlier findings <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002814#pcbi.1002814-Khosrovani1" target="_blank">[17]</a>. However, the actual boundaries of the firing window are different when an oscillation that differs from the intrinsic STO is imposed (bottom panels as opposed to top panels). When the entire network is stimulated, there is a clear phase-dependency of AP spikelet count, as the spikelet count ranges from 1 to 4 depending on the phase (left panels). However, when only one cell in a cluster of 9 cells is stimulated, this phase dependency is less clear, as the spikelet count can still take different values, but is of limited variability and generally equals 2 (right panels).</p

Strength and timing of motor responses mediated by rebound firing in the cerebellar nuclei after Purkinje cell activation

The cerebellum refines the accuracy and timing of motor performance. How it encodes information to perform these functions is a major topic of interest. We performed whole cell and extracellular recordings of Purkinje cells (PCs) and cerebellar nuclei neurons (CNs) in vivo, while activating PCs with light in transgenic mice. We show for the first time that graded activation of PCs translates into proportional CN inhibition and induces rebound activity in CNs, which is followed by graded motor contractions timed to the cessation of the stimulus. Moreover, activation of PC ensembles led to disinhibition of climbing fiber activity, which coincided with rebound activity in CNs. Our data indicate that cessation of concerted activity in ensembles of PCs can regulate both timing and strength of movements via control of rebound activity in CNs

Factors underlying AP spikelet count.

<p><b>A.</b> Correlation between spike ADP duration and AP spikelet count. In general, a longer ADP increases the chances of larger amounts of spikelets on top of the somatic ADP. This is most readily apparent when stimulating the entire network (top panel). Still, the ADP duration by itself does not provide an adequate explanation for the amount of spikelets that are part of the spike shape, since the number of spikelets can still vary considerably even within a millisecond bin both when the entire networks fires and when only one cell does. When only one cell in the network fires, the relation between ADP duration and number of spikelets does not appear to be linear, even though on average a higher number of spikelets is still more likely at longer ADP durations (bottom panel). <b>B.</b> Changing the intrinsic conductance values of the low-threshold calcium current changes the amplitude of the oscillations (as indicated by the dashed lines aligned with the peaks and troughs of the depicted traces), but also the number of spikelets (time of occurrence is indicated with red markers in each depicted trace). The number of spikelets decreases as the T-type calcium expression level decreases. <b>C.</b> Spike ADP and dendrosomatic coupling currents as AP spikelet count determinants. Single cells in a 9-cell network were stimulated for all of the simulation results shown. Warmer colors represent higher numbers of AP spikelets (color-coding is the same for all four panels and indicated in the figure). Spike ADP duration and dendrosomatic charge flow form a trajectory. It is readily apparent from all four panels that higher numbers of spikelets are more likely to occur at longer ADPs, but in addition decreased charge flow increases the chance of generating an AP with more spikelets. As a result, the prediction of AP spikelet count can be improved when taking both ADP duration and dendrosomatic charge flow into account. At different T-type calcium expression levels, the range of possible spikelet counts and the distributions thereof vary. Clearly, spike ADP and dendrosomatic coupling currents are major determinants for the AP spikelet count measured at the soma, but other currents both intra- and intercellular can cause local phenomena in the distributions along the trajectories shown.</p

Architecture and electrophysiological properties of the cell model.

<p><b>A.</b> Schematic representation of the three-compartmental cell model used. From top to bottom, the compartments represent the dendrite, the soma and the axon hillock (also indicated in gray on the left). Current flows internally between the dendritic and the somatic compartment as well as between the somatic compartment and the axon hillock, as indicated in orange. In addition, current can flow between a cell and up to eight cells it is connected to through the gap junctions in the dendritic compartment, indicated in green. Each compartment has its own set of ion channels. The dendrite has a high-threshold calcium current I_CaH (P/Q-type) and resultant internal calcium concentration [Ca²⁺], a calcium-dependent potassium current I_K,Ca, a cationic current I_h and a passive leak current I_ld. At the soma, there is a low-threshold calcium current I_CaL (T-type), a fast sodium current I_Na, a potassium current with a slow component I_K,s and a fast component I_K,f, and a passive leak current I_ls. The axon hillock compartment has a fast sodium current I_Na,ax, a fast potassium current I_K,f and a passive leak current I_la. <b>B.</b> Normalized representation of the major STO components at the soma. The gray line shows the somatic membrane potential as a reference. The upward slope of the STO is caused by an activation of low-threshold calcium ion channels, leading to a depolarizing current (blue line). As the membrane potential becomes more depolarized, the calcium ion channels inactivate and current leaking from soma to dendrite increases in intensity (red line), causing the membrane potential to drop again. <b>C.</b> Example of a spike. A depolarizing current is applied at the dendritic compartment (red line), which exhibits a slow depolarization. The somatic compartment (black line) responds to this with a slow depolarization on top of which a fast sodium spike is generated. The axon hillock (blue line) shows fast sodium responses to the depolarization in the somatic compartment: the peak of the first sodium spike occurs before the somatic sodium spike (as reported by Mathy et al. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002814#pcbi.1002814-Schweighofer1" target="_blank">[27]</a>) and a burst of spikes is generated riding on the somatic depolarization. This burst of spikes is propagated back to the soma to some extent and is visible as spikelets on the calcium depolarization.</p

Modulation of Electrotonic Coupling in the Inferior Olive by Inhibitory and Excitatory Inputs: Integration in the Glomerulus

Dendritic spines in glomeruli of the inferior olive are coupled by gap junctions and receive both inhibitory and excitatory inputs. In this issue of Neuron, Lefler et al. (2014), Mathy et al. (2014), and Turecek et al. (2014) provide new insight into how these inputs modulate electrical coupling and oscillatory activity

Olivary subthreshold oscillations and burst activity revisited

The inferior olive forms one of the major gateways for information that travels to the cerebellar cortex. Olivary neurons process sensory and motor signals that are subsequently relayed to Purkinje cells. The intrinsic subthreshold membrane potential oscillations of the olivary neurons are thought to be important for gating this flow of information. In vitro studies have revealed that the phase of the subthreshold oscillation determines the size of the olivary burst and may gate the information flow or encode the temporal state of the olivary network. Here, we investigated whether the same phenomenon occurred in murine olivary cells in an intact olivocerebellar system using the in vivo whole-cell recording technique. Our in vivo findings revealed that the number of wavelets within the olivary burst did not encode the timing of the spike relative to the phase of the oscillation but was related to the amplitude of the oscillation. Manipulating the oscillation amplitude by applying Harmaline confirmed the inverse relationship between the amplitude of oscillation and the number of wavelets within the olivary burst. Furthermore, we demonstrated that electrotonic coupling between olivary neurons affect this modulation of the olivary burst size. Based on these results, we suggest that the olivary burst size might reflect the expectancy of a spike to occur rather than the spike timing, and that this process requires the presence of gap junction coupling