Next, to exclude any major involvement of postsynaptic receptor saturation we investigated the impact of γDGG, which is a competitive antagonist of AMPARs that effectively suppresses EPSCs by occupying a portion of the available AMPARs. Because of its competitive behavior and fast unbinding rate, the relative suppression by γDGG is a perfect reporter of glutamate transients; in the case of postsynaptic receptor saturation, excessive glutamate will compete with γDGG to overcome its suppressive effect and EPSCs will be unaffected by its presence. At 1 mM, γDGG effectively suppressed all responses by more than 35% of their original size. However, no effect was observed on the linear behavior of responses (*R*2-values 0.993 ± 0.001; *n* = 5). In addition, in accordance with our earlier results, slopes were significantly lower at 100 and 200 Hz compared with other frequencies (*p* < 0.05; *n* = 5), yet those at 300, 500, and 700 Hz were indistinguishable from each other (*p* > 0.05; *n* = 5; **Figure 3E**). Interestingly, the relative reduction was strongest for short bursts,

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but showed a gradual decrease toward bursts of five pulses at 200, 300, and 500 Hz (*p* < 0.05 for responses of two versus five pulses at all three frequencies; *n* = 5; **Figure 3F**), indicative of additional glutamate release with consecutive pulses. However, no further decrease was observed for longer bursts (*p* < 0.05 for responses of five versus 20 pulses; *n* = 5), meaning that release had reached its optimum within five pulses. Together, these results indicate that signaling at high-frequency between PF-PC is restricted to the presynaptic site by insufficient release. While some additional release might occur within the first few pulses of a burst, prolonged activation cannot be maintained. Also, despite the facilitation we found for individual EPSCs at 100 Hz (**Figure 2C**), this was not accompanied by a shift in the relative glutamate content per synapse (**Figure 3F**). Therefore, the reported facilitation mostly reflects recruitment of more synapses and not additional transmitter release at individual synapses, highlighting that results obtained through stimulation of a bundle of PFs cannot be extrapolated directly to the level of individual synapses. More so, our data show that overall synaptic release is not facilitated at 100 Hz.

#### **PAIRED RECORDINGS OF GrCs AND PCs** *IN VITRO*

The results described above showed the implications of grouped activity from synchronously activated GrCs, but they failed to accurately describe the behavior of individual connections. We therefore performed paired whole-cell patch clamp recordings from 13 connected GrC – PC pairs in mice. Because this configuration turned out to be relatively short-lived, we could not test responses for the same wide range of stimuli as with extracellular stimulation. We chose to elicit bursts of action potentials at 200 Hz, because at this frequency, on the one hand, we found some limitations such as depletion with prolonged activity, while on the

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other hand we observed potential room for additional signaling at higher frequencies (up to 300 Hz; see above).

Recordings from individual GrC-PC pairs confirmed a low initial RP, yet substantial differences were observed between these pairs (**Figures 4A–D**). Overall, we found an initial failure rate (FR) of 0.83 ± 0.01 (**Figure 4D**). The FR was significantly reduced to a minimum of 0.66 ± 0.02 at the third action potential (*p* < 0.05; *n* = 13). However, from the third action potential on, the FR began to show a gradual increase again and returned to baseline levels at the sixth action potential (*p* > 0.05 1st versus 6th action potential, *p* < 0.05 3rd versus 6th action potential; *n* = 13). On the basis of the cumulative RP calculated over the first 3 action potentials [RPcumulative 1−3 = 1-(FR1\*FR2\*FR3)], pairs could be separated into a "reluctant"-group (RPcumulative 1−3 < 0.55, average RPcumulative 1−3 = 0.41 ± 0.05; *n* = 8) and a "responsive"-group (RPcumulative 1−3 > 0.8, average RPcumulative 1−3 = 0.86 ± 0.02; *n* = 4; **Figure 4A**). Even though the initial FR was similar between the two groups (*p* > 0.05; *n* = 8 and *n* = 4), a dramatic difference was observed between the FR of the 2nd and 3rd spike (*p* > 0.05, 0.86 ± 0.01 and 0.78 ± 0.01 for reluctant connections; 0.48 ± 0.03 and 0.43 ± 0.05 for responsive connections, respectively; *n* = 8 and *n* = 4, respectively; **Figure 4D**). No difference in FR was observed after the 3rd spike (*p* > 0.05; *n* = 8 and *n* = 4). Accordingly, the cumulative RP (RPcumulative 1−n = 1-(FR1\*FR2\*FRn), unveiled a striking difference between the two groups (**Figure 4E**); whereas the chance of releasefor at least one of the action potentials reached >90% for the responsive connections at the fourth action potential this chance grew to ∼50% for reluctant connections and had only reached ∼75% at the 7th action potential.

The EPSC amplitude of the response to the first action potential was 8.54 pA ± 0.22 (*n* = 8) for reluctant connections (**Figure 4C**). No further change in amplitude occurred throughout the burst. Amplitudes for responsive connections were significantly larger for the 2nd, 3rd, and 6th action potential (*p* < 0.05; *n* = 4) and also responses to the 4th and 5th spike followed this trend. Thus, although the initial RP is low at PF-PC synapses, we conclude that high-frequency burst activity may facilitate release within the first few spikes (both in reluctant and responsive connections) and result in a high cumulative RP (in particular in responsive connections) to ensure signaling and overcome a low initial RP.

#### **DISCUSSION**

For a long time, it was believed that cerebellar GrCs operate at low firing frequencies (but see Eccles et al., 1966). It is only due to recent technical advances that GrCs have been shown to fire bursts of action potentials at surprisingly high frequencies of several 100 Hz with instantaneous frequencies up to 1 kHz (Chadderton et al., 2004; Hensbroek et al., 2006; Jörntell and Ekerot, 2006; Valera et al., 2012) and that individual action potentials in highfrequency bursts of GrCs are reliably translated into consistent calcium transients at presynaptic PF varicosities, showing only minor attenuation for intervals starting at approximately 500 Hz (Brenowitz and Regehr, 2007). Here, we demonstrate that mice can also show bursts of GrC activity at high-frequency, but that the capability of their PFs to relay frequency coded information to PCs is limited to ∼300 Hz for grouped activity. Moreover, at the level of individual PFs, release is too unreliable to convey temporally

coded information. In line with previous studies in rat (Brenowitz and Regehr, 2007; for review see Le Guen and De Zeeuw, 2010), murine PF inputs constitute a heterogenic group of terminals with various levels of RP allowing differential filtering. Our data suggest that high-frequency PF activity may facilitate transmitter release during initial spiking to ensure signaling onto PCs, but that variability is too high to allow temporally coded signaling at individual synapses.

#### **LIMITATIONS OF TRANSMITTER RELEASE AT THE MURINE PF-PC SYNAPSE**

Our main finding demonstrates that release from PF terminals onto PC dendrites is limited during physiologically relevant highfrequency bursts. Although the current observations in awake behaving mice and those of others in rabbits and anesthetized rats indicate that cerebellar GrCs can fire bursts of action potentials with instantaneous frequencies up to ∼1 kHz (Chadderton et al.,2004; Hensbroek et al.,2005; Ruigrok et al.,2011), our*in vitro* results following stimulation of bundles of PFs show that signaling at the murine PF-PC synapse is confined to ∼300 Hz at best. This maximal limit is likely to be an overestimation, because the stimulus conditions under which these results were obtained allow for different PFs to be active at different time points within a burst. In this manner, a group of PFs can maintain a highly effective activity pattern, while the effective activity of each individual terminal is in fact substantially lower (see also below). Indeed, our double patchclamp recordings of connected GrC-PC pairs in mice confirmed that overall PF terminals exhibit a low RP. These recordings showed a low average initial RP of ∼0.17, which is in line with previous observations obtained in rats by Dittman et al. (2000), but not by those ofValera et al. (2012), who reported an initial RP of 0.44. The differences with the latter study may be partially due to the specific strains of rodents used, differences in extracellular calcium concentration in the bath, and/or the approach used to calculate the RP. Valera et al. (2012) calculated RP with the multi-probabilityfluctuation-analysis (MPFA), a method that relies on a presumed distribution of Prsite (i.e., the probability for a single vesicle to be released at a particular site) to calculate RP from the quantal distribution. However, this approach does not take into account heterogeneity among synapses that arises from differential factors such as presynaptic calcium transients and/or build-up of residual calcium (Brenowitz and Regehr, 2007). Moreover, whereas Valera et al. (2012) used a noise-based signal-to-noise ratio to discriminate events in paired recordings, we applied a noise-based multi-variable threshold detection method; both methods have the potential to misrepresent RP either by missing small events or detecting noise, resulting in an underestimation or overestimation, respectively.

#### **PF TERMINALS IN MICE ARE HETEROGENIC**

We noticed a clear heterogeneous distribution between release properties of individual connected GrC-PC pairs. Given that larger EPSC amplitudes coincided with a higher RP, this difference might reflect diversity between potentiated and depressed synapses (Bender et al., 2009). However, we cannot exclude the possibility that this heterogeneity also results partly from differences between terminals from the ascending versus the horizontal segment of

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**FIGURE 4 | Paired GrC-PC recordings reveal heterogeneity in PF release probability. (A)** Examples of paired recordings. Based on the cumulative release probability over the first three spikes, pairs could be subdivided into a "reluctant" group (left) and "responsive" group (right). (top traces) Original voltage clamp recordings from PCs (purple and green for "reluctant" and "responsive" connections, respectively) and their processed signals used for analysis (red); asterisks indicate detected events. (bottom traces) Current clamp recordings from GrCs. Spiking is induced via current injection. Dashed lines indicate detected spikes. **(B)** Averaged responses over all recordings from cells shown in A. Beginning and end of current injections are indicated by arrowheads. Note a faster overall response in the responsive connection (green). **(C)** EPSC amplitudes did not change in size during the burst for

reluctant connections (purple, *p* > 0.05 for all comparisons). However, for responsive connections (green) EPSCs elicited by the 2nd, 3rd, and 6th spike were significantly larger than that by the 1st spike. No significant difference was observed between reluctant and responsive connections for EPSCs following the first spike. **(D)** Failure rate for all recorded pairs (gray), reluctant (purple), and responsive (green) connections. Responsive connections showed a short-lived facilitation for the 2nd and 3rd spike, resulting in a significantly smaller failure rate compared to reluctant connections (*p* < 0.05; *n* = 8 and *n* = 4, respectively; one cell out of the 13 was ambiguous and not included in one of the two main categories). **(E)** Cumulative probability of release for all recorded pairs (gray), reluctant (purple), and responsive (green) connections.

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PFs (Sims and Hartell, 2005, 2006). In addition, the heterogeneity may be related to the great diversity found in presynaptic calcium transients of PF terminals, which can differ more than 10-fold (Brenowitz and Regehr, 2007). It is not unlikely that this heterogeneity has resulted from experience-driven long-term plasticity, either facilitating or repressing the responsiveness of connections by adjusting presynaptic calcium transients.

Despite a heterogeneous distribution, we found a low initial RP for all PF-PC synapses; whereas the PF to Golgi cell synapse is considered to be weak (Dieudonné, 1998; Robberechts et al., 2010), the PF to molecular layer interneuron synapse has been shown to be more reliable (Crowley et al., 2007; Satake et al., 2012). These observations are remarkable, because all types of PF connections mentioned above probably persist along a single PF (Palay and Chan-Palay, 1974; Napper and Harvey, 1988), which inherently suggests differential filtering. Therefore, it will be intriguing to see how heterogeneity will affect the ability of PFs to signal at high frequencies, not only at their input to PCs, but also to Golgi cells and molecular layer interneurons. Moreover, it will be interesting to find out how the plasticity rules that control the efficacy at PF-PC synapses compare to those controlling PF-interneuron synapses (Gao et al., 2012).

#### **TECHNICAL LIMITATIONS AND CAVEATS**

We took three different approaches in the current study to investigate GrC–PC interactions; these included extracellular recordings of GrCs *in vivo*, recordings of intracellular PC responses *in vitro* following extracellular stimulation of bundles of PFs, and intracellular recordings of individual GrC-PC pairs *in vitro*. The *in vitro* approaches presented particular technical limitations and caveats. Recording EPSCs from PCs following PF activity is made notoriously complicated by several factors (Roth and Häusser, 2001): the large dimension of a PC prevents the desired control over electrical properties in patch-clamp experiments; the extensive dendritic arborization filters postsynaptic currents; the small size and condense packing of PFs make isolated activation difficult; and spontaneously active inputs obscure individually evoked events. These complications probably had some effect on the measurements following PF bundle stimulations as well as those during the paired recordings. When stimulating a bundle of PFs, the elicited response will be the multiplication of dynamic stochastic variables and, given a relatively low RP, will only reflect activity from a subset of all the stimulated PFs. Considering the heterogenic behavior, when some fibers might show activity to a substantial portion of the stimuli in a burst, while others might respond only periodically, the composed response will not reflect activity from a constant number of terminals. Because the total number of stimulated fibers is unknown, it is unclear what proportion is unresponsive. When, for example, a burst of 10 stimuli is given, the response to the 4th and 5th stimulus can be made up by a completely different subset of PFs. As a result, heterogeneity greatly limits the possibilities for analysis of those responses elicited by grouped activity and, in fact, limits applicability of methods proven effective at other release sites (Saviane and Silver, 2007; Valera et al., 2012). Yet, an equilibrium between driving and suppressing forces will establish as heterogenic differences and asynchronous activation of fibers will be averaged out at least partly by sustained activity. Thus, the measurements following extracellular bundle stimulation may to some degree be subject to misrepresentation, but the steadystate current can probably serve as an indirect indicator of overall synaptic efficacy (Saviane and Silver, 2006; Valera et al., 2012). Finally, finding connected pairs of GrCs and PCs was indeed difficult and holding on to both cells for long periods of time proved even more challenging. These technical difficulties forced us to restrict ourselves to investigate only the most physiologically relevant stimulus parameters and limited the power of our statistical analyses.

## **FUNCTIONAL IMPLICATIONS**

The notion that frequency coding is partially lost in individual GrC firing patterns has interesting implications for the cerebellar network as a whole and challenges the idea that GrCs merely act as interposed relay-neurons. What is the purpose for a GrC to fire a high-frequency burst when actual release falls behind? The main benefit of a relatively low RP is that few spontaneous events occur, creating a relatively noise-free environment. However, this comes at the expense of reliability and consistency; such a system is not well-designed to employ rate coding over its entire frequency range in a linear fashion. Nevertheless, as depicted in **Figure 4**, the cumulative probability of release at the murine PF-PC synapse reached nearly 1 within a few spikes for the "High RP"-group. This means that a brief PF burst could overcome the initial low RP to ensure release within the time window of the burst. Moreover, as the presynaptic insufficiency caused a rapid fall in RP, restricted release prevents immediate saturation of the postsynaptic site, thus leaving room for temporal summation at a lower rate. Ultimately, these characteristics point toward a non-linear mode of synaptic transmission, in which the actual occurrence of a synaptic event bears significance as well as its timing within a burst.

Granule cell activity is tightly controlled by tonic inhibition from Golgi cells, resulting in few action potentials at rest (Hensbroek et al., 2006; Mapelli and D'Angelo, 2007); GrCs can be relieved from this inhibition when Golgi cell activity is diminished or when excitatory MF input exceeds the inhibiting force. Moreover, glutamate released by MFs can directly act on Golgi cell terminals and suppress GABA release, forming an activitydependent feed-forward loop (Gao et al., 2012). When the balance is shifted from inhibition to excitation, a time window is created in which a GrC can fire a burst of action potentials (D'Angelo and De Zeeuw, 2009; Mapelli et al., 2010; Solinas et al., 2010). As such, the granular layer can be regarded as a "gate-keeper" that can selectively allow information to pass from MFs to PCs. Because MF terminals are tailored to maintain reliable signaling at very high frequencies (Sargent et al., 2005; Hallermann et al., 2010), it is remarkable that, to some extent, rate coding is lost further down the network. This implies that the information encoded by high-frequency firing of MFs may have limited value for PCs, but rather shapes the time window in which GrCs can produce a burst of activity. Thus, combining strong inhibition together with a relatively low RP results in a system in which synaptic events are restricted and a high signal-to-noise ratio is effectuated.

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Our finding that bundles of PFs can display a nearly linear, frequency-sensitive relationship between burst duration and total synaptic charge has some physiological relevance, because there is evidence that PFs are activated in bundles (Ebner et al., 2005). Moreover, GrCs are also prone to fire together in groups, because of the impact of Golgi cell inhibition, which can produce a center-surround pattern of activity in the granular layer further enhancing the filter function of the granular layer (Mapelli and D'Angelo, 2007). This leads to the interesting possibility that, while output from individual PFs can be relatively insignificant and poorly timed, a group of selectively activated PFs can reliably convey and maintain frequency coded MF activity, while background noise from spontaneous release can be minimized.

In conclusion, our findings indicate that at the PF-PC synapse the firing mode of GrCs in high-frequency bursts overcomes the unreliability and inconsistency this synapse exhibits so as to ensure signaling at the partial cost of rate coding. Together with strong Golgi cell inhibition and center-surround activation

## **REFERENCES**

of PF groups, this creates an environment in which the granular layer forms a strong spatiotemporal filter and, while a single GrC action potential becomes insignificant, controlled bursting can reliably convey selective information from MF input to PC output.

## **ACKNOWLEDGMENTS**

We kindly thank the Dutch Organization for Medical Sciences (ZonMw; FEH, CIDZ), Life Sciences (ALW; BvB, ZG, FEH, CIDZ), Erasmus university fellowship (FEH), Senter (NeuroBasic; CIDZ), and the ERC-advanced, CEREBNET and C7 programs of the European Community (CIDZ) for their financial support. The extracellular data recorded in the rabbit were acquired with the help of R. Hensbroek and J.I. Simpson and are included with their consent.We thank C. Hansel, J.I. Simpson as well as E. Galliano and other members of the De Zeeuw-lab for helpful discussions and critically reviewing the paper. The authors declare no competing financial interest.

morphologically identified interneurons in the vestibulocerebellum. *J. Neurosci.* 31, 712–724.

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of the cerebellum granular layer predicts cicuit spatio-temporal filtering properties. *Front. Cell. Neurosci.* 4:12. doi: 10.3389/fncel.2010.00012

Valera, A. M., Doussau, F., Poulain, B., Barbour, B., and Isope, P. (2012). Adaptation of granule cell to Purkinje cell synapses to high-frequency transmission. *J. Neurosci.* 32, 3267– 3280.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 27 November 2012; paper pending published: 17 February 2013;* *accepted: 30 April 2013; published online: 21 May 2013.*

*Citation: van Beugen BJ, Gao Z, Boele H-J, Hoebeek F and De Zeeuw CI (2013) High frequency burst firing of granule cells ensures transmission at the parallel fiber to Purkinje cell synapse at the cost of temporal coding. Front. Neural Circuits 7:95. doi: 10.3389/fncir.2013. 00095*

*Copyright © 2013 van Beugen, Gao, Boele, Hoebeek and De Zeeuw. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any thirdparty graphics etc.*

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## Non-Hebbian spike-timing-dependent plasticity in cerebellar circuits

## *Claire Piochon, Peter Kruskal , Jason MacLean and Christian Hansel\**

*Department of Neurobiology, University of Chicago, Chicago, IL, USA*

#### *Edited by:*

*Egidio D'Angelo, University of Pavia, Italy*

#### *Reviewed by:*

*Arnaud J. Ruiz, UCL School of Pharmacy, UK Andreas Frick, INSERM, France*

#### *\*Correspondence:*

*Christian Hansel, Department of Neurobiology, University of Chicago, 947 E. 58th Street/J243, Chicago, IL 60637, USA. e-mail<EMAIL_ADDRESS>
firing in target cells, are potentiated. At glutamatergic synapses onto hippocampal and neocortical pyramidal cells, synaptic activation followed by spike firing in the target cell causes long-term potentiation (LTP)—as predicted by Hebb—whereas excitatory postsynaptic potentials (EPSPs) evoked after a spike elicit long-term depression (LTD)—a phenomenon that was not specifically addressed by Hebb. In both instances the action potential in the postsynaptic target neuron is an instructive signal that is capable of supporting synaptic plasticity. STDP generally relies on the propagation of Na+ action potentials that are initiated in the axon hillhock back into the dendrite, where they cause depolarization and boost local calcium influx. However, recent studies in CA1 hippocampal pyramidal neurons have suggested that local calcium spikes might provide a more efficient trigger for LTP induction than backpropagating action potentials. Dendritic calcium spikes also play a role in an entirely different type of STDP that can be observed in cerebellar Purkinje cells. These neurons lack backpropagating Na+ spikes. Instead, plasticity at parallel fiber (PF) to Purkinje cell synapses depends on the relative timing of PF-EPSPs and activation of the glutamatergic climbing fiber (CF) input that causes dendritic calcium spikes. Thus, the instructive signal in this system is externalized. Importantly when EPSPs are elicited before CF activity, PF-LTD is induced rather than LTP. Thus, STDP in the cerebellum follows a timing rule that is opposite to its hippocampal/neocortical counterparts. Regardless, a common motif in plasticity is that LTD/LTP induction depends on the relative timing of synaptic activity and regenerative dendritic spikes which are driven by the instructive signal.

Spike-timing-dependent plasticity (STDP) provides a cellular implementation of the Hebb postulate, which states that synapses, whose activity repeatedly drives action potential

**Keywords: calcium, climbing fiber, dendrite, long-term depression, long-term potentiation, parallel fiber, Purkinje cell, pyramidal cell**

#### **INTRODUCTION**

Hebb's postulate on synaptic modifications, which was formulated in 1949 in his book "*The Organization of Behavior,*" has laid the foundation for subsequent experimental work on memory storage by neuronal assemblies (Hebb, 1949):

*"When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A's efficiency, as one of the cells firing B, is increased."*

A more popular version of this rule—assigned to neurobiologist Carla Shatz—says "neurons that fire together wire together." The discovery of long-term potentiation (LTP) in 1973 demonstrated that synaptic connections can indeed be strengthened in a use-dependent way, thus reflecting a key prediction of the Hebb postulate (Bliss and Lømo, 1973). LTP is now widely regarded as a potentiation mechanism involved in circuit development and adult learning. However, for more than 20 years, researchers did not dissociate the relative roles of synaptic input

and action potential generation in the postsynaptic neuron in the induction of LTP (see Linden, 1999). The implication inherent to Hebb's postulate is that excitatory synapses that contribute to the initiation of action potentials in the target cell will be strengthened. This component of the Hebb rule was demonstrated by spike-timing-dependent plasticity (STDP) studies, in which the relative timing of presynaptic activity and postsynaptic spike firing determines the direction and amplitude of synaptic weight change. Excitatory postsynaptic potentials (EPSPs) preceding postsynaptic action potentials within a time window of up to tens of milliseconds cause LTP, while activation in the reverse order induces long-term depression (LTD) (Markram et al., 1997; Bi and Poo, 1998; Debanne et al., 1998). While Hebb did not explicitly discuss the weakening of synapses in his hypothesis, LTD was suggested in a complementary statement by Stent (Stent, 1973) based on studies by Hubel and Wiesel examining plasticity during the critical period in visual cortex (Hubel and Wiesel, 1965; Wiesel and Hubel, 1965). STDP has generated immense interest as a plasticity mechanism that not only obeys Hebb's rule, but also reconciled LTP studies with a renewed interest in temporal coding (König et al., 1996).

We will begin this review with a description of key features of STDP as observed in hippocampal and neocortical pyramidal cells. Then, we will present recent observations that in CA1 hippocampal pyramidal cells LTP is more sensitive to local dendritic spikes than to backpropagating action potentials that originate in the axon hillhock (Golding et al., 2002). We will discuss these findings in an attempt to reach a general assessment of the role of dendritic spikes in forms of plasticity that depend on the detection of temporal order. There are more variations on the STDP theme: in cerebellum-like structures, such as the dorsal cochlear nucleus (DCN) or the electrosensory lobe (ELL) of mormyrid electric fish, anti-Hebbian STDP has been described, in which EPSPs followed by spikes induce LTD, and activation in the reverse order leads to LTP (Bell et al., 1997; Tzounopoulos et al., 2004). In the cerebellum itself, the available data also point toward an STDP rule with anti-Hebbian timing requirements (Wang et al., 2000). However, there are no regenerative Na+ spikes in Purkinje cell dendrites (Stuart and Häusser, 1994; Ohtsuki et al., 2012), and the direction of synaptic gain change at parallel fiber (PF) to Purkinje cell synapses depends on the co-activation of the climbing fiber (CF) input instead (Coesmans et al., 2004). CF activation causes two types of spikes that remain locally restricted: complex spikes in the soma and calcium spikes in the dendrite (Schmolesky et al., 2002; Davie et al., 2008). We will suggest that backpropagating action potentials provide an instructive plasticity signal in the neocortex and hippocampus, and that a similar function is served by the temporal correlation between local dendritic calcium spikes and synaptic activity in Purkinje cells. Thus, cerebellar plasticity is timing-dependent, but does not depend on somatic spike output and is thus non-Hebbian in nature.

#### **STDP AND THE BACKPROPAGATION OF SOMATIC ACTION POTENTIALS INTO DENDRITES**

Hebbian plasticity requires that activity at impinging synaptic inputs is paired with an instructive signal in the postsynaptic target neuron. This role can be served by the occurrence of an appropriately timed action potential, which propagates from the initial segment "back" into the dendrites. The discovery of action potential backpropagation into the dendrites was thus a prerequisite for an initial mechanistic description of Hebbian-style STDP. To demonstrate that action potentials are initiated close to the soma and actively invade the dendrites, Stuart, and Sakmann performed somato-dendritic double-patch recordings from layer V pyramidal neurons. They observed that (a) action potentials can be recorded in the dendrites after injection of depolarizing current pulses or synaptic stimulation, and (b) that action potentials are initiated in the axon hillock regardless of whether these action potentials were evoked by somatic or dendritic current injection, or by synaptic stimulation (Stuart and Sakmann, 1994). In summary, these results indicate that action potentials in these neurons are initiated close to the soma, and subsequently backpropagate into the dendrites. Similar observations were made in CA1 hippocampal pyramidal neurons (Spruston et al., 1995).

A role for backpropagating action potentials in plasticity was demonstrated a few years later. It was shown that in CA1 hippocampal pyramidal neurons pairing of subthreshold EPSPs with backpropagating action potentials causes LTP, and that the potentiation did not occur when these two stimuli were applied in isolation, or when spike backpropagation was blocked with local application of tetrodotoxin (TTX; Magee and Johnston, 1997). In a back-to-back paper in the same issue of *Science*, it was demonstrated using dual patch-clamp recordings that pairing of EPSPs with postsynaptic action potentials promotes LTP at synapses between connected layer 5 pyramidal neurons (Markram et al., 1997). This study also looked at the timing of pre- and postsynaptic activity in more detail, and found that LTP is induced when EPSPs precede the action potentials by 10 ms, but that application of these stimuli in reverse order results in LTD. Longer intervals (100 ms) neither elicit LTP nor LTD (Markram et al., 1997). Similarly narrow timing windows (≤20 ms) were found in STDP studied in hippocampal cultures/slice cultures (Bi and Poo, 1998; Debanne et al., 1998). **Figure 1A** shows original data from the study by Bi and Poo (1998), and illustrate that LTP results either from coincident occurrence of EPSPs and action potentials (0 ms latency), or from EPSPs followed by an action potential (positive latency), whereas LTD is observed when the spike precedes the EPSP (negative latency, see **Figure 1B** for a model scheme). Backpropagating action potentials evoke calcium transients in the dendrites that result from the activation of voltage-dependent calcium channels (Markram et al., 1995). The amplitude of spine calcium transients evoked by paired activation of EPSPs and action potentials depends on the temporal order. Calcium signals are larger when EPSPs precede action potentials by latencies of less than 50 ms and that calcium influx is less when the sequence is reversed (Koester and Sakmann, 1998; see also Graupner and Brunel, 2007). These findings are in line with the idea that LTP induction has a higher calcium threshold than LTD induction (Bienenstock et al., 1982; Bear et al., 1987; Hansel et al., 1997). This timing between pairings is not sufficient by itself. Packets of multiple pairings with this temporal structure are needed to provide sufficient depolarization, but lower frequency STDP pairings can also be effective given additional somatic depolarization (Sjöström et al., 2001). Further, given a burst of postsynaptic action potential firing paired with a single presynaptic action potential, the direction and extent of plasticity depends on the timing of dendritic calcium transients with the presynaptic spike (Zilberter et al., 2009). It should be noted, however, that the calcium transient amplitude is likely not the only factor involved. For example, it has been shown that the potentiation in STDP-style protocols is NMDA receptor-dependent, while LTD requires the activation of metabotropic glutamate (mGluR) receptors, suggesting that two different calcium sensors downstream of these receptors might regulate LTP and LTD induction (Nevian and Sakmann, 2006). Thus, the localization and specific activation/inactivation conditions of these calcium sensors are likely to influence the calcium signaling requirements as well. Regardless of the underlying details, it seems fair to say that local depolarization events and calcium transients serve key functions in controlling the LTP/LTD balance. But which dendritic activity patterns evoke the

*t*) between the onset of

timing. Spike timing is defined by the time interval (*-*

appropriate calcium transients under physiological conditions? It has been argued that local dendritic spikes, rather than backpropagating Na+ spikes may be instrumental for plasticity control (see Lisman and Spruston, 2010). This challenge to the classic STDP model is based on the observation that (a) backpropagating action potentials typically do not invade distal dendrites, and thus STDP may not be a general plasticity mechanism, (b) LTP can be induced in the absence of Na+ spikes, and (c) local depolarization can be more efficient in triggering LTP than backpropagating action potentials (Golding et al., 2002; Hardie and Spruston, 2009). This latter effect might result from the fact that local dendritic events, such as calcium spikes or AMPA/NMDA receptor-mediated responses provide a more prolonged depolarization than fast Na+ spikes (Lisman and Spruston, 2010). It is, however, conceivable that Na+ spikes, under conditions where they contribute to plasticity, facilitate the initiation of local calcium spikes, and that thus STDP is a physiologically relevant model for plasticity, but acts locally through dendritic calcium spikes. From a mechanistic point of view, these local calcium spikes can present an instructive signal whether or not their occurrence is facilitated by Na+ spike backpropagation (Hardie and Spruston, 2009).

#### **VARIATIONS OF SPIKE-TIMING-DEPENDENT PLASTICITY: ANTI-HEBBIAN STDP**

One of the very first reports of STDP did not result from recordings from hippocampal or neocortical pyramidal neurons, but from medium ganglion (MG) cells of the ELL of the mormyrid electric fish *Gnathonemus petersii* (Bell et al., 1997). The ELL is a cerebellum-like structure, and MG cells are GABAergic neurons that are described as Purkinje-like cells—they receive

the EPSP and the spike peak (see traces on top). Scale bars: 50 mV and 10 ms. **(B)** Model scheme for Hebbian STDP. LTD results when the spikes precede the EPSPs, whereas LTP is induced when the EPSPs are evoked before spike onset. **(A)** is taken from Bi and Poo (1998). Copyright 1998 by the Society for Neuroscience.

glutamatergic PF synapses, but lack the CF input that is characteristic for cerebellar Purkinje cells. In these Purkinje-like neurons, pairing of a PF-EPSP with a postsynaptic spike results in LTD if the spike follows the EPSP onset within 60 ms. In contrast, LTP is induced when the spikes are delivered outside this time window, or PF-EPSPs are evoked at 1 Hz in the absence of spikes (Bell et al., 1997; Han et al., 2000). Thus, STDP in this cerebellumlike structure follows an anti-Hebbian temporal order (**Figure 2**). The available data support the notion that this type of STDP is under control of the spike output of the postsynaptic target cell. The spikes that were evoked in these experiments by somatic current injection are so-called "broad spikes" that are TTX-sensitive (Bell et al., 1997), and are initiated in the soma/proximal dendrite, from where they propagate into the apical dendrite (Gomez et al., 2005; Engelmann et al., 2008). Broad spikes certainly differ from fast action potentials that are capable of producing cortical STDP—broad spikes are 8–15 ms wide and only reach amplitudes in the range of 40–60 mV (Bell et al., 1997). Still, these spikes are at least partially mediated by voltage-gated Na+ influx and are initiated in or near the soma, providing a signal that reflects the electrical output of MG cells. Thus, it seems fair to state that STDP in the ELL is anti-Hebbian with regard to the temporal order controlling LTP and LTD induction, but nevertheless falls into the category of Hebbian-style learning rules, because of the critical involvement of spike backpropagation into the dendrites. This type of anti-Hebbian STDP is not restricted to non-mammalian vertebrates, but has also been described in a mammalian cerebellum-like structure, the DCN, which is a brainstem region that is part of the auditory system. In cartwheel cells, which are inhibitory interneurons that resemble MG cells in the fish ELL, activation of EPSPs by PF stimulation leads to LTD if the

induced when the spike is initiated before an EPSP is evoked. Stimulation in the reverse order (EPSP-spike) results in LTD. Note that this figure panel shows an idealized model of anti-Hebbian STDP and differs from the experimentally obtained data presented in **(A)**, in which potentiation is also seen with intervals *>*400 ms. **(A)** is modified from Bell et al. (1997) with permission from Macmillan Publishers Ltd: Nature, copyright 1997.

EPSPs are followed after 5 ms by spike activity. No synaptic change results from activation in the reverse order (Tzounopoulos et al., 2004). In cartwheel cells, somatic depolarization leads to simple spike and/or complex spike firing, and is believed to trigger dendritic calcium spikes. The depression resulting from EPSP-spike sequences is presynaptically expressed and requires retrograde cannabinoid signaling (Tzounopoulos et al., 2007). Similarly, LTD induced by spike-EPSP sequences in layer 5 pyramidal neurons has been shown to require the activation of presynaptic CB1 receptors (Sjöström et al., 2003). Thus, this signaling mechanism is not restricted to anti-Hebbian STDP. We will discuss below that while anti-Hebbian STDP has been described in most detail in cerebellum-like structures, a form of STDP with anti-Hebbian (and non-Hebbian) components also plays a role in the cerebellum itself. Moreover, modeling studies have suggested that in CA1 hippocampal pyramidal neurons, anti-Hebbian STDP could function to equalize synaptic weights along the axis of the apical dendrite (Rumsey and Abbott, 2006). Due to the scope of this review, however, we will not discuss these modeling studies in detail.

delay between EPSP onset and the broad spike peak during the pairing period (360 stimuli at 1 Hz). **(B)** Model scheme for anti-Hebbian STDP. LTP is

### **NON-HEBBIAN STDP IN THE CEREBELLUM**

Surprisingly, STDP has not been studied in as much detail in the cerebellum proper. This might be due to the fact that in the cerebellum, LTP has been discovered later than in most other brain areas. While a presynaptic form of LTP has been described in 1996 (Salin et al., 1996), postsynaptic LTP—a potential reversal mechanism for the postsynaptically expressed LTD—has only been documented in 2002 (Lev-Ram et al., 2002). Nevertheless, sufficient data are available to draw some conclusions. LTD at PF synapses onto Purkinje cells results from co-activity of the PF and the CF input (Ito et al., 1982), during which the CF triggers complex spikes that can be recorded in the soma (for review, see Schmolesky et al., 2002). The first study that looked at timing requirements reported that LTD was induced best when CF stimulation (and complex spike activity) preceded PF activation with an interval of less than 250 ms (Ekerot and Kano, 1989). However, this report was inconclusive as (a) LTD was monitored with extracellular recordings of simple spikes—a measure that does not directly reflect synaptic plasticity, and (b) LTD was also observed when PF activity preceded CF activity by 5–20 ms. Subsequent studies found that LTD is most efficiently induced when PF stimulation precedes complex spike activity by 50–250 ms (Chen and Thompson, 1995; Wang et al., 2000; Safo and Regehr, 2008). Chen and Thompson showed that 600 pairings of PF and CF activity caused LTD independent of the timing interval. However, when using only 100 pairings, LTD is more sensitive to timing requirements: LTD was induced best when PF stimulation preceded CF activity by 250 ms, a depression that did not reach statistical significance resulted from PF+CF co-activation with 125 ms or 0 ms intervals, and no change was observed when CF stimulation preceded PF activity by 250 ms (Chen and Thompson, 1995). Similarly, Wang et al. showed that LTD is induced when PF stimulation precedes complex spike activity by 150 ms or when the two inputs are simultaneously activated, but LTD does not result from co-activation using a 500 ms interval, or a reversed activation sequence (150/500 ms interval). Interestingly, although not discussed by the authors, CF-PF stimulation with an interval of 150 ms results in a weak potentiation that lasts about 20 min (Wang et al., 2000). Safo and Regehr obtained similar results, but also noted a depression when CF activity preceded PF activity by 50 ms (Safo and Regehr, 2008). However, since more pronounced LTD was induced by PF stimulation first (50 and 150 ms), these data generally seem to confirm the observations made by the other two groups. These studies point toward an anti-Hebbian STDP mechanism in the cerebellum, similar to the anti-Hebbian STDP described in cerebellum-like structures (but note that in cerebellum-like structures the timing intervals seem to be shorter). In the light of the discrepancies in the literature, we would like to point out that in our hands 100 Hz PF burst stimulation followed after 120 ms by CF activity causes LTD (Piochon et al., 2010), which is in line with the observations by Chen and Thompson (1995) and Wang et al. (2000). At the single spine level, evoked calcium transients are largest when the PF input is activated before CF stimulation (Wang et al., 2000). This observation might explain why this temporal sequence is optimal for LTD induction, since at these cerebellar synapses LTD has a higher calcium threshold for induction than LTP (Coesmans et al., 2004).

Remarkably, such a requirement for specific temporal order and similar activation sequences can also be observed in behavioral learning. One example is the need for temporal specificity in associative eyeblink conditioning—a form of motor learning that involves the cerebellum: the optimal interval between application of a tone (conditioned stimulus; conveyed by the PF input) and an air puff application to the eye (unconditioned stimulus; conveyed by the CF input) is between 200–400 ms, thus offering a rare opportunity to relate timing intervals that were observed in *in vitro* and *in vivo* learning studies, respectively (Thompson and Krupa, 1994). In line with these results, in gain adaptation of the vestibulo-ocular reflex (VOR), another type of learning mediated by the cerebellum, CF activity needs to follow PF activity by 100 ms (Raymond and Lisberger, 1998). These examples illustrate that both LTD induction and forms of behavioral learning require PF activity prior to CF activation for the adaptive change to occur.

Cerebellar plasticity depends on the relative timing between the activation of PF synapses and the occurrence of CF-evoked complex spikes. As a result it appears that in STDP an instructive signal for the strengthening or weakening of a synapse can arise from a number of sources: a backpropagating action potential or a synaptically evoked calcium spike depending on the structure or system. However, cerebellar STDP differs from STDP at hippocampal/neocortical synapses, and also from STDP in cerebellum-like structures, in that it does not depend on the axonal spike output. In cerebellar Purkinje cells—in contrast to pyramidal cells and Purkinje-like cells—Na+ action potentials that are elicited in the axon hillhock do not actively backpropagate, but rather spread passively into the dendrites (Stuart and Häusser, 1994; Ohtsuki et al., 2012). Thus, the dendrite does not receive feedback information on whether action potentials were fired or not, which is a key component of Hebbian plasticity (but note that dendritic calcium transients can vary in amplitude depending on whether the cell is in an up or down state; Kitamura and Häusser, 2011). Rather, bidirectional PF plasticity depends on the relative timing with activity of the heterosynaptic CF input, which provides an externalized instructive signal. Interestingly, the somatically recorded complex spike does not seem to play a role in cerebellar STDP: double-patch recordings from the soma and dendrite of Purkinje cells have shown that the classic complex spike waveform only occurs in the soma, and that CF activation results in all-or-none EPSPs in the dendrites instead, which can be associated with local spike activity (Davie et al., 2008; Ohtsuki et al., 2009, 2012). As these dendritic spikes typically do not cause additional spikes near the

soma, it seems that CF activity evokes two types of spikes in the soma and dendrites that occur independently from each other. Thus, STDP at cerebellar PF to Purkinje cell synapses is anti-Hebbian with regard to the optimal temporal order of synaptic activation and the occurrence of local dendritic spikes, but is non-Hebbian with regard to the lack of action potential backpropagation and thus information on the neuron's spike firing output.

As outlined above, PF plasticity in both the cerebellum proper and in cerebellum-like structures follows an anti-Hebbian STDP rule, but in contrast to Purkinje cells in the mammalian cerebellum, Purkinje-like cells in cerebellum-like structures such as the mormyrid ELL show action potential backpropagation, which may be involved in STDP. To reconcile these observations we review plasticity mechanisms in the mormyrid cerebellum proper. Neither STDP nor action potential backpropagation have been systematically studied in cerebellar Purkinje cells of mormyrid fish. However, it has been shown that PF-LTD results from PF activation followed after 20–50 ms by CF stimulation, pointing toward an anti-Hebbian STDP mechanism (Han et al., 2007). The role of the axonal spike output remains unclear. CF activation does not result in complex spikes, but in all-or-none EPSPs. Moreover, Na+ action potentials recorded from the somata of mormyrid Purkinje cells have unusually small amplitudes, typically not exceeding 30 mV (de Ruiter et al., 2006). It seems unlikely that these reduced Na+ spikes backpropagate, although mormyrid Purkinje cells express the Na+ channel α subunits Nav1.1, Nav1.2, and Nav1.6 in their dendrites (de Ruiter et al., 2006). On the other hand, somatic depolarization or CF stimulation can evoke broad spikes that can be recorded in the somata of mormyrid Purkinje cells and are associated with dendritic calcium transients that are at least partially mediated by P/Q-type voltage-gated calcium channels (Han et al., 2007). In contrast to their mammalian counterparts, mormyrid CFs only contact very proximal parts of the dendrite ("horizontal dendrite") and do not invade the molecular layer. Thus, activation in or close to the soma can evoke calcium spikes (broad spikes) in the dendrites. The relevance of these broad spikes for plasticity is currently not understood: pairing PF activation with broad spikes evoked by CF stimulation causes LTD, but when the broad spikes are triggered by somatic injection of depolarizing currents LTP is induced instead (Han et al., 2007). Further studies are needed to determine under which conditions broad spike activity promotes LTD and LTP, respectively, and to assess the role of the axonal spike output in STDP in cerebellar Purkinje cells of mormyrid fish.

In summary, it can be concluded that a form of STDP does exist in cerebellar Purkinje cells, but that there are two important differences to STDP in pyramidal cells: (a) the optimal temporal order of synaptic activation and spike firing is reversed, so that synaptic activity followed by spike activity results in LTD rather than LTP induction, and (b) in mammalian Purkinje cells (and possibly mormyrid Purkinje cells), the instructive signal is externalized, and locally elicited calcium spikes play a key role instead. This latter difference has important functional consequences: in contrast to Hebbian plasticity, what matters is not the timing relative to the axonal spike output, but rather the timing relative to the

hippocampal and neocortical pyramidal cells. Action potentials are elicited near the soma and backpropagate into the dendrite, where the accompanying depolarization leads to calcium influx (red). The timing relative to incoming EPSPs (blue) evoked at glutamatergic synaptic inputs (green) determines whether LTP or LTD is induced. **(B)** Non-Hebbian STDP in cerebellar Purkinje cells. Here, somatic/axonal action potentials do not actively invade the dendrite. Rather, CF activation causes local dendritic calcium spikes. LTD results if PF-EPSPs precede CF activity. In hippocampal/neocortical circuits this activation sequence (synaptic activity—spike) promotes LTP instead.

activity of the CF input, a qualitatively different, heterosynaptic input (**Figure 3**).

#### **WAS DONALD HEBB WRONG?**

To answer this question, it needs to be acknowledged first that the famous Hebb postulate—as cited in the introduction—is only a small component within a larger conceptual framework that Hebb presented in his book "The Organization of Behavior." When using the terms "Hebbian" and "non-Hebbian," we thus specifically refer to the spike timing mechanism described in the Hebb postulate. Within this framework, the Hebb postulate describes a learning rule for types of neurons, in which action potential backpropagation takes place. By extension, cerebellar STDP can be described as "non-Hebbian" as Purkinje cells lack regenerative backpropagating Na+ spikes (Stuart and Häusser, 1994; Ohtsuki et al., 2012).

STDP does exist in the cerebellum, but depends on dendritic calcium spikes instead. Moreover, the temporal order of STDP found in the cerebellum and in cerebellum-like structures is opposite to that expected from an STDP mechanism that follows the Hebb rule (e.g., Bell et al., 1997; Wang et al., 2000). We argue that the Hebb rule remains a widely applicable plasticity concept, but that there are important exceptions and limitations that need to be acknowledged when generalizing.

To understand STDP rules, it is useful to take mechanistic aspects of LTP and LTD induction into consideration. Both forms of plasticity are initiated by local dendritic calcium transients, whose specific features, such as amplitude, localization, and kinetics ultimately determine the polarity of synaptic weight change (for discussion, see Bear et al., 1987; Hansel et al., 1997; Wang et al., 2000; Coesmans et al., 2004; Nevian and Sakmann, 2006). This strict dependence on calcium signaling explains why, for example, LTP in pyramidal cells can be triggered either by local spikes in the dendrites, or by action potential backpropagation: both types of spike activity are associated with local calcium influx (Markram et al., 1995; Golding et al., 2002). At more distal synaptic input locations local calcium spikes may be the most effective means to trigger LTP (Golding et al., 2002; Hardie and Spruston, 2009). However, this observation does not generally exclude a role for Hebbian STDP in plasticity, particularly at more proximal synapses. It remains to be determined which type of spike activity is most relevant under physiological conditions (see also Lisman and Spruston, 2010).

Does the existence of a very different type of STDP (non-Hebbian/reverse temporal order) in the cerebellum challenge the general importance of Hebbian-style plasticity mechanisms? Not necessarily, because cerebellar Purkinje cells have unique features that set them apart from other types of neurons. Of these features, two are particularly relevant for our discussion, because they mark significant differences to hippocampal and neocortical pyramidal cells: first, there is no action potential backpropagation into Purkinje cell dendrites (Stuart and Häusser, 1994; Ohtsuki et al., 2012). As a consequence, the dendrites receive no information on the axonal spike output. However, we suggest that the role of the backpropagating action potential is served by the instructive signal from the CF. Second, Purkinje cells spontaneously fire action potentials (simple spikes) at discharge rates in the range of ∼20–80 Hz (Häusser and Clark, 1997), whereas pyramidal cells are almost silent at rest (Margrie et al., 2002). This latter difference is important, because low firing rates allow pyramidal neurons to act as coincidence detectors (König et al., 1996). In contrast, the high firing rates found in Purkinje cells prevent these neurons from using relative spike timing as a relevant measure for processes such as STDP, because the short interval between spikes makes it difficult to distinguish between post- and pre-spike events. This notion holds for simple spikes—that are intrinsically triggered and can result from PF activity—but not for spikes evoked by CF discharges that occur at 1–2 Hz at rest (Simpson et al., 1996). Thus, it does not come as a surprise that STDP at PF synapses onto Purkinje cells is based on timing relative to CF-evoked spike activity (Chen and Thompson, 1995; Wang et al., 2000). A remarkable consequence is that STDP at these cerebellar synapses depends on the relative timing of activity at two qualitatively different, independent synaptic inputs. The externalization of the instructive signal in the cerebellum is very different from the prevalent theme of Hebbian plasticity in neocortex and hippocampus of timing relative to somatic/axonal action potential firing (**Figure 3**). In the cerebellum, this non-Hebbian form of STDP allows the CF input to assume the role of a teacher and instructor in cerebellar motor learning (Simpson et al., 1996). Cerebellar STDP seems unique in that it depends on specific features of Purkinje cell physiology and of the cerebellar microcircuit. Thus, a more general reading of Hebb's postulate

is necessary for it to be applied throughout the central nervous system.

#### **ACKNOWLEDGMENTS**

We would like to thank Curtis C. Bell and Nicolas Brunel for invaluable comments on the manuscript and members

#### **REFERENCES**

of the MacLean and Hansel laboratories for helpful discussions. The authors were supported by the DANA Foundation (Jason MacLean), a National Science Foundation CAREER Award (0952686 to Jason MacLean), and a grant from the National Institute of Neurological Disorders and Stroke (NS-062771 to Christian Hansel).

LTD via coincident activation of presynaptic NMDA and cannabinoid receptors. *Neuron* 39, 641–654.

pyramidal cell dendrites. *Nature* 367, 69–72.

by calcium release. *Nat. Neurosci.* 3, 1266–1273.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 12 September 2012; accepted: 26 December 2012; published online: 11 January 2013.*

*Citation: Piochon C, Kruskal P, MacLean J and Hansel C (2013) Non-Hebbian spike-timing-dependent plasticity in cerebellar circuits. Front. Neural Circuits 6:124. doi: 10.3389/fncir.2012.00124 Copyright © 2013 Piochon, Kruskal, MacLean and Hansel. This is an openaccess article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*

## Olivary subthreshold oscillations and burst activity revisited

#### *Paolo Bazzigaluppi 1†, Jornt R. De Gruijl 2†, Ruben S. van der Giessen1, Sara Khosrovani 1, Chris I. De Zeeuw1,2 and Marcel T. G. de Jeu1 \**

*1 Department of Neuroscience, Erasmus Medical Center, Rotterdam, Netherlands*

*2 Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam, Netherlands*

#### *Edited by:*

*Egidio D'Angelo, University of Pavia, Italy*

*Reviewed by: Leonard Maler, University of Ottawa, Canada Scott Hooper, Ohio University, USA*

#### *\*Correspondence:*

*Marcel T. G. de Jeu, Department of Neuroscience, Erasmus Medical Center, P.O. Box 2040, 3000 CA Rotterdam, Netherlands. e-mail<EMAIL_ADDRESS>
*†These authors have equally contributed to this work.*

The inferior olive (IO) 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.

**Keywords: inferior olive, subthreshold oscillations, wavelets, climbing fiber, cerebellum, gap junctions**

#### **INTRODUCTION**

The inferior olive (IO) forms the sole source of climbing fiber inputs to Purkinje cells in the cerebellar cortex (Szentágothai and Rajkovits, 1959; Desclin, 1974). Climbing fibers excite Purkinje cells in the cerebellar cortex, resulting in a powerful, all-or-none depolarization called a complex spike (Eccles et al., 1966; Thach, 1967; Ito and Simpson, 1971). Climbing fibers may fire in bursts (Crill and Kennedy, 1967; Crill, 1970; Maruta et al., 2007; Mathy et al., 2009) and thereby they can modify the complex spike (Mathy et al., 2009). These climbing fiber bursts are generated at the axon hillock of olivary cells and they backpropagate to the soma where they give rise to small wavelets (Mathy et al., 2009).

IO neurons have two intrinsic properties that play an important role in their firing behavior: IO neurons generate subthreshold oscillations (Llinás and Yarom, 1986; Khosrovani et al., 2007) and they are coupled to one another via dendrodendritic gap junctions (Llinás et al., 1974; Sotelo et al., 1974; Khosrovani et al., 2007; Van Der Giessen et al., 2008). The subthreshold oscillations may serve as a timekeeping device, whereas the gap junctions (i.e., connexin 36) may be necessary to form functional ensembles of olivary cells (Llinás et al., 1974; Lang et al., 1996; De Zeeuw et al., 1998).

Recently, Mathy et al. (2009) have demonstrated that the burst activity of climbing fibers conveys information about the timing of the spike relative to the phase of the olivary subthreshold oscillations. The timing of the olivary activity was encoded by the number of spikes in the olivary axonal burst (i.e., climbing fiber burst). Furthermore, they showed that this climbing fiber burst (timing information) affected downstream Purkinje cells by altering the strength of the synaptic transmission between parallel fibers and Purkinje cells. Their results challenge current views concerning the role of climbing fibers in motor control, and they might reconcile the opposing theories established for motor timing and motor learning (Simpson et al., 1996; Mauk et al., 2000). However, an important part of their dataset was collected using whole-cell patch-clamp recordings from *ex vivo* slice preparations of the IO. Their *ex vivo* slice preparation had two major disadvantages; the olivary cells were isolated from their olivocerebellar module [which alters their electrophysiological behavior (Chorev et al., 2007; Khosrovani et al., 2007)] and the oscillations were artificially imposed to the neurons by injecting a fixed-amplitude sinusoidal current. Consequently, the finding that the climbing fiber signal encodes the temporal state of the olivary network could be due to the altered physiological condition of the IO neurons.

**Abbreviations:** IO, inferior olive; Cx36, connexin 36; C57BL/6 mice, C57 black 6 mice; WT, wild type; KX, ketamine and xylazine; MMF, medetomidine, midazolam, and fentanyl; SSTO, sinusoidal subthreshold oscillation; LTO, low-threshold calcium (Ca2+) oscillation; ADP, afterdepolarization; SEM, standard error of the mean.

In the present study, we investigated whether this timing code was also generated in olivary cells present in an intact olivocerebellar system using the *in vivo* whole-cell recording technique. *In vivo* recordings were obtained under two different anaesthetic conditions to exclude drug-specific effects. Comparisons between spontaneous and somatosensory-evoked action potentials were made to elucidate origin-related differences. And we studied the consequences of genetically (connexin 36 knock-out mice: Cx36−*/*−) as well as pharmacologically manipulated (Harmaline) subthreshold oscillations on these olivary bursts.

### **MATERIALS AND METHODS**

#### *In vivo* **WHOLE-CELL RECORDINGS**

C57 black 6 mice (C57BL/6 mice) were anesthetized by an intraperitoneal injection of a mixture of ketamine and xylazine (KX; 65 and 10 mg/kg; *n* = 40), or a mixture of medetomidine, midazolam, and fentanyl (MMF; 0.5 mg/kg, 5 mg/kg, and 0.05 mg/kg; *n* = 6). All Cx36−*/*− mutants (*n* = 9) were anesthetized with KX. *In vivo* whole-cell recordings were performed as described by Khosrovani et al. (2007). In a subset of KX anesthetized animals (*n* = 4), peripheral stimulations were provided by electrical stimulation of the whisker pad to generate somatosensory evoked action potentials in the recorded olivary neuron. The stimulation protocol consisted of short bipolar stimulations (2 ms, 0.5 mA) that were randomly administered. In a second subset of KX anesthetized animals (*n* = 6), harmaline (50 mg/kg) was injected intraperitoneally after all baseline recordings were established. In each neuron, the following membrane properties were determined: input resistance, membrane capacitance (Cm), resting membrane potential, and firing rate. The access resistance (Ra), membrane resistance (Rm), and Cm were calculated using the following formulas: Ra = *V-/*Ii; Rm = *(*V*-* − RaIss*) /*Iss and Cm = τ *(*1*/*Ra + 1*/*Rm*)*. Tau (τ), instantaneous (Ii), and steady state currents (Iss) were determined from current responses evoked by −10 mV steps. The resting membrane potential (Vm) was determined from the readout of the baseline potential. All animal procedures were in accordance with the guidelines of the ethics committee of the Erasmus Medical Center.

#### **DATA ANALYSIS**

Data analyses were performed for neurons with resting membrane potentials lower than −45 mV, typical olivary spike waveforms (i.e., expressing spike afterdepolarizations with at least one wavelet), and spike amplitudes *>*60 mV (number of neurons: *n*KX = 37, *n*MMF = 8, *n*Cx36−*/*− = 10). Subthreshold oscillations in the spontaneous membrane potential were quantified by measuring the frequency and amplitude of the oscillations. In neurons that expressed sinusoidal subthreshold oscillations (SSTOs), the phase spiking preference was determined by fitting the SSTO prior to the spontaneous action potential to a sine wave function. The fitted curve was extrapolated following the action potential, and spike occurrence was determined within phase bins of 45◦ (**Figure 1**). In neurons that expressed low-threshold calcium (Ca2+) oscillations (LTOs), the spike position was determined as either on top of the low-threshold Ca2+ spike or between them (**Figure 8A**). The number of wavelets on a typical olivary spike afterdepolarization (ADP) was counted for each spike.

#### **STATISTICAL ANALYSIS**

Statistical analysis of basic membrane properties was performed using Mann–Whitney *U-*test. The spiking preference in relation to the phase of the SSTO oscillation was examined using the Rayleigh test, and the shifts in the phase–frequency distributions under different conditions were compared using the Student's *t*-test. Modulations of the number of wavelets were tested by comparing the number of wavelets in each phase bin with the overall average number of wavelets using the one-sample Student's *t*-test. For bimodal analysis of the spiking preference in neurons expressing LTOs, the χ2 test was used. Modulations of the number of wavelets in these LTO neurons were analyzed statistically using the Student's *t*-test. Student's *t*-tests were also employed to test the significance of the correlation coefficients. For statistical comparisons of the linear regression lines, we used the analysis of covariance (ANCOVA) followed by a *post hoc* Tukey's HSD analysis (Matlab, The Mathworks, Natick, MA). All of the numerical values presented in the text represent the mean ± SEM.

#### **RESULTS**

#### **THE GENERATION OF OLIVARY WAVELETS** *In vivo* **DOES NOT DEPEND ON THE PHASE OF SINUSOIDAL SUBTHRESHOLD OSCILLATIONS**

*In vivo* whole-cell patch-clamp recordings were obtained from neurons of the mouse IO. These recordings were collected under two different anaesthetic conditions induced by either KX or MMF to exclude anaesthesia-specific effects. Olivary neurons that were recorded under KX and MMF conditions revealed similar basic membrane properties (**Table 1**), and these properties were comparable to those obtained in previous *ex vivo* measurements (Llinás and Yarom, 1981; Long et al., 2002; De Zeeuw et al., 2003; Leznik and Llinas, 2005). However, the input resistance was slightly, but not significantly, higher under MMF conditions.

We have previously shown that *in vivo* olivary neurons can exhibit two different types of subthreshold oscillations: typical rhythmic 3–9 Hz SSTOs, or rhythmic 1–3 Hz LTOs (Khosrovani et al., 2007). Spontaneous action potentials in olivary cells that exhibited SSTOs depend on the peak phase of the oscillation under both anaesthetic conditions (both *r* = 0*.*71, both *p <* 0*.*05), but the phase-frequency distribution of spikes under MMF was slightly shifted compared to that under the KX condition (**Figures 2A** and **3A**; KX: 98 ± 5◦, MMF: 119 ± 7◦; *p <* 0*.*05). To find out whether the phase of the subthreshold oscillations determines the number of wavelets in an olivary spike *in vivo*, we counted the number of wavelets that were superimposed on the spike ADP (**Figure 1B**; see arrows) and examined their dependency on the phase and amplitude of the subthreshold oscillation. On average, olivary spikes expressed 2*.*2 ± 0*.*1 wavelets (*n* = 155 spikes) under KX anaesthesia and 2*.*5 ± 0*.*2 wavelets (*n* = 73 spikes) under MMF anaesthesia. These values were not significantly different (*p* = 0*.*08) and were comparable to those measured *ex vivo* (Mathy et al., 2009). The timing of the spike in relation to the phase of the SSTO did not

**wavelets of an olivary neuron. (A)** Left panel: trace of a spontaneous sinusoidal subthreshold oscillation (SSTO) from an olivary neuron *in vivo*. The gray trace indicates the sinusoidal fit of the SSTO prior to the occurrence of

action potential. **(B)** Right panel: enlargement of the olivary action potential shown in the left panel. The arrows indicate the olivary wavelets on top of the afterdepolarization (ADP).

*Statistical analyses of basic membrane properties were performed using the Mann–Whitney U-test.*

*\*Cx36*−*/*− *mutant mice were anaesthetized using the KX mixture.*

*Rm, membrane resistance; Cm, membrane capacitance; Vm, membrane potential; f, firing frequency. All numerical values indicate the median (25–75th percentile).*

determine the number of olivary wavelets in either of the two conditions (**Figures 2B** and **3B**; all *p >* 0*.*05). Inspection of the data of each neuron separately also did not reveal any correlation between the timing of the spike and the number of olivary wavelets. Thus, across the phase bins in which spikes occurred, there was no clear dependence of wavelet number on oscillation phase in our *in vivo* preparation. Alternatively, the amplitude of oscillation might be able to impose a modulatory effect on the number of wavelets. Therefore, we correlated the number of wavelets with the amplitude of the oscillation. This analysis was performed using data collected under both types of anaesthesia (**Figures 2C** and **3C**). Significant correlations between the amplitude of the SSTO and the number of wavelets were detected for both anaesthetic conditions (**Figures 2C** and **3C**, all phase-bins together, *r*KX = −0*.*36, *r*MMF = −0*.*58, both *p <* 0*.*01). The overall correlations revealed a negative relationship between the amplitude of the oscillation and the number of wavelets. Furthermore, the phase subset plots of **Figures 2C** and **3C** show that there is no number of wavelet preferences between the different phase-bins.

#### **SPONTANEOUS vs. SOMATOSENSORY-EVOKED ACTION POTENTIALS**

Up to now, all the analyses were performed on spontaneous action potentials. Spike triggering inputs of olivary neurons can activate a subset of cellular responses that might alter the relationship between SSTO, spikes and wavelets. Therefore, we also investigated the relationship between SSTO and wavelets in somatosensory-evoked action potentials. Although strong stimuli were applied randomly to the mouse whisker pad, somatosensory-evoked action potentials in olivary cells that exhibited SSTOs were more easily generated at the peak of the oscillation, indicating also under this condition a clear spiking preference (**Figure 4A**, *r* = 0*.*57, *p <* 0*.*05, *n* = 78). The phase-frequency distribution of somatosensory-evoked spikes was slightly shifted compared to the spontaneous spikes under the KX condition (**Figures 2A** and **4A**; spontaneous: 98 ± 5◦ and evoked: 119 ± 8◦; *p <* 0*.*05). On average, the evoked olivary spikes expressed 1*.*4 ± 0*.*1 wavelets (*n* = 78 spikes), which is significantly smaller than the amount expressed on spontaneous spikes (2*.*2 ± 0*.*1 wavelets; *p <* 0*.*05). However, it is important to note that our somatosensory-evoked action potential group contain more recordings of cells with bigger SSTO amplitudes (see below). The timing of the evoked spike in relation to the phase of the SSTO did not determine the number of olivary wavelets (**Figure 4B**; all *p >* 0*.*05); indicating that the phase of the oscillation did not modulate the number of olivary wavelets on these spikes either. The amplitude of the oscillation and number of wavelets of these evoked spikes were significantly correlated to each other (**Figure 4C**, all phase-bins together, *r*SSS = −0*.*49, *p <* 0*.*01), indicating also an inverse relationship

## **FIGURE 2 | Burst size of olivary wavelets and SSTOs under KX anaesthesia. (A)** The occurrence of spontaneous spikes (spike probability) in

relation to the phase of the SSTO obtained from olivary neurons recorded under KX anaesthesia (*n* = 155). **(B)** Average number of wavelets on the ADP of spontaneous spikes in relation to the phase of the SSTO obtained from olivary neurons recorded under KX anaesthesia (*n* = 155). **(C)** Relationship

between number of wavelets and amplitude of SSTO were plotted for each phase-bin. The center graph shows the correlation between the amplitude of the SSTOs and the number of wavelets of all data (all phase-bins together) measured under KX anaesthesia. Least squares linear regression lines and correlation coefficients were computed [*r*KX = −0*.*36, *n* = 155, and *p <* 0*.*01 (*t*-test)].

**FIGURE 3 | Burst size of olivary wavelets and SSTOs under MMF anaesthesia. (A)** The occurrence of spontaneous spikes (spike probability) in relation to the phase of the SSTO obtained from olivary neurons recorded under MMF anaesthesia (*n* = 73). **(B)** Average number of wavelets on the ADP of spontaneous spikes in relation to the phase of the SSTO obtained from olivary neurons recorded under MMF anaesthesia (*n* = 73).

**(C)** Relationship between number of wavelets and amplitude of SSTO were plotted for each phase-bin. The center graph shows the correlation between the amplitude of the SSTOs and the number of wavelets of all data (all phase-bins together) measured under MMF anaesthesia. Least squares linear regression lines and correlation coefficients were computed [*r*MMF = −0.58, *n* = 73, and *p <* 0*.*01 (*t*-test)].

#### **FIGURE 4 | Burst size of olivary wavelets and SSTOs of**

**somatosensory-evoked spikes. (A)** The occurrence of stimulus evoked spikes (spike probability) in relation to the phase of the SSTO obtained from olivary neurons recorded under KX anaesthesia (*n* = 78). **(B)** Average number of wavelets on the ADP of stimulus evoked spikes in relation to the phase of the SSTO obtained from olivary neurons recorded under KX anaesthesia

(*n* = 78). **(C)** Relationship between number of wavelets and amplitude of SSTO were plotted for each phase-bin. The center graph shows the correlation between the amplitude of the SSTOs and the number of wavelets of all data (all phase-bins together) measured on somatosensory evoked spikes. Least squares linear regression lines and correlation coefficients were computed [*r*SSS = −0*.*49, *n* = 78, and *p <* 0*.*01 (*t*-test)].

between the amplitude of the oscillation and the number of wavelets on somatosensory-evoked spikes. Overall, we conclude that under *in vivo* conditions, the phase of the SSTO does not regulate the number of wavelets (i.e., output) on olivary spikes, but that the number of wavelets depends on the amplitude of the SSTO. This phenomenon can be observed on both spontaneous as well as somatosensory-evoked action potentials, despite their different origin and the activation of different cellular responses.

#### **ELECTRICAL SYNAPSES**

Olivary neurons are interconnected via gap junctions formed by connexin 36 (Cx36). The lack of Cx36 leads to an absence of electrotonic coupling, to a more voltage-dependent SSTO, to an increased excitability at hyperpolarizing states and to an altered interaction between SSTO and the generation of an action potential (Long et al., 2002; De Zeeuw et al., 2003; Van Der Giessen et al., 2008). Therefore, Cx36−*/*− mutant mice provide a condition in which the relationship between the generation of action potentials and the phase of the oscillation is weakened, which might give the opportunity to unmask possible phase-related modulatory effects on olivary burst firing. *In vivo* whole-cell recordings from the SSTO neurons of Cx36−*/*− mutants anesthetized with KX revealed a reduction in the spiking preference in relation to the phase of the oscillation (**Figure 5A**, *r* = 0*.*56, *p <* 0*.*05). Also the phase-frequency distribution of spikes from olivary neurons in the Cx36−*/*− mutants was shifted compared to spikes recorded from olivary neurons in wild type (WT) mice under KX anaesthetic conditions (**Figures 2A** and **5A**; WT: 98 ± 5◦, Cx36−*/*−: 123 ± 8◦; *p <* 0*.*05). On average, the olivary spikes of Cx36−*/*− mutants expressed 2*.*3 ± 0*.*1 wavelets (*n* = 83 spikes) under KX anaesthesia. This value was not significantly different compared to the value obtained from the olivary neurons of WT mice under either KX or MMF anaesthesia (*p* = 0*.*38 and *p* = 0*.*30, respectively). In the recordings from mutant mice, we did not detect a significant modulation of the number of wavelets in relation to the phase of the oscillation (**Figure 5B**, all *p >* 0*.*05). Thus, the phase of the SSTO did also not modulate the number of olivary wavelets in our *in vivo* Cx36−*/*− preparations exhibiting a weakened relationship between the oscillation phase and the spiking preference. However, different from all our previous results, the amplitude of the oscillation and number of olivary spike wavelets were not significantly correlated in Cx36−*/*− mutants (**Figure 5C**, *r*CX36 = −0*.*20, *p >* 0*.*05). Therefore, we conclude that under *in vivo* conditions, gap junction coupling via Cx36 may be involved in the modulatory effect of the amplitude of oscillation.

In all four conditions (KX anesthesia, MMF anesthesia, somatosensory-evoked action potentials and Cx36−*/*−), the majority of the action potentials occur during the peak of the oscillation (∼45–135◦). In order to analyze the impact of the oscillation amplitude on the number of wavelets, we selected and pooled the action potentials of the 45–90◦ and 90– 135◦ phase subsets for further analysis and cross-conditional comparisons. Consequently, we removed any putative confounding factors of other phase-subsets. Also under these new constraints, the amplitude of oscillation and number of wavelets were significantly correlated to each other in three out of four conditions (**Figure 6**; *r*KX = −0*.*27, *r*MMF = −0*.*67, *r*SSS = −0*.*55, all *p <* 0*.*01; *r*CX36 = −0*.*21, *p >* 0*.*05), confirming the amplitude dependency under all circumstances except in animals lacking electrical coupling. Cross-conditional comparisons of the regression lines revealed a significant difference in amplitude dependency between these groups (*p <* 0*.*01, ANCOVA). Further analyses revealed that the linear regression line made from the MMF data (the slope as well as the intercept) was significantly different from the linear regression lines of the other conditions (all *p <* 0*.*05, Tukey's HSD test).

#### **HARMALINE**

So far, this correlation was obtained by combining the results from many cells. This was necessary because under our *in vivo* conditions, the variability of the oscillation amplitude within a single cell is limited. In order to investigate the causality of this relationship further, we manipulated the amplitude of the oscillations by injecting mice with harmaline (50 mg/kg) during the recordings and correlated the number of wavelets with the manipulated amplitude of the oscillation of a single cell. In all recorded cells (*n* = 6), harmaline increased the amplitude of the oscillation but did not affect the firing rate (**Figure 7A**). Single cell analysis of data collected from harmaline-injected WT animals (*n* = 3) revealed also significant correlations between the amplitude of the SSTO and the number of wavelets (**Figure 7B**, all three WTs *p <* 0*.*01), confirming the inverse relationship between the amplitude of the oscillation and the number of wavelets but now demonstrated within single cells. The three cells that were collected from Cx36−*/*− mutants did not show any significant correlations (**Figure 7B**, all three Cx36−*/*− *p >* 0*.*05), which also agrees with our results mentioned previously. Thus, manipulating the amplitude of the oscillation from small to large reduced the probability of expressing wavelets (i.e., a smaller burst in olivary axons/climbing fibers), and this causal relationship was not observed in olivary neurons lacking Cx36 gap junctions.

#### **LOW-THRESHOLD Ca2+ DEPOLARIZATIONS AFFECT THE GENERATION OF OLIVARY WAVELETS**

In addition to neurons that exhibit SSTO, the IO also contains neurons that express rhythmic 1–3 Hz LTOs. Because this type of oscillation was never observed in mice that were anesthetized with MMF, the following analyses were only conducted for olivary neurons recorded from mice that were anesthetized by KX. Spontaneous action potentials in olivary neurons that exhibited LTOs revealed a significant preference for spiking on top of the low-threshold Ca2+ depolarizations (**Figures 8A,B**, *p <* 0*.*05). Because the waveform of the LTO cannot be fitted correctly to a sine wave, it is impossible to determine the oscillation phase and subsequently the phase-spiking relationship. Therefore, wavelet analysis was performed using a simpler bimodal approach. The number of wavelets was counted on the ADP of spontaneous olivary spikes and grouped for the spikes that were on top of and in between the low-threshold Ca2+ depolarizations. On average,

**FIGURE 5 | Burst size of olivary wavelets and SSTOs in Cx36−***/***− mutants. (A)** The occurrence of spontaneous spikes (spike probability) in relation to the phase of the SSTO obtained from olivary neurons recorded in Cx36−*/*− mutants (*n* = 83). **(B)** Average number of wavelets on the ADP of spontaneous spikes in relation to the phase of the SSTO obtained from olivary neurons recorded in Cx36−*/*− mutants (*n* = 83). **(C)** Relationship

between number of wavelets and amplitude of SSTO were plotted for each phase-bin. The center graph shows the correlation between the amplitude of the SSTOs and the number of wavelets of all data (all phase-bins together) measured in Cx36−*/*− mutant mice. Least squares linear regression lines and correlation coefficients were computed [*r*CX36 = −0*.*20, *n* = 83, and *p >* 0*.*05 (*t*-test)].

**(45–135◦ ) of the SSTOs.** Relationships between number of wavelets and amplitude of SSTO were plotted of spikes that occur during the peak phase (45–135◦ ) of the SSTOs. Least squares linear regression lines and correlation coefficients were computed for all four conditions (KX anesthesia, MMF anesthesia, somatosensory-evoked action potentials and Cx36−*/*−). Amplitude of the oscillation and number of olivary spike wavelets are

ing and the number of olivary wavelets was relatively crude. Therefore, we correlated the number of wavelets with the size of the low-threshold Ca2+ depolarization. A significant correlation between the size of the low-threshold Ca2+ depolarization and the number of wavelets was found (**Figure 8D**, *p <* 0*.*01). These correlations revealed a negative relationship between the size of the low-threshold Ca2+ depolarization and the number of wavelets. In the Cx36−*/*− mutants, no correlation was detected (**Figure 8E**, both *p >* 0*.*05). However, the size and number of the low-threshold Ca2+ depolarizations were limited in this Cx36−*/*− mutant study (**Figure 8E**).

somatosensory evoked spikes [**C**; *r*SSS = −0*.*55, *n* = 44 (56%), *p <* 0*.*01 (*t*-test)] and are not significantly correlated when measured in Cx36−*/*− mutant mice [**D**; *r*CX36 = −0*.*21, *n* = 53 (64%), *p >* 0*.*05 (*t*-test)].

Overall, the size of the olivary axonal burst can be regulated, but it depends on the amplitude of subthreshold oscillation or size of the low-threshold Ca2+ depolarization that is expressed by the olivary neuron. Furthermore, gap junction coupling via Cx36 may be involved in this process.

## **DISCUSSION**

We demonstrated that the phase of the olivary SSTO did not regulate the number of wavelets in olivary bursts under *in vivo* conditions. Instead, we found that the number of wavelets in olivary bursts correlated with the amplitude of the olivary subthreshold oscillation. In addition, this property was not observed in olivary neurons that lacked Cx36 gap junctions. These findings change the current view regarding the nature of the information that is transmitted from the IO to Purkinje cells.

## **NUMBER OF WAVELETS: MODULATION BY PHASE OR AMPLITUDE?**

Our electrophysiological recordings confirm that olivary neurons can generate wavelets under *in vivo* conditions, which was first reported by Crill (1970). The average number of olivary wavelets observed in the present study is similar to previously reported results obtained both *in vivo* and *ex vivo*

(Crill, 1970; Mathy et al., 2009). We found that the timing of the spike in relation to the phase of these spontaneous subthreshold oscillations did not determine the number of olivary wavelets in our *in vivo* preparation. It is, though, important to note that in the trough of the oscillation phase bins are present in which no spikes occurred and therefore no wavelets were generated. This scenario is substantially different from a spike that generates no wavelets, because in the first case there is an absence of signal whereas in the latter case the number of wavelets is completely down modulated. This result is inconsistent with the *ex vivo* findings of Mathy et al. (2009). This opposite result might be due to differences in the experimental design. First, Mathy et al. (2009) injected an artificial fixed-amplitude sinusoidal current into the soma to compensate for the lack of spontaneous subthreshold oscillations in their *ex vivo* preparation. Such an induced subthreshold oscillation does not mimic the spontaneous subthreshold oscillation generated by a rhythmic ensemble of dendritic high-threshold and somatic low-threshold Ca2+ conductances together with a Ca2+-dependent potassium conductance and an H-conductance (Llinás and Yarom, 1986). Second, the IO is isolated in the slice preparation. Removal of the IO from the olivocerebellar loop changes the physiology of olivary cells (Chorev et al., 2007; Khosrovani et al., 2007; Bracha et al., 2009). The observed phase modulation in olivary cells could be due to one or both of these altered physiological conditions. The potential drawback of the *in vivo* preparation is the use and influence of anaesthetics. To address this issue, we used two different kinds of anaesthetics: KX and MMF. Neither condition revealed a phase modulation of the number of wavelets across the phase bins in which spikes occurred. Despite their different influences on a variety of membrane properties, the effects on the phase modulation of the number of wavelets were unambiguous. This result indicates that the type of anaesthetic used does not affect this process. Furthermore, similar results were obtained by analyzing somatosensory-evoked action potentials, suggesting no additional effects by cellular responses related to input processing.

In the present study, however, we provide direct evidence that the number of olivary wavelets is related to the oscillation amplitude. Interestingly, oscillations with small amplitudes showed larger numbers of olivary wavelets compared to oscillations with large amplitudes. To our knowledge, this is the first study to report that the amplitude of the subthreshold oscillation can modulate wavelet number in olivary cells. Both anaesthetics (KX and MMF) revealed an amplitude dependency of the number of wavelets, but the dependency on amplitude was also significantly stronger under MMF than under KX conditions, indicating a possible role for NMDA and/or GABA receptors in this process. The existence of this relationship was confirmed by using Harmaline to manipulate the amplitude of the subthreshold oscillations. The drug is known to cause an 8–14 Hz tremor in mice (Wang and Fowler, 2001) and it is assumed to act by modulating the rhythm-generating ionic currents of IO cells (Llinás and Yarom, 1986; Choi et al., 2010; Park et al., 2010). In our experimental set-up Harmaline caused a prominent increase in the amplitude of the oscillations, without affecting the frequency of the oscillations and the firing frequency. The data collected in these experiments demonstrate the causality of the inverse relationship between oscillations amplitude and number of wavelets, and agrees with the results obtained during physiological oscillations. We also demonstrated that this inverse relationship was not present in olivary neurons that lacked Cx36 gap junction coupling to other olivary neurons. Although gap junction coupling is not necessary for the generation and maintenance of olivary oscillations, it can clearly affect the oscillatory properties of IO cells (Long et al., 2002; De Zeeuw et al., 2003; Leznik and Llinas, 2005). However, it is not yet clear how (mechanistically) the gap junctions are involved in the amplitude-dependent modulation of the wavelets.

#### **FUNCTIONAL RELEVANCE OF CLIMBING FIBER BURST MODULATION**

We have demonstrated that the amplitude, but not the phase, of olivary oscillations modulates the climbing fiber bursts. This mechanism allows olivary axons to convey information about the amplitude of the olivary subthreshold oscillation to Purkinje cells. In the present results, the olivary axonal bursts are smaller when the spike is evoked on a subthreshold oscillation with large amplitude than when it is evoked on a subthreshold oscillation with small amplitude. Studies of Gellman et al. (1985) and Andersson and Armstrong (1987) revealed that the IO can function as an "unexpected" event detector; complex spikes were induced in animals that received an "unexpected" perturbation during their movement. We hypothesize that from the movement command an expected sensory profile is generated. The IO compares this expected sensory profile with the achieved sensory profile that is generated during the movement. An unexpected perturbation induces a mismatch between these two profiles and consequently the IO cells generate a burst of action potentials. We suggest

#### **REFERENCES**

in inferior olive neurons are dynamically regulated by P/Q- and T-type calcium channels: a study in mutant mice. *J. Physiol.* 588, 3031–3043.

that this expected sensory profile is encoded in the amplitude of the subthreshold oscillations in that a high level of "expectation" is reflected in high amplitude of the oscillation. Or in other words, low-amplitude oscillations make the olivary neurons more likely to respond strongly to signals with a high teaching level. Subsequently, olivary bursts are relayed to Purkinje cells, where they can modify the synaptic transmission between parallel fibers and Purkinje cells (Mathy et al., 2009) and adjust motor performance. According to this theory, the inability of olivary neurons without Cx36 to modulate the axonal bursts should, in Cx36 mutant mice, result in impairment in discriminating between the occurrences of expected and unexpected events during movements. Both locomotion and eye-blink conditioning experiments in Cx36−*/*− mutant mice revealed that these mice were indeed not able to correctly convert the associated conditional tone into an expected sensory event (Van Der Giessen et al., 2008), which is in line with our hypothesis.

Thus, the amplitudes of olivary subthreshold oscillations might provide a mechanism to grade the expectancy of an event. This information is conveyed by olivary axons to Purkinje cells and is encoded by the size of the burst. Because amplitude modulation of the olivary oscillations in Cx36−*/*− neurons cannot regulate the burst size, we conclude that the Cx36 gap junction coupling of olive cells is necessary for this mechanism.

#### **AUTHOR CONTRIBUTIONS**

All experiments were performed at the Erasmus MC. Paolo Bazzigaluppi, Jornt R. De Gruijl, and Marcel T. G. de Jeu designed the experiments, Ruben S. van der Giessen, Sara Khosrovani, and Paolo Bazzigaluppi performed the experiments. Paolo Bazzigaluppi, Jornt R. De Gruijl, and Marcel T. G. de Jeu analysed the experiments, Paolo Bazzigaluppi, Jornt R. De Gruijl, Chris I. De Zeeuw, and Marcel T. G. de Jeu wrote the manuscript. All authors approved the final version.

#### **ACKNOWLEDGMENTS**

We thank Dr. T. Ruigrok for carefully reading the manuscript and for providing helpful suggestions. This work was supported by The Netherlands Organization for Scientific research—ZonMw; grant 917.96.347 (Marcel T. G. de Jeu).

as the major source of cerebellar climbing fibers in the rat. *Brain Res.* 77, 365–384.

their pharmacological modulation: an *in vitro* study. *J. Physiol.* 376, 163–182.

*Psychopharmacology (Berl.)* 158, 273–280.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 31 August 2012; accepted: 02 November 2012; published online: 22 November 2012.*

*Citation: Bazzigaluppi P, De Gruijl JR, van der Giessen RS, Khosrovani S, De Zeeuw CI and de Jeu MTG (2012) Olivary subthreshold oscillations and burst activity revisited. Front. Neural Circuits 6:91. doi: 10.3389/fncir. 2012.00091*

*Copyright © 2012 Bazzigaluppi, De Gruijl, van der Giessen, Khosrovani, De Zeeuw and de Jeu. This is an openaccess article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*

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

## *Laurens Witter 1†, Cathrin B. Canto1†, Tycho M. Hoogland1, Jornt R. de Gruijl 1 and Chris I. De Zeeuw1,2\**

*1 Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam, Netherlands 2 Department of Neuroscience, Erasmus Medical Center, Rotterdam, Netherlands*

#### *Edited by:*

*Egidio D'Angelo, University of Pavia, Italy*

#### *Reviewed by:*

*Deborah Baro, Georgia State University, USA Sergio Solinas, University of Pavia, Italy*

#### *\*Correspondence:*

*Chris I. De Zeeuw, Cerebellar Coordination and Cognition Group, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, NL-1105 BA Amsterdam, Netherlands e-mail<EMAIL_ADDRESS>†These authors have contributed equally to this work.*

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.

**Keywords: olivo-cerebellar network, Purkinje cells, rebound, cerebellar nuclei, motor control**

### **INTRODUCTION**

The cerebellum integrates sensory and motor information to learn and refine the timing of motor performance. Sensory and motor information enters the cerebellar cortex via climbing fibers that originate in the inferior olive (IO) and via mossy fibers that originate in a variety of precerebellar sources (Ito, 1984). Climbing fibers synapse onto Purkinje cells (PCs) in rostrocaudally oriented cerebellar cortical zones (Ozden et al., 2009) and generate complex spikes (CSs) (Eccles et al., 1964). There is a one-to-one relation between IO neuron firing and the occurrence of a CS in the target PC (Eccles et al., 1966). Apart from CSs, which occur at a relatively low rate of about 1 Hz at rest and up to 5–8 Hz during optimal stimulation (Llinas and Volkind, 1973; Llinas and Yarom, 1986; Llinas and Sasaki, 1989; Sasaki et al., 1989; Lang et al., 1999), PCs fire simple spikes (SSs) at 50–100 Hz (Latham and Paul, 1971). SSs are intrinsically driven in PC cell bodies by resurgent sodium currents (Raman and Bean, 1997; Afshari et al., 2004; Aman and Raman, 2007) and are modulated by excitatory and inhibitory inputs from the mossy fiber—parallel fiber pathway and molecular layer interneurons (MLIs), respectively (Jacobson et al., 2008; Oldfield et al., 2010). The activity of MLIs, the axons of which target PCs within an individual sagittal zone, can be influenced by both parallel fibers and climbing fibers (Ekerot and Jorntell, 2001; Jorntell and Ekerot, 2002, 2003; Szapiro and Barbour, 2007; Bosman et al., 2010; Mathews et al., 2012; Badura et al., 2013). Ultimately, information from the zones of PCs is processed by cerebellar nuclei neurons (CNs) (Palay and Chan-Palay, 1974; Palkovits et al., 1977), which can inhibit the

IO (De Zeeuw et al., 1988; Angaut and Sotelo, 1989; Ruigrok and Voogd, 1990; Fredette and Mugnaini, 1991) or provide an excitatory projection to a variety of premotor targets in the brainstem or thalamus (Bentivoglio and Kuypers, 1982; Voogd and Ruigrok, 1997; Garwicz, 2000).

Given the central hub position of the PC—CN projection, it is key to understand how PCs and CNs encode their information and how their activities integrate to control motor behavior (Aizenman and Linden, 1999; Alvina et al., 2008; Hoebeek et al., 2010; Bengtsson et al., 2011; De Zeeuw et al., 2011; Witter et al., 2011a; Person and Raman, 2012a,b). One of the main questions is to what extent behaviorally relevant information is transferred by individual PCs through rate coding or by synchronously timed activity and silent periods in ensembles of PCs (Bell and Grimm, 1969; Sjolund et al., 1977; Sasaki et al., 1989; Welsh et al., 1995; Levin et al., 2006; Walter et al., 2006; Heck et al., 2007; Catz et al., 2008; de Solages et al., 2008; Ozden et al., 2009; Schultz et al., 2009; Wise et al., 2010; Person and Raman, 2012a,b). Since PC axons are, like climbing fibers, organized in sagittal zones enabling ensembles of PCs to innervate a specific set of CNs, it is conceivable that PCs employ this modular organization to direct CN activity. A potential mechanistic target for such modulation is rebound activity in CN neurons, which is characterized by an elevated firing frequency following release from PC inhibition and which may rely on concerted activation and/or silencing of PCs (Llinas and Muhlethaler, 1988; Aizenman and Linden, 1999; Molineux et al., 2006, 2008; Tadayonnejad et al., 2010; Engbers et al., 2011). Rebound activity could impact postsynaptic structures such as the thalamus, red nucleus, IO and lateral reticular formation (Teune et al., 2000), and eventually motor behavior (De Zeeuw et al., 2011). However, whether CN rebound firing can be proportionally induced by graded and timed modulation of activity in specific ensembles of PCs *in vivo* and whether such a titrating process can shape motor output accordingly has not been resolved. Investigating these questions has been hampered by the difficulty of classical electrophysiological tools to stimulate specific cell types selectively, let alone to stimulate these cells in small ensembles, and to record from CNs in the whole cell mode *in vivo*. Here we used a genetic approach to express the H134R variant of channelrhodopsin-2 (ChR2) specifically in PCs by crossing L7 cre (Oberdick et al., 1990) with ChR2(H134R) (Ai32line) mice (Madisen et al., 2012). We performed whole cell and extracellular recordings of PCs and CNs as well as video recordings of tail and limb movements, while stimulating ensembles of PCs with different intensities of light during precisely determined, yet variable time periods. We found that graded activation and subsequent cessation of sagittal PC ensembles *in vivo* translated into corresponding CN inhibitions and rebounds, which in turn evoked proportional muscle contractions and movements, indicating that rebound firing may orchestrate activity in premotor brain areas and thereby control muscle activity.

#### **RESULTS**

To assess network connectivity between PC ensembles and CNs at the physiological level we performed whole cell and extracellular *in vivo* recordings of PCs and CNs in genetically modified mice that expressed ChR2(H134R)-eYFP under the L7 promotor (Oberdick et al., 1990; Madisen et al., 2012). Expression of the channelrhodopsin-2/eYFP fusion protein was restricted exclusively to PCs in these mice (**Figures 1A–C**). In the cerebellar nuclei, the fusion protein was present in axons and PC terminals surrounding CNs. There was no expression in other neuronal cell types in the cerebellum or the rest of the brain.

#### **LIGHT-DRIVEN PURKINJE CELL MODULATION**

We first made whole cell current clamp and extracellular recordings from PCs *in vivo* in response to light stimulation by three blue LED lights positioned around the cerebellum of anesthetized mice (*N* = 7) (**Figure 2A**). The LEDs were controlled by a custom-made linear LED driver (**Figure 2B**), which allowed us to adjust the strength of the light in a linear fashion (see **Figure 2C** for power curve). PCs were identified by CS and SS activity and the characteristic climbing fiber pause (De Zeeuw et al., 2011). Baseline SS activity (i.e., without light stimulus) was 72 ± 19 Hz (**Figure 3A**). Enhancing the light from 10 to 100% significantly increased the SS firing frequency of PCs from 80 ± 25 Hz to 124 ± 11 Hz [cell-wise comparison: *t(*5*)* = −4*.*742, *p* = 0*.*005], while it reduced the latency of the first SS from 9*.*1 ± 5*.*8 ms to 6*.*0 ± 4*.*7 ms [all latencies: *t(*148*)* = 5*.*181, *p <* 0*.*001] (**Figures 3A–C**). Interestingly, light stimulation was also effective in increasing SS activity when the PC was in the downstate (compare **Figures 3B,C**) (Loewenstein et al., 2005; Schonewille et al., 2006; Jacobson et al., 2008). We were unable to find a direct response within the first 50 ms of light stimulation in any other cell type in the cerebellar cortex. These data demonstrate that with our stimulus device and protocol we were able to selectively activate PCs in L7-ChR2 (H134R) mice in a reliable and graded manner.

#### **GRADED PURKINJE CELL ACTIVATION TRANSLATES INTO PROPORTIONAL CEREBELLAR NUCLEI INHIBITION**

We assessed the effect of graded, transient light-driven activation of PCs on CN spiking using different stimulus intensities and frequencies in anesthetized mice. CNs were identified based on their depth measured from the pial surface (1500–2400μm), their direct response to PC stimulation and their basic electrophysiological properties (Uusisaari et al., 2007; Bengtsson et al., 2011; Witter et al., 2011b). Recordings of CNs were targeted at the interposed nucleus of the cerebellum. CN membrane resistance, capacitance, and firing frequency varied from 11.7–779.1 M*-*, 60.1–772.6 pF, and 0–138.4 Hz, respectively (*N* = 21). Despite the large differences in cell physiological parameters, we were not able to distinguish separate clusters of cells indicative of neuronal subtypes. Also, depth of the recording was not associated with any cell physiological parameter or with the occurrence of rebound firing in these CNs. In current clamp, short light activations (1–3 ms) evoked single inhibitory postsynaptic potentials (IPSPs) in CNs (*N* = 8) (**Figure 4A**). As expected due to differences in

**FIGURE 1 | PC-specific expression of ChR2(H134R)-eYFP under control of L7-pcp2. (A)** Coronal section of the cerebellum of an L7-ChR2(H134R) eYFP mice. PC and molecular layers show dense expression of the ChR2-eYFP fusion protein (eYFP: yellow). **(B)** Detail of a sagittal section of an L7-ChR2(H134R)-eYFP mouse. ChR2-eYFP protein expression was restricted to PC membranes. PC somata are indicated with arrowheads.

Note that MLIs are visible as small dark exclusions in the PC arborizations of the molecular layer. The neuronal expression of ChR2-eYFP fusion protein was found only in PCs of the cerebellum, but not in other neuronal structures in the rest of the brain. **(C)** Detail of PC axons innervating the cerebellar nuclei. CNs are marked with '∗'. Counterstain in **(A)** with DAPI (pink).

**FIGURE 2 | Graded whole field light stimulation by three blue light emitting diodes (LEDs) positioned around the cerebellum of mice controlled by a custom-built linear LED driver. (A)** Overview of the experimental set-up. Whole cell and extracellular recordings were made from Purkinje cells (PCs), cerebellar nuclei neurons (CNs), molecular layer interneurons (MLIs) and granule cells. At the same time, bilateral electroencephalogram (EEG) was recorded from motor cortex, referenced on

the right parietal cortex. **(B)** Circuit diagram of one channel of the LED driver. A 10-turn dial permitted setting of light-intensity during experiments. A TTL input can be used to trigger the light from an external source. V-Max, V-Min, and A-Lim (measuring from calibration voltage or current) are used to limit the voltage and current through the LED and to calibrate the 10-turn dial. Up to three LEDs can be connected in parallel on a single channel. **(C)** LED power is a linear function of the dial setting in the range between 20 and 75%.

**FIGURE 3 | Whole cell** *in vivo* **recordings of PCs during optogenetic stimulation. (A)** Latency to the first simple spike (SS) and the increase in firing frequency during the light stimuli at different intensities. **(B,C)** Light stimulation (465 nm, 1000 ms, denoted by the blue bars below the traces) of the cerebellum at weak (left panels) and strong (right panels) light intensities. Both in the upstate **(B)** and downstate **(C)** PCs show graded increases of SSs and CSs during stimulation. Note, ChR2 (H134R) has slow kinetics; at light-offset, the cell remains depolarized for a few milliseconds before it settles back down to a baseline state (arrow). Red lines indicate the subthreshold membrane potentials before and during the light stimulation. Asterisks indicate CSs.

connectivity with PCs and differences in stimulation intensities, these IPSPs varied in amplitude among cells (−3*.*84 ± 2*.*13 mV) and onset latency (4*.*21 ± 1*.*44 ms). Nevertheless, weaker light activation consistently induced smaller IPSP amplitudes compared to those following strong stimulations in all CNs tested. Next, in voltage clamp we held cells at potentials between −30 and −100 mV while stimulating PCs to calculate the currentvoltage relationship (IV curve) of PC input. When stimulating PCs for several tens of ms, summations of postsynaptic currents were indicative of synchronized inputs to CNs (**Figure 4B**). In most traces we were able to identify two or three summated postsynaptic currents before the inputs became less synchronized. The onset of the evoked currents occurred at 4*.*04 ± 1*.*34 ms following the stimulus, while the timing of the first and that of the second peak synaptic current after the onset of the stimulus were 7*.*08 ± 1*.*80 ms and 11*.*93 ± 3*.*38 ms, respectively (**Figure 4B**). We determined the reversal potential for the synaptic current from the peaks of both the first and second peak-current. An inward current was observed at strongly hyperpolarized potentials, while outward currents were observed at more depolarized potentials (Erev = −76*.*42 ± 8*.*66 mV, slope: 4*.*37 ± 2*.*06 pA/mV; *N* = 3) (**Figure 4C**), which is in line with previously reported characteristics of the PC to CN synapse (Llinas and Muhlethaler, 1988; Zheng and Raman, 2009; Hoebeek et al., 2010). In current clamp mode recordings, we were able to inhibit CNs in a graded fashion using different intensities of light showing that a gradually changing rate of PC firing can lead to a proportional change in CN firing (**Figures 4D,F,G**). At cessation of the light stimulus, neurons remained inhibited for a variable period depending on the strength of the light stimulus. Following a weak stimulation

**before, during, and after optogenetic activation of PCs. (A)** Brief 1 ms activation of PCs (blue bar) was sufficient to evoke IPSPs in CNs (onset is indicated with an arrow). The inset shows the average trace. **(B)** CN responses (top panel) at various holding potentials (lower panel) used to determine the reversal potential of the evoked events in CNs (stimulus in blue). The dashed numbered lines indicate the peaks of the first (1) and second (2) induced postsynaptic event, respectively. **(C)** IV curves of all CN neurons tested (individual CN neurons are indicated with different colors, squares indicate responses to the first peak, triangles to the second). The reversal potential (Erev = −76*.*42 ± 8*.*66 mV) is in agreement with a GABA*A*-mediated current. **(D)** Graded PC stimulation (blue bar) [ranging

Black lines indicate raw data, red lines Gaussian convolved traces. **(E)** Histograms of the latency of the first spike in CNs after light-driven PC mediated inhibition. The distribution shows a long tail for both weak and strong stimulation intensities. **(F)** The normalized firing rate (firing rate during stimulation or rebound divided by the prestimulus firing rate) of the CN shown in **(D)** during light stimulus (blue), 100 ms post light stimulus (green dots), or 200 ms post light stimulus (orange dots). **(G)** Summary as in **(F)**, but for all cells. Different neurons are color coded over the three panels. **(H)** The maximal stimulation intensities from the panels in **(F)** are plotted to show that inhibition during the stimulation is necessary for rebound to occur.

of 1000 ms the latency to the first spike (time from stimulation offset to first spike) ranged from 1.62 to 448 ms with an average of 41*.*45 ± 80*.*85 ms, whereas following a strong stimulation of the same duration it varied from 0.52 to 91.47 ms with an average of 19*.*37 ± 20*.*70 ms (*N* = 10, **Figure 4E**). Thus, the time to onset is shorter [*t(*243*.*287*)* = 3*.*621, *p <* 0*.*001] with a smaller variance [Levene's test: *F(*61*.*36*)*, *p <* 0*.*001] for strong stimulation indicating that release from strong synchronous PC inhibition leads to more precisely timed CN firing compared to weak PC-mediated inhibition.

#### **REBOUND FIRING IN CEREBELLAR NUCLEI NEURONS FOLLOWS TIMED OFFSET OF PURKINJE CELLS**

In most CNs (10 out of 13) optogenetically-induced inhibition was followed by a rebound wave of excitation, which lasted up to tens of milliseconds. We did not observe a relation with the occurrence or the strength of rebound firing and cell physiological parameters such as membrane resistance, nor with the recording location. Rebound excitation was often biphasic with an initial excitation followed by inhibition and a second excitation (**Figure 4D**). The timing of the peak excitation as well as the first inhibition and second excitation [as determined by convolving the spike train with a Gaussian of width σ = 1 ms, see Materials and methods, (Hoebeek et al., 2010)] after either 500 or 1000 ms of PC stimulation were not significantly different [500 vs. 1000 ms; first peak: 32*.*2 ± 17*.*0 vs. 45*.*7 ± 30*.*8, *t(*15*)* = 0*.*95, *p* = 0*.*36; inhibition: 50*.*4 ± 11*.*9 vs. 62*.*6 ± 20*.*9, *t(*12*)* = 1*.*13, *p* = 0*.*28; second peak: 85*.*8 ± 28*.*6 vs. 91*.*8 ± 20*.*4, *t(*10*)* = 0*.*38, *p* = 0*.*71]. The average firing rate over the period after the stimulus (100 and 200 ms, for both 500 and 1000 ms) was significantly higher than the pre-stimulus firing rate in all comparisons [*F(*1*,* 35*)* = 14*.*69, *p <* 0*.*001, and *F(*2*,* 41*)* = 13*.*82, *p <* 0*.*001 for 500 and 1000 ms stimulation, respectively; *post-hoc* all *p <* 0*.*001] (**Figures 4F–H**). Comparing different stimulus strengths revealed that five out of eight cells showed a significantly stronger inhibitory response during the stimulus when the stimulus strength was increased (power from 8*.*59 ± 8*.*55% to 58*.*89 ± 25*.*07%; ANOVA; *p <* 0*.*001, **Figures 4F,G**). Similarly, five out of eight cells showed a significantly stronger rebound after stronger light stimulation (ANOVA; *p <* 0*.*001) (**Figure 4G**). Thus, the strength of this rebound was also related to the strength and duration of the light stimulus. In general it was the case that cells showing strong inhibition also showed rebound firing (**Figure 4H**).

#### **EVOKED MOVEMENTS FOLLOW TERMINATION OF SYNCHRONOUSLY ACTIVATED PURKINJE CELLS IN AWAKE MICE**

To directly investigate the impact of light stimulation of PCs on movements, we optogenetically stimulated PCs over lobules V and VI in awake mice (**Figure 5A**) (Stark et al., 2012). These cerebellar lobules have been reported to show zonal proximal limb and tail representations in cats and rodents (Provini et al., 1968; Robertson, 1984; Buisseret-Delmas and Angaut, 1993; Jorntell et al., 2000; Ekerot and Jorntell, 2001). Mice were placed in a dark environment on a freely rotating transparent disc to allow recording of behavior from underneath with an infrared camera (**Figure 5A**), while we stimulated an estimated 400 PCs (see Materials and Methods) with flashes of blue light. Stimulations in resting mice resulted in stereotypical twitches of tail and proximal limbs (**Figures 5B–F**). Robust behavioral responses could be elicited by stimuli ranging from 25 to 500 ms (**Figure 5F**). In line with PC and CN responses, the behavioral response was graded and linearly related to the power density of the light stimulus (R2 = 1*.*00) (**Figure 5D**), while the onsets of the muscle contractions were strongly related to the offset of the stimulus (R2 = 1*.*00) (**Figures 5E–H**). The behavioral response was delayed with respect to the end of the stimulus by an average of 81*.*5 ± 27*.*9 ms (129 trials, *N* = 3 mice; 68*.*7 ± 36*.*0 ms, 85*.*1 ± 24*.*8 ms, 86*.*3 ± 22*.*2 ms for individual mice) (**Figure 5E**). The strength of the response did not diminish or enhance with repeated activation for the intervals used (*r* = −0*.*07, *p* = 0*.*49; mean response: 104*.*9 ± 36*.*5% of first response at 2*.*9 ± 1*.*4 s) (**Figure 5E**).

#### **MUSCLE CONTRACTIONS RESULTING FROM SYNCHRONOUSLY ACTIVATED PURKINJE CELLS ARE NOT MEDIATED BY CEREBRAL CORTEX**

To examine whether the cerebral cortex was required to initiate movements following optogenetic stimulation of the cerebellar cortex we recorded electroencephalograms (EEGs) from primary motor cortex and electromyograms (EMGs) from the musculus biceps femoris of the hind limb in anesthetized mice, while stimulating PCs and recording CN activity in the medial cerebellar nucleus (*N* = 14) (**Figures 2A**, **5I,J**). Stimulation-offset triggered averages of the cortical EEG showed a stereotypic EEG waveform consisting of a sequence of peaks and troughs (P1, N1, P2, N2, and P3 subsequently, *N* = 14) (**Figures 5I,J**). The timing between the left and right EEG for these components was identical for 500 and 1000 ms light stimulations (**Table 1**). Apart from yielding a robust response in the cortical EEG, stimulations of 500 to 1000 ms duration resulted in stereotypical twitch responses in the tail and proximal limbs of anesthetized mice. The onset of

muscle twitches was related to the termination of the light stimulus, with the maximal rectified EMG response at 48*.*0 ± 10*.*3 ms after stimulus offset [41*.*2 ± 2*.*2 ms and 52*.*0 ± 11*.*2 ms after 500 and 1000 ms stimulation, respectively, *t(*18*)* = 3*.*07, *p* = 0*.*007; *N* = 7, and *N* = 12] (**Figures 5I,J**). Instead, the onset of the EMG response occurred earlier at 36*.*18 ± 11*.*05 ms [30*.*52 ± 7*.*21 ms and 39*.*48 ± 11*.*79 ms after 500 and 1000 ms stimulation, respectively *t(*18*)* = 2*.*04, *p* = 0*.*055; *N* = 7, and *N* = 12], which places it at similar times as the first input to the cerebral cortex (Meeren et al., 1998). Thus, the movements evoked by optogenetic stimulation of the cerebellum were likely initiated via a direct pathway (e.g., red nucleus and/or lateral reticular formation) and not through projections to the cerebral cortex.

#### **MODULATION OF THE OLIVO-CEREBELLAR FEEDBACK LOOP BY PURKINJE CELLS**

Optogenetic stimulation of PCs elicited robust SS activity (**Figure 3**). This, in theory should lead to inhibition of GABAergic CNs that project to the IO and a resulting disinhibition of olivary neurons to cause an increase of CS activity. This prediction indeed holds. During light activation for 1000 ms the average CS rate (*N* = 7) increased significantly from a baseline of 0*.*73 ± 0*.*38 Hz to 1*.*54 ± 0*.*89 Hz and 1*.*84 ± 0*.*45 with a low and high stimulus strength, respectively [baseline vs. weak *t(*12*)* = −2*.*194, *p* = 0*.*049; weak vs. strong *t(*6*.*002*)* = −2*.*811, *p* = 0*.*031; baseline vs. strong *t(*6*)* = −2*.*841, *p* = 0*.*030]. The observed increase in CS activity, which occurs consistently throughout trials, might in principle result from single cell connections in the olivocerebellar loop, but it may be facilitated through more extensive network properties in that multiple PCs of the same sagittal zone converge onto individual CNs (De Zeeuw et al., 2011). When the membrane depolarization of a single PC during light stimulation *in vivo* was prevented by hyperpolarizing current injections, the SS frequency of that particular cell did not increase, whereas its CS rate increased persistently during and directly after the light stimulus that was applied to multiple PCs within a zone (**Figure 6A**). This indicates that the network properties of an ensemble of PCs are sufficient to induce an increase in CS activity, even when the SS activity of the recorded PC is suppressed. If the CS activity of a particular zone is enhanced following optogenetic stimulation of PCs through the network properties of the olivocerebellar loop, one expects that the activity of MLIs, which receive climbing fiber input through spillover (Jorntell and Ekerot, 2003; Szapiro and Barbour, 2007), will also be increased once the CS increase occurs, but not earlier than that. Indeed, MLIs responded to a 1000 ms light stimulation with a significant increase in firing frequency from 11*.*61 ± 2*.*43 Hz to 28*.*89 ± 4*.*32 Hz [*t(*4*)* = −3*.*476, *p* = 0*.*025; *N* = 3], but this increase was delayed for more than 50 ms relative to the onset of the light stimulus reflecting elapsed time prior to disinhibition of the IO by the light stimulus (**Figure 6B**). We observed several large postsynaptic events in voltage clamp recordings of CNs both during and after light stimulation, which probably reflect climbing fiber collateral input (**Figure 7**). In addition, activity in the climbing fibers probably also facilitated late CN rebound via their collaterals (**Figures 6C,D**, **7**), because during voltage clamp recordings of CNs we observed putative climbing

**FIGURE 5 | Timed motor responses in awake mice during optogenetic activation of PCs. (A)** For the behavioral assay head-fixed mice were placed on a transparent disc that could freely rotate. The optic fiber was placed on the brain surface of lobules V and VI (left) for optogenetic stimulation. Light was delivered to the brain via a LED coupled to the optic fiber. Right: Bottom view of a mouse responding to optogenetic activation of PCs (250 ms, ∼5 mW/mm2) with a twitch of its tail and hind legs after stimulus offset. Camera frames were acquired at 100 Hz. Differences between two frames at the stimulus offset ("pre," cyan) and 200 ms post-offset ("post," red) show

relative position change between the two time points. **(B)** Individual behavioral responses (gray traces), response corresponding to twitch shown in **(A)** [red trace, one frame chosen at offset (pre), and one 200 ms post-offset (post)] and mean behavioral response (black trace) following a 250 ms light stimulus. **(C)** Behavioral responses were graded with increases in light intensity. Estimated power densities are shown at a depth of the PC monolayer (∼120 μm). **(D)** Normalized behavioral response plotted vs. power density showing a linear correlation (R2 = 0*.*9993, slope = 0.19). *(Continued)*

#### **FIGURE 5 | Continued**

**(E)** Raster plot showing individual behavioral onsets relative to the stimulus offset (time = 0). Inset: box plots (three mice indicated by different colors) of behavioral onsets relative to the stimulus offset (whiskers indicate distance from 25 to 75% interquartile ranges to furthest observations, center mark represents the median). **(F)** The onset of behavior shifted with an increase in stimulus duration, such that the relative delay to a behavioral response onset relative to stimulus offset was maintained. Note that behavioral responses can be elicited by stimuli with durations of 25 ms. **(G)** Time from stimulus onset to behavioral onset plotted against stimulus duration followed a linear relationship (R2 = 0*.*9999 and slope = 0.96) demonstrating that the onset of behavioral responses shifts relative to the stimulus duration. **(H)** The

interstimulus interval did not have an effect on strength of the behavioral response (*r* = −0*.*071, *p* = 0*.*49). **(I)** and **(J)** Simultaneous recordings of CNs, bihemispheric EEG, and EMG to 500 ms **(I)** and 1000 ms **(J)** light stimulation of PCs in anesthetized mice. For clarity, the stimulation period has been truncated and only the last 45 ms of the stimulus is shown in the blue box. Vertical scale bars apply to both EEG traces and EMG traces in **(I)** and **(J)**. Top panels, average Gaussian-convoluted spike train of all CNs. Middle panels, left and right EEG. Bottom panels, rectified, differentiated and again rectified EMG responses. The vertical dotted lines indicate the location of the positive (P1 to P3) and negative (N1 to N2) deflections in the EEG signals. Note that the onset of the EMG response occurs before the first response peak in the EEG, while the EMG signal itself is preceded by CN activity.

#### **Table 1 | Timing of EEG and EMG components.**

*Timing of motor cortex EEG relative to optogenetic activation of PCs. Each column lists the average delay from stimulus offset to the indicated response. Each row lists a different condition or statistical test. N lists number of sets of ten traces analyzed.*

fiber-mediated excitatory postsynaptic currents (EPSCs) within 50 to 100 ms after termination of the light stimulation coinciding with the moments when CSs occur in PCs (see also **Figure 7**). Indeed in PCs, a robust but loosely timed CS was observed after stimulus offset. For 1000 ms excitation of PCs this CS had an average latency of 73*.*11 ± 32*.*73 ms (*N* = 6), whereas for 500 ms excitation this latency (82*.*13 ± 49*.*43 ms) was slightly, but not significantly longer [*t(*275*.*53*)* = −1*.*904, *p* = 0*.*058]. Taken together, these observations indicate that light-driven activation of PCs is effective in disinhibiting the IO and that the timing of the CS activity of PCs and that of the activity in the presumptive climbing fiber collaterals after offset of the light stimulus both correlate well with the temporal characteristics of the rebound in CNs.

 Yet, technical limitations have hampered intracellular *in vivo* whole cell recordings of CNs and selective PC stimulation. In the present study, we used the Ai32 (ChR2(H134R)-eYFP) transgenic mouse and a L7-Cre driver line to allow for selective and temporally well controlled activation

**FIGURE 6 | Optogenetic stimulation of PCs elicits an increase in CS activity, which is most likely a network effect. (A)** Light stimulation (blue bar) evokes an increase in CS activity even when the SS increase is prevented by intracellular current injection via the patch electrode. Additionally, a CS was observed after stimulus offset. Notice the depolarized membrane potentials after stimulus offset indicating slow inactivation of the ChR2 (H134R) channel (arrows). **(B)** MLI activity is not directly increased in response to PC stimulation but after *>*50 ms delay. This activation is likely due to the recorded CS increase **(A)** that leads to MLI activation through glutamate spillover. The increase in MLI firing frequency outlasted the light stimulus [see also Gaussian-convoluted trace in red; Putative MLIs, *N* = 3; baseline firing rate 50 ms before light stimulation: 1*.*69 ± 9*.*54, firing rate *<*50 ms after strong light stimulation: