Implications of variable synaptic weights for rate and temporal coding of cerebellar outputs

  1. Shuting Wu
  2. Asem Wardak
  3. Mehak M Khan
  4. Christopher H Chen
  5. Wade G Regehr  Is a corresponding author
  1. Department of Neurobiology, Harvard Medical School, United States
  2. Department of Neural and Behavioral Sciences, Pennsylvania State University College of Medicine, United States

Abstract

Purkinje cell (PC) synapses onto cerebellar nuclei (CbN) neurons allow signals from the cerebellar cortex to influence the rest of the brain. PCs are inhibitory neurons that spontaneously fire at high rates, and many PC inputs are thought to converge onto each CbN neuron to suppress its firing. It has been proposed that PCs convey information using a rate code, a synchrony and timing code, or both. The influence of PCs on CbN neuron firing was primarily examined for the combined effects of many PC inputs with comparable strengths, and the influence of individual PC inputs has not been extensively studied. Here, we find that single PC to CbN synapses are highly variable in size, and using dynamic clamp and modeling we reveal that this has important implications for PC-CbN transmission. Individual PC inputs regulate both the rate and timing of CbN firing. Large PC inputs strongly influence CbN firing rates and transiently eliminate CbN firing for several milliseconds. Remarkably, the refractory period of PCs leads to a brief elevation of CbN firing prior to suppression. Thus, individual PC-CbN synapses are suited to concurrently convey rate codes and generate precisely timed responses in CbN neurons. Either synchronous firing or synchronous pauses of PCs promote CbN neuron firing on rapid time scales for nonuniform inputs, but less effectively than for uniform inputs. This is a secondary consequence of variable input sizes elevating the baseline firing rates of CbN neurons by increasing the variability of the inhibitory conductance. These findings may generalize to other brain regions with highly variable inhibitory synapse sizes.

Editor's evaluation

This important manuscript emphasizes some previously ignored aspects of synaptic communication between Purkinje neurons and their targets in the cerebellar nuclei. The evidence supporting the main conclusions is solid.

https://doi.org/10.7554/eLife.89095.sa0

Introduction

The cerebellum is involved in behaviors ranging from balance, motor control, and motor learning, to social and emotional behaviors (Hull and Regehr, 2022). Cerebellar dysfunction has been linked to severe motor impairment and psychiatric disorders, including autism spectrum disorder, schizophrenia, bipolar disorder, and depression (Argyropoulos et al., 2020; Schmahmann and Sherman, 1998). Within the cerebellar cortex, mossy fiber inputs from many brain regions excite granule cells that in turn activate Purkinje cells (PCs), which are the sole outputs. PCs project primarily to the cerebellar nuclei (CbN, also known as deep cerebellar nuclei [DCN]), which then project to other brain regions. Clarifying how PCs control the firing of CbN neurons is a vital step in understanding cerebellar processing.

PCs are GABAergic and fire spontaneously at frequencies ranging from 10 spikes/s to over 100 spikes/s (Thach, 1968; Zhou et al., 2014). It was estimated that 40 PCs converge onto each CbN glutamatergic projection neuron to strongly suppress their firing (Person and Raman, 2012a). This is offset by the tendency of CbN neurons to fire spontaneously (Raman et al., 2000) and by numerous excitatory inputs from climbing fiber (CF) and mossy fiber collaterals (Najac and Raman, 2017; Wu and Raman, 2017). Excitatory inputs to CbN neurons are small and slow, and their number and firing rates are poorly constrained (Najac and Raman, 2017; Wu and Raman, 2017). Therefore, in this study, we mainly focus on the control of CbN neuron firing by inhibitory inputs from PCs.

It has been proposed that PCs convey information with a rate code, a temporal code, or a combination of both, but there has been considerable debate regarding whether PC outputs are primarily encoded by firing rate or by a combination of rate and timing (Abbasi et al., 2017; Brown and Raman, 2018; Cao et al., 2012; Gauck and Jaeger, 2000; Heck et al., 2013; Herzfeld et al., 2023a; Hoehne et al., 2020; Hong et al., 2016; Medina and Khodakhah, 2012; Payne et al., 2019; Person and Raman, 2012a; Person and Raman, 2012b; Sedaghat-Nejad et al., 2022; Stahl et al., 2022; Sudhakar et al., 2015; Walter and Khodakhah, 2009; Wu and Raman, 2017). There is compelling evidence that a rate code is used for some behaviors where the firing rates of PCs and CbN neurons are inversely correlated, and the behavior outputs can be readily explained by the changes in the firing rate of PCs and CbN neurons (Abbasi et al., 2017; Herzfeld et al., 2023a; Hong et al., 2016; Medina and Khodakhah, 2012; Payne et al., 2019; Sudhakar et al., 2015; Walter and Khodakhah, 2009). On the other hand, synchronous firing or pauses of PCs allow precise temporal control of CbN neuron firing (De Schutter and Steuber, 2009; Gauck and Jaeger, 2000; Han et al., 2020; Hong et al., 2016; Özcan et al., 2020; Person and Raman, 2012a; Sedaghat-Nejad et al., 2022; Steuber et al., 2007). Dynamic clamp studies of CbN neurons with uniform-size PC inputs demonstrated the ability of PC synchrony to promote CbN neuron firing and precisely entrain spike timing (Gauck and Jaeger, 2000; Person and Raman, 2012a), but whether PC synchrony is prominent in vivo has been debated (Herzfeld et al., 2023a; Sedaghat-Nejad et al., 2022). Synchronous pauses of PC firing have also been shown to be highly effective at transiently elevating CbN neuron firing (Han et al., 2020). A recent study found that an electrically coupled subtype of molecular layer interneuron (MLI) synchronously fire and inhibit PCs in vivo (Lackey et al., 2023), thereby suggesting the presence and potential importance of synchronous pauses in vivo.

Here we reexamine how PCs control the firing of CbN neurons. We find that unitary PC-CbN inputs have highly variable amplitudes, and some are very large. We used dynamic clamp and simulations to explore the implications of nonuniform input sizes. We find that for a given total inhibitory conductance, nonuniform input sizes led to highly variable inhibitory conductances and high basal CbN firing rates. Inputs of all sizes regulated the rate and timing of CbN neuron firing, but larger inputs had a bigger influence. Although PC synchrony had less influence on the firing rates of CbN neurons for nonuniform-size inputs because of higher basal firing rates, synchronizing the firing of several large inputs effectively regulated the CbN neuron firing. Thus, the nonuniform distribution of PC input sizes allows the information of firing rates, timing, and synchrony to be conveyed from the cerebellar cortex to the CbN in a simultaneous, graded manner.

Results

PC to CbN input sizes are highly variable in juvenile mice

To understand how PCs control the firing of CbN neurons, it is necessary to determine the distribution of the sizes of PC inputs. Previously, PC to CbN inputs were approximated by uniform inputs based on the average of all inputs recorded (Han et al., 2020; Person and Raman, 2012a; Wu and Raman, 2017). We reexamined the issue of input sizes by determining the sizes of many individual inputs in young (P10-20, n = 74) and juvenile (P23-32, n = 83) animals (Figure 1). We cut brain slices, stimulated PC axons with an extracellular electrode placed far from the CbN, and recorded the evoked inhibitory postsynaptic currents (IPSCs) in CbN glutamatergic projection neurons with whole-cell recordings. We used a high Cl- internal solution to determine the distribution of input sizes because it provided superior stability, low-access resistance, and high sensitivity compared to a low Cl- internal. We adjusted the stimulus intensity to stochastically activate an individual PC axon and isolate a unitary input. This approach is shown for three inputs onto the same CbN neuron for a juvenile mouse (P27) (Figure 1a–c). As shown in the individual trials for a small (weak) input, stimulation evoked short-latency IPSCs (720 pA) in some trials but not others (Figure 1a, upper, gray). The average of successes (Figure 1a, upper, black) and failures (Figure 1a, upper, blue) is shown. This is also apparent in a plot of the IPSC amplitudes for each trial (Figure 1a, lower) showing that the IPSCs were evoked in a fraction of the trials. Similar experiments are shown for a medium-size input (Figure 1b, 1690 pA) and a large-size (strong) input (Figure 1c, 6280 pA). To better evaluate the variability in the input sizes, we plotted the distributions of unitary PC to CbN input conductances for young (Figure 1d, P10–20) and juvenile (Figure 1e, P23–32) mice. Input sizes were somewhat variable in young mice (coefficient of variation [CV] = 0.7; Figure 1d), which is qualitatively similar to previous findings (P13–29, n = 30) (Person and Raman, 2012a). In juvenile mice, there was considerably more variability, primarily because of the presence of many medium and large inputs (mean 52.9 ± 6 nS, CV = 1.0; Figure 1e). This is evident in a comparison of the cumulative histograms of IPSC conductances for young and juvenile animals, which showed significantly different distributions (Figure 1f; p<0.0001, Kolmogorov–Smirnov test). These results show that PC to CbN input strength is highly variable in juvenile mice.

The amplitudes of Individual Purkinje cell (PC) to cerebellar nuclei (CbN) inputs are highly variable.

Unitary PC-CbN IPSCs were recorded in brain slices. The data in (a–c) were obtained for this study. The histogram of input sizes in (d–e) were based on new experiments (d, n = 44; e, n = 39), on Turecek et al., 2017 (e, n = 44), and Khan et al., 2022 (d, n = 30). (a) Example of a small PC-CbN input (P27). Top: responses evoked with the same stimulus intensity are superimposed for 40 trials (gray), and the average of successes (black) and failures (blue) is shown. Bottom: IPSCs’ amplitudes are plotted as a function of trial number. (b) As in (a), but for a medium-size input onto the same cell. (c) As in (a), but for a large-size input onto the same cell. (d) Distribution of input sizes for PC-CbN IPSCs in young mice (P10–P20, n = 74). (e) Distribution of input sizes for PC-CbN IPSCs in juvenile mice (P10–P20, n = 83). (f) Normalized cumulative plot of conductances. The distributions of unitary input sizes in P10–20 animals and P23–32 animals were significantly different (p<0.0001) with a Kolmogorov–Smirnov test.

Using dynamic clamp and simulations to examine the effects of variable input sizes

The variability in the amplitudes of PC to CbN inputs in juvenile animals raised the issue of how the wide range of input sizes influences the way PCs control the firing of CbN neurons. We then designed dynamic clamp studies using highly variable PC-CbN input sizes. The PC-CbN unitary conductances we used in dynamic clamp studies were based on our measured input sizes with two corrections (‘Methods’). First, we corrected for the effects of a high-chloride internal solution by scaling down the amplitudes by a factor of 2.3, based on the estimates of conductance values determined with high and physiological Cl- internal solutions (Bormann et al., 1987; Gjoni et al., 2018; Sakmann et al., 1983). Second, we corrected the input sizes for the depression (reduced to 40% of initial amplitude) that occurs during physiological activation (Pedroarena and Schwarz, 2003; Telgkamp and Raman, 2002; Turecek et al., 2017; Turecek et al., 2016), as has been done in previous dynamic clamp studies (Han et al., 2020; Person and Raman, 2012b; Wu and Raman, 2017). The excitatory conductances we used in dynamic clamp studies were based on previous studies (Najac and Raman, 2017; Wu and Raman, 2017), with a relatively unconstrained frequency to pair with different inhibitory conductances.

The corrected distribution of input sizes used to guide our dynamic clamp studies is shown in Figure 2f (red). For simplicity, instead of having a continuous range of input sizes, we approximated the distribution of PC input sizes in juvenile animals (Figure 1e, ‘Methods’) with 16 small (3 nS), 10 medium (10 nS), and 2 large (30 nS) inputs (Figure 2f, gray; total 200 nS, similar to the total conductances used in Person and Raman, 2012a). The timing and frequency of the inputs were based on PC firing recorded in awake-behaving mice, with each input firing at 83 spikes/s (Figure 2—figure supplement 1, ‘Methods’). The resulting spike times (Figure 2a, Figure 2—figure supplement 2) were convolved with the corresponding unitary conductance size (Figure 2—figure supplement 2) to generate the small (Figure 2b, green), medium (Figure 2b, blue), and large (Figure 2b, red) conductances, which were then summed to generate the total inhibitory conductance arising from all inputs (Figure 2b, black). The total inhibitory conductance showed large variations that were dominated by the medium and large inputs (Figure 2b), which in turn produced large fluctuations in the membrane potential when injected into a CbN neuron (Figure 2c). Spiking of the CbN neuron occurred mainly when the amplitude of the total inhibitory conductance was small (Figure 2b and c).

Figure 2 with 3 supplements see all
Large Purkinje cell (PC) inputs powerfully influence cerebellar nuclei (CbN) neuron firing.

Dynamic clamp experiments and simulations were conducted with variable distribution of PC input sizes. (a) The corrected distributions of PC-CbN input sizes in juvenile animals (f, red) were approximated with 16 small inputs (3 nS, green), 10 medium inputs (10 nS, blue), and 2 large inputs (30 nS, red). Raster plots are shown for the spike times of the 16 small (green), 10 medium (blue), and 2 large (red) inputs used in dynamic clamp experiments. (b) The total conductance waveform is shown (black) along with contributions from small (green), medium (blue), and large (red) inputs. (c) Spikes in a CbN neuron evoked by the total conductance in (b) in dynamic clamp experiments. (d) The normalized cross-correlograms of input spiking and CbN neuron spiking for small, medium, and large inputs for dynamic clamp experiments. Different cells (n = 6, gray) are shown along with the average cross-correlograms (colored traces). (e) Summary of the excitation (e), inhibition (i), and half-decay time (t1/2), as defined in the inset for the data in (d). Inset shows how the parameters are determined. (f) Cumulative histogram of all recorded inputs from Figure 1 for P23–32 mice corrected for depression and internal solution (red), the simplified input distribution used in dynamic clamp studies in a (gray), and inputs drawn from that distribution that were used in a simulation (black). (g) Calculated cross-correlograms (normalized) for each of the different inputs used in a simulation. (h) Summary of the excitation (e), inhibition (i), and half-decay time (t1/2), for a simulation as defined in the inset for the simulated cell in (g) (black), and for inputs to nine other simulated cells with different distributions of inputs (gray). The summary data are shown as the mean ± SEM.

All inputs had effects on the timing of CbN neuron firing, but larger inputs had much bigger effects. As shown in the cross-correlograms of input timing and CbN neuron spiking, small inputs produced a small transient decrease in CbN neuron spiking (32% decrease; Figure 2d, left), while medium-size inputs strongly reduced CbN neuron spiking (80% decrease; Figure 2d, middle), and large inputs transiently shut down CbN neurons for approximately 2 ms (Figure 2d, right). Intriguingly, the inhibition generated by inhibitory inputs was preceded by an increase in spikes that was particularly prominent for large inputs (61% increase for large, 29% increase for medium, and 8% increase for small; Figure 2d). The magnitude of excitation (Figure 2e, top) and inhibition (Figure 2e, middle), as well as the duration of inhibition (Figure 2e, bottom), were all positively correlated with the size of the PC input. These findings highlight the ability of large PC inputs to control the timing of CbN neuron spiking.

We then performed simulations to extend these findings to a more realistic distribution of PC input sizes (‘Methods’). We determined the sizes of PC input by assigning them values randomly drawn from the distribution of Figure 2f (red) until the total inhibitory conductance reached 200 nS. An example distribution of PC input sizes used in a simulation is shown in Figure 2f. The cross-correlograms of input timing and CbN neuron spiking for each of the PC inputs were determined, and they all showed a similar pattern, with an elevation followed by a suppression of CbN firing (Figure 2g). The magnitudes of the excitation and inhibition of CbN firing had a similar dependence on the size of the PC inputs as shown for the example neuron (Figure 2h, black) and for nine other simulated neurons (Figure 2h, gray). In light of the many types of voltage-gated ion channels present in CbN neurons (Afshari et al., 2004; Aizenman and Linden, 1999; Alviña and Khodakhah, 2008; Raman et al., 2000; Raman and Bean, 1999; Santoro et al., 2000; Steuber et al., 2011), we were somewhat surprised that the simple integrate-and-fire model we used was able to recapitulate our dynamic clamp studies, which indicates that our conclusions are not specific to the properties of the CbN neurons. We also performed simulations with different scaling factors of the distribution of input sizes, while keeping the same total conductances (Figure 2—figure supplement 3). Applying a scaling factor of 0.5 or 1.5 changed the number and size of PC inputs, but the results were qualitatively similar, and in all cases larger inputs had a greater influence on the firing of CbN neurons. These simulations complemented our dynamic clamp studies and showed that the properties of cross-correlograms and their dependence on the size of the PC input can be extended to realistic distributions of input sizes.

The autocorrelation of PC firing leads to disinhibition prior to inhibition of CbN neurons

The elevated spiking observed prior to the inhibition by a PC input is intriguing. If such a cross-correlogram was observed in vivo, it might be attributed to excitation of the CbN neuron by other inputs that preceded PC inhibition, but that cannot be the case for our experimental conditions. Another possibility is that the refractory period of PC firing results in reduced inhibition and effective excitation. In that case, the properties of this disinhibition period will be shaped by the firing statistics of PCs. We tested this hypothesis by performing dynamic clamp experiments with the timing based on three different PCs recorded in vivo and on an artificial Poisson distribution (Figure 3a). As shown in the interspike interval (ISI) histograms (Figure 3a), auto-correlograms (Figure 3b), and in raster plots (Figure 3—figure supplement 1a), the three recorded PCs had refractory periods that were correlated with their firing frequency (~3.5 ms for 49 Hz, ~2 ms for 83 Hz, and ~1.5 ms for 122 Hz firing), while the Poisson input did not have a refractory period. We then generated inhibitory conductances by convolving spike times with a 20 nS single-input conductance. To compensate for the differences in input firing frequency, we adjusted the number of the inputs so that the total inhibitory conductances were approximately the same for all cases (12 × 20 nS at 49 Hz, 9 × 20 nS at 83 Hz, 6 × 20 nS at 122 Hz, and 9 × 20 nS for Poisson inputs). When input spike timing was based on in vivo recordings of PC firing, the average inhibitory conductances decreased prior to the PC spike as a result of the refractory period (Figure 3c). Consequently, the cross-correlograms (Figure 3d) and raster plots (Figure 3—figure supplement 1b) showed elevated spiking in the CbN neuron prior to suppression by the PC spike. This is because a PC that spikes at t = 0 does not spike for several milliseconds before or after that spike. Consistent with our hypothesis, the speed and amplitude of this decrease in inhibitory conductance and the associated elevation in spiking depended on the PC firing statistics, with the faster firing inputs associated with larger, shorter-lived effects (49 Hz: 27% increase, halfwidth 5.3 ms; 83 Hz: 39% increase, halfwidth 3.4 ms; 122 Hz: 65% increase, halfwidth 1.7 ms; Figure 3d). The cross-correlograms of PC inputs and CbN firing transiently showed transient suppression of firing that was followed by briefly elevated firing that was most prominent for rapidly firing PCs (Figure 3d, PCs that fire at 122 spikes/s). This elevated firing was also readily explained by the properties of PC firing and the refractory period of PCs. The Poisson input that lacked a refractory period did not have a decrease in the inhibitory conductance, and therefore CbN firing was not elevated prior to or following inhibition in the cross-correlogram (Figure 3c and d, far right). Thus, the autocorrelations of PC firing led to different extents of elevated CbN firing prior to suppression, and this affects how they control the spike timing of CbN neurons.

Figure 3 with 1 supplement see all
Autocorrelation of Purkinje cell (PC) firing leads to excitation prior to inhibition of cerebellar nuclei (CbN) neurons.

Dynamic clamp experiments were conducted using PCs with different firing statistics. (a) Interspike interval (ISI) histograms for three different PCs recorded in vivo (left), and for an artificial Poisson input lacking a refractory period (right). (b) Autocorrelation functions for the ISI distributions in (a). At 0 ms, all graphs peak at 1 and graphs are truncated to allow better visualization. (c) Calculated spike-triggered average inhibitory conductances for different cases (12 × 20 nS at 49 Hz, 9 × 20 nS at 83 Hz, 6 × 20 nS at 122 Hz, and 9 × 20 nS for Poisson inputs), with ISIs drawn from the corresponding distributions in (a). (d) Cross-correlograms are shown for PC inputs and CbN spiking for dynamic clamp experiments that used the distributions in (a).

The amplitude and coefficient of variation of PC inhibition regulate the firing rate of CbN neurons

Thus far, we have shown the differential effects on CbN spike timing by different-size PC inputs. PCs also control the average firing rate of CbN neurons in addition to spike timing. We used dynamic clamp to determine how the amplitude and the CV of the inhibitory conductance influence CbN firing. We began by examining how changing the amplitude of a constant inhibitory conductance altered the firing of CbN neurons (Figure 4a). The firing rate of CbN neurons was approximately 200 spikes/s for an inhibitory conductance of 30 nS, but as the magnitude of the inhibitory conductance increased the spike rate decreased, and an inhibitory conductance of 65 nS silenced CbN firing (Figure 4a and b). As expected, the CbN firing rate was inversely related to the amplitude of inhibitory conductance (Figure 4b). We then examined how the variability in the inhibitory conductance influenced CbN spiking. We kept the total inhibitory conductance constant while varying the sizes and numbers of PC inputs that contribute to the total inhibitory conductance (Figure 4c). We generated the total inhibitory conductance for the indicated numbers and sizes of PC synaptic inputs with the timing based on PC firing recorded in vivo (Figure 4c, Figure 2—figure supplement 1; ‘Methods’). While the average of the conductance wave was the same for all combinations of input sizes and numbers, as the size of the inputs increased, the conductance became increasingly variable (Figure 4c) and the CV became larger (Figure 4e). Evoked firing rates in CbN neurons were low for many small inputs, but as the number of inputs decreased and the size increased, the firing rate increased markedly (from 12 spikes/s to 107 spikes/s; Figure 4d and f). The firing rate was strongly dependent on the CV of the inhibitory conductance (Figure 4g). These findings establish that increases in the magnitude of the average inhibitory conductance suppress firing, whereas increases in the variability of the inhibitory conductance promote firing.

The amplitudes and fluctuations of the total inhibitory conductance both regulate the firing of cerebellar nuclei (CbN) neurons.

Dynamic clamp experiments and simulations were conducted with varied amplitudes and fluctuations in the total inhibitory conductance. (a) CbN neuron spiking observed for constant inhibitory conductances of the indicated amplitudes. (b) Summary of CbN neuron firing rates (n = 7 cells) during constant inhibitory conductances as in (a). (c) Inhibitory conductances with the same average conductance (56 nS) are shown for a constant conductance (far left) and for different cases with the numbers and sizes of inputs varied. (d) CbN neuron spiking is shown for dynamic clamp experiments with the inhibitory conductances in (c). (e) The coefficient of variation (CV) of the inhibitory conductance is plotted as a function of the input size. (f) CbN firing rate is plotted as a function of input size. (g) CbN firing rate is plotted as a function of the CV of the inhibitory conductance. (h) Normalized cumulative plots of conductances of three different input distributions (solid lines) drawn from the observed distribution (Figure 2f, red) and for the 40 × 5 nS inputs (dashed line). (I) CbN firing rates in dynamic clamp experiments that used the inhibitory conductances in (h) are plotted as a function of the CV of the inhibitory conductance. (j) Simulated CbN firing rates based on 1000 different input distributions randomly drawn from the observed distribution of input sizes (filled gray) and for 40 × 5 nS inputs (open circle) are plotted as a function of the CV of the inhibitory conductance. The three distributions used in dynamic clamp experiments in (i) are highlighted. The summary data are shown as the mean ± SEM.

Figure 4—source data 1

Different distributions and CbN neuron firing rates.

https://cdn.elifesciences.org/articles/89095/elife-89095-fig4-data1-v1.xlsx

The dependence of CbN firing on the variability of the inhibitory conductance prompted us to examine the influence of variable input sizes on the basal firing rate of CbN neurons. Three different distributions of PC input sizes drawn from the observed distribution of input sizes (Figure 2f, red) with different CVs and a total conductance value of 200 nS (Figure 4h, solid lines), together with 40 uniform 5 nS PC inputs (Figure 4h, dashed line), were used to generate inhibitory conductances for dynamic clamp experiments. Even though the average conductance was the same, the firing rates for the three nonuniform input sizes were higher than for uniform inputs (38, 50, and 67 spikes/s vs. 27 spikes/s; Figure 4i). The differences in the firing rates are readily explained by the CV of total conductances generated from different-size inputs (Figure 4i). We also simulated the firing evoked by 1000 different distributions of PC input sizes drawn from the observed input size distribution with a total conductance size of 200 nS and observed a broad range of firing rates that depended on the CV of the total inhibitory conductance (Figure 4j). Both dynamic clamp experiments and simulations showed that for the same total inhibitory conductance, CbN neuron firing rates are always higher for nonuniform inputs than for uniform inputs as a result of the higher CV of the inhibitory conductance (Figure 4i and j).

Different-size inputs reliably transfer a rate code

The ability of different-size PC inputs to convey a rate code to CbN neurons was unclear, given that both the magnitude and variability of total inhibition can influence the firing rates of CbN neurons. We first addressed this issue using simulations, in which we varied the firing rates of each PC input individually to determine how faithfully they convey a rate code. Simulations were similar to those of Figure 2g and h, but with the firing rates for each input varied between 0 and 160 spikes/s while maintaining the average firing rates of other inputs at 80 Hz (‘Methods’). Varying the firing rate of individual PC inputs led to an approximately linear, negatively correlated change in CbN firing rate (Figure 5a). Single large inputs were surprisingly effective at regulating the firing rates of CbN neurons. In this example, varying a single 37 nS input from 0 to 160 spikes/s decreased the output firing frequency from 126 spikes/s to 35 spikes/s (Figure 5a, red). There was a remarkably consistent relationship between the firing rates of CbN neurons and the total inhibitory conductances (Figure 5b). The slope of the CbN output firing rate/PC input firing rate was negatively correlated with the input size for the example neuron (Figure 5c, color coded) and for nine other neurons (Figure 5c, gray), and when different scaling factors were used (Figure 5—figure supplement 1). We also address this issue using dynamic clamp experiments in which the frequencies of small, medium, or large inputs were varied (Figure 5—figure supplement 2) and found that the ability of individual inputs to suppress CbN neuron firing also depended on input size (Figure 5d). These studies establish that individual PC inputs convey a rate code that depends on input size.

Figure 5 with 2 supplements see all
Purkinje cell (PC) inputs effectively convey an inverse rate code.

Simulations and dynamic clamp experiments were conducted with the firing rates of PC inputs varied. (a) Simulations with different-size inputs drawn from the distribution in Figure 2f and the firing rates of each input were varied. The firing rates of cerebellar nuclei (CbN) neurons are plotted as a function of the firing rate of the varied PC input, with the color indicating the input size. (b) The firing rates of CbN neurons in (a) are plotted as a function of the total inhibitory conductance (Gi) resulting from varying the firing rate of each PC input. The color scale is the same as in (a). (c) The slopes of CbN output firing rates vs. PC input firing rates are plotted for different-size inputs for the simulated CbN neuron in (a) and (b) (black), and nine other neurons with different input size distributions (gray). (d) Dynamic clamp experiments were performed with small (16 × 3 nS, green), medium (10 × 10 nS, blue), and large (2 × 30 nS, red) inputs. The firing rates of either all small, all medium, or all large inputs were varied (Figure 5—figure supplement 2). The slope of CbN output firing rate vs. PC input firing rate divided by the number of inputs is plotted for different-size inputs.

Figure 5—source data 1

Simulations and dynamic clamp experiments for rate codes.

https://cdn.elifesciences.org/articles/89095/elife-89095-fig5-data1-v1.xlsx

Synchronous firing of uniform- or variable-size PC inputs

Previous dynamic clamp studies based on uniform PC input sizes revealed the potential importance of synchrony in regulating the firing of CbN neurons (Gauck and Jaeger, 2000; Person and Raman, 2012a). We extended this approach to assess the effects of synchrony for different-size PC inputs. We performed dynamic clamp experiments with both uniform-size inputs (Figure 6a) and variable-size inputs (Figure 6b) in each cell. We first looked at the effects of synchrony on the firing rate of CbN neurons. In the absence of synchrony, uniform-size inputs generated a baseline conductance with low variability and CV (Figure 6aii and iii) that resulted in a low baseline firing rate of CbN neurons (25 spikes/s; Figure 6aiv). Synchronizing either 25 or 50% of the uniform inputs (Figure 6ai) increased the variability (Figure 6aii) and the CV (Figure 6aiii) of the total inhibitory conductance, which then resulted in a robust increase in the firing rate of CbN neurons (45 spikes/s with 25% synchrony, 1.8-fold; 88 spikes/s with 50% synchrony, 3.5 fold; Figure 6aiv and v; Figure 6c, black).

The influence of Purkinje cell (PC) synchrony on the firing rates of cerebellar nuclei (CbN) neurons for uniform- and variable-size PC inputs.

Dynamic clamp experiments were conducted with either uniform (a) or variable inputs (b) in which the conductance was kept constant, but the synchrony of the inputs was varied. (a) (i) The extent of synchrony of uniform-size inputs (40 × 5 nS) is varied. (ii) The inhibitory conductance in which the synchrony of inputs is varied as in (a). (iii) The coefficient of variation (CV) of the inhibitory conductance. (iv) CbN neuron firing rate with the conductance (average, black; individual cells, n = 14, gray). (v) The normalized firing rate for the cells in (iv). (b) Similar experiments were performed on the same cells as in (a), but for inputs of variable-sizes (small: 16 × 3 nS, green; medium: 12 × 8 nS, blue; large: 2 × 30 nS, red). In some cases, all types of inputs were synchronized (purple), in others only the small inputs (green) or the medium inputs (blue) were synchronized. (c) CbN neuron firing rate as a function of the percentage of synchronous inputs. (d) CbN neuron firing rate as a function of the total amplitude of synchronous inputs. (e) The normalized CbN firing rates as a function of the amplitude of the synchronized inputs. (f) CbN neuron firing rate as a function of the CV of the inhibitory conductances. (g–j) Similar plots to (c–f), but for simulations with different-size inputs (dark purple, based on a single distribution, light purple based on nine other different distributions) and uniform inputs (40 × 5 nS, black circles). (k) Violin plots showing the simulated CbN neuron firing rate with 100 different distributions of different-size inputs (not syn), and the two largest inputs or the two smallest inputs synchronized. (l) As in (k) but normalized to the nonsynchronized firing rate. The summary data are shown as the mean ± SEM.

Figure 6—source data 1

Dynamic clamp experiments and simulations for syncrony.

https://cdn.elifesciences.org/articles/89095/elife-89095-fig6-data1-v1.xlsx

For nonuniform inputs (small: 16 × 3 nS; medium: 8 × 12 nS; large: 2 × 30 nS), the generated conductance was more variable (Figure 6bii and iii), resulting in a higher baseline firing rate of CbN neurons (43 spikes/s; Figure 6biv), which is similar to Figure 4h–i. Synchronizing 50% of all inputs (small, medium, and large inputs, Figure 6bi, left) increased the variability (Figure 6bii, left) and CV (Figure 6biii, left) of the conductance, which then elevated the firing of CbN neurons from 43 spikes/s to 91 spikes/s (Figure 6biv, left; Figure 6c). The firing rates for 50% synchrony with nonuniform and uniform inputs were comparable (Figure 6aiv and biv), but because baseline firing rates were higher for nonuniform inputs, their relative increase in CbN firing was much lower (2.1-fold for nonuniform inputs compared to 3.5-fold for uniform inputs, p<0.0001; Figure 6av and bv). We also examined the effect of synchronizing 25 or 50% of small or medium inputs (Figure 6bi, right). Synchronizing 25 or 50% of either small or medium inputs barely increased the variability and CV of the conductance and led to minimal increases in the firing of CbN neurons (Figure 6bii–v, right). These observations suggest that the effects of PC synchrony primarily depend on the total amplitude of synchronized inputs (Figure 6d and e). The firing rates of nonsynchronized and synchronized inputs for either uniform or nonuniform input sizes are readily explained by the influence of the CV of the inhibitory conductance on the CbN firing rate (Figure 6f).

Simulations allowed us to explore the effects of a full range of synchrony levels, which would be impractical to test experimentally (Figure 6g–j). We examined uniform inputs (40 × 5 nS) and determined the effect of synchronizing 1, 2, … 20 inputs (Figure 6g–j, black open circles). We also examined nonuniform-size inputs drawn from the full distribution of sizes (Figure 2f, red), with a total conductance of 200 nS, and determined the firing rates evoked when we synchronized different combinations of inputs (Figure 6g–j, dark purple: for one distribution of input sizes; light purple: for nine other distributions of input sizes). The effects of synchrony on firing rates and the dependence on the CV of the conductance were qualitatively similar for simulations (Figure 6g–j) and dynamic clamp experiments (Figure 6c–f). There was a great deal of scatter in the firing rates plotted as a function of the percentage of synchronized inputs (Figure 6g, dark purple), but much less scatter when the firing rates were plotted as a function of the total amplitude of the synchronized inputs (Figure 6h, dark purple). This is consistent with our observation that synchronizing many small- or medium-size inputs does not have much effect on the firing frequency of CbN neurons (Figure 6bi–v, right; Figure 6c and d). For nonuniform inputs, synchrony has less influence on firing rates because the baseline firing rates are higher (Figure 6e and i). Nonetheless, synchrony of even a small number of large inputs can robustly elevate firing rates. As shown in simulations, synchronizing the two largest inputs can elevate CbN firing rate by approximately 20%, while synchronizing the two smallest inputs barely changed CbN firing rate (Figure 6k and l).

We also examined the influence of PC synchrony on the spike timing of CbN neurons in dynamic clamp experiments of Figure 6. Synchrony strongly altered the cross-correlograms of PC inputs and CbN firing, especially for the uniform-size inputs (Figure 7a and b). For uniform inputs (5 nS), single unsynchronized inputs weakly influenced the spike timing of CbN neurons (19% increase; 62% decrease; Figure 7a, left). Also, 50% synchrony of uniform inputs silenced CbN neurons for several milliseconds, and this was proceeded by a prominent increase in CbN spiking (89% increase; Figure 7a, right). This preceding increase in CbN spiking is similar to what was observed for medium and large inputs due to the autocorrelation function of PC firing (Figures 2 and 3) and reflects a disinhibitory period due to the synchronous pauses in PC spikes. This indicates that many small, perfectly synchronized uniform inputs function equivalently as a single large input in controlling the spike time of CbN neurons. For asynchronous nonuniform inputs, as in Figure 2, the influence of individual inputs on the timing of CbN firing depended on the size of the input, and single large inputs could have a very large influence on the timing of CbN spiking (47% increase; 100% decrease; Figure 7b, left, red). Synchronizing 50% of all nonuniform inputs generated a cross-correlogram similar to that of synchronizing 50% uniform inputs (Figure 7a and b, right). The strength of excitation (Figure 7c, top), amplitude of inhibition (Figure 7c, bottom), and duration of inhibition (Figure 7d) were dependent on the synchrony amplitude, as was the case for simulations (Figure 7e and f). For both dynamic clamp experiments and simulations, synchronizing the same amplitude of inputs led to larger timing effects for uniform inputs than for nonuniform inputs (Figure 7c–f), likely because the CbN firing rates with PC synchrony are lower for uniform inputs. These findings establish that PC synchrony and single large inputs are functionally equivalent in controlling the spike timing of CbN neurons, and for nonuniform inputs, the effects of synchrony on CbN spike timing depend on the total size of synchronized inputs.

The influence of Purkinje cell (PC) synchrony on the spike timing of cerebellar nuclei (CbN) neurons for uniform- and variable-size PC inputs.

(a) Top: the cross-correlograms of PC input and CbN neuron spiking for nonsynchronous inputs (left) and 50% synchronous inputs (right) with uniform-size inputs. Individual cells (gray) and averages (black) are shown. Lower: as above, but normalized to the baseline firing rate. (b) As in (a) but for different-size inputs (as in Figure 6). The cross-correlograms of small (green), medium (blue), and large (red) inputs for unsynchronized inputs (left) and 50% synchronous inputs (right, purple) are shown. (c) Summary plots of the excitation (e), inhibition (I) as a function of the amplitude of synchronized inputs. (d) As in (c) but for half-decay time (t1/2). (e, f) As in (c, d), but for simulations. The summary data are shown as the mean ± SEM.

Synchronously pausing uniform- or variable-size PC inputs

Brief synchronous pauses of PC firing can also promote CbN neuron firing at rapid time scale (Gauck and Jaeger, 2000; Han et al., 2020). Such pauses in PC firing can arise from inhibition by the synchronous firing of electrically coupled MLIs (Lackey et al., 2023), CF synaptic activation of MLIs (Coddington et al., 2013; Szapiro and Barbour, 2007), and synchronous CF suppression of PC firing through ephaptic signaling (Han et al., 2020). Dynamic clamp studies with uniform-size inputs have shown that pausing a fraction of PC inputs reliably activates CbN neurons (Han et al., 2020). Here we used dynamic clamp studies and simulations to explore the effects of PC pauses on CbN neuron firing with uniform or nonuniform input sizes (Figure 8a). We first performed dynamic clamp experiments in which we paused the firing of 25, 50, and 100% of the PC inputs for 2 ms (Figure 8b). This is illustrated by raster plot showing that inputs from 50% of the PCs were briefly eliminated (Figure 8b, gray box). Pauses in PC firing led to large, short-latency increases in CbN neuron firing for both uniform inputs (Figure 8c; Han et al., 2020) and nonuniform inputs (Figure 8c and d). For nonuniform inputs, we compared the effects of pausing the firing of different-size ranges of PC inputs by pausing the smallest, largest, and middle range of inputs. The larger the size of the paused inputs, the larger the resulting increases in CbN neuron firing (Figure 8c and d), and the magnitude of firing rate increases were determined by the total amplitude of the paused inputs (Figure 8e). Pausing produced larger relative increases in CbN neuron firing for uniform-size PC inputs than that for nonuniform PC inputs (Figure 8c–e), which is likely a secondary consequence of the higher baseline firing frequencies for nonuniform inputs. Simulations where the firing of random combinations of PC inputs was paused for 2 ms produced similar results (Figure 8f). Therefore, brief pauses in PC firing evoke transient increases in CbN neuron firing, and the effects are determined by the total amplitude of paused PC inputs. We also found that CbN firing quickly returned to baseline levels after the transient increase following brief PC pauses (Figure 8c). Consequently, PC pauses only moderately increased the average firing rates of CbN neurons, as shown for a pause interval of 50 ms (Figure 8g). The pause interval did not influence the properties of the transient increases in CbN firing evoked by pauses, but did affect the magnitude of the changes in average firing frequency. For 20 ms, 50 ms, and 100 ms intervals between the 2 ms pauses where all PC spikes were eliminated, the average CbN firing frequency increase was 3.2-, 1.9-, and 1.5-fold for uniform inputs, and 2.1-, 1.5-, and 1.2-fold for nonuniform inputs, respectively. In summary, while the transient increase in CbN firing by PC pauses primarily depends on the total amplitude of paused PC inputs, the overall changes of CbN firing rate at longer time scales depend on both the total amplitude of paused PCs inputs and the pause intervals.

The influence of Purkinje cell (PC) pauses on the firing of cerebellar nuclei (CbN) neurons for uniform- and variable-size PC inputs.

Dynamic clamp experiments and simulations were conducted with either uniform or variable inputs in which different percentages of PC inputs were paused for 2 ms. (a) Normalized cumulative plots of conductances of PC inputs for a uniform (40 × 5 nS, dashed line) and a variable distribution (purple). (b) Raster plot shows example spiking for 40 PC inputs with spikes eliminated in a 2-ms period (gray region) in 50% of the PCs. (c) Normalized histograms showing the relative changes in CbN neurons firing for uniform (black) and variable (colored) PC inputs, when PC inputs were paused for 2 ms in 25 and 50% of the inputs. For variable inputs, three different populations of inputs were paused: the smallest, those in the middle of the distribution, and the largest inputs. Average values (bold) and individual cells (faint lines, n = 8) are shown. (d) The normalized CbN firing rates as a function of the percentage of paused inputs for uniform (black symbols fit with solid black line) and variable inputs (colored). (e) The normalized CbN firing rates as a function of the total amplitude of paused inputs for uniform (black) and variable inputs (colored). Fits are shown for uniform inputs (solid black line) and variable inputs (dashed line). (f) As in (e), but for simulations where different subsets of PC inputs were paused for 2 ms. (g) The normalized overall CbN firing rates as a function of the total amplitude of paused inputs for uniform (black) and variable (purple) inputs from a simulation where different subsets of PC inputs were paused for 2 ms every 50 ms. Fits are shown for uniform inputs (solid black line) and variable inputs (dashed purple line). The summary data are shown as the mean ± SEM.

Figure 8—source data 1

Dynamic clamp experiments and simulations for PC pauses.

https://cdn.elifesciences.org/articles/89095/elife-89095-fig8-data1-v1.xlsx

Discussion

Our finding that single PC to CbN inputs are highly variable in size has important implications for how PCs control the firing of CbN glutamatergic projection neurons (Figure 9). We find that individual PCs can strongly influence the firing of CbN neurons, and that PC to CbN synapses are well suited to both implement rate codes and precisely regulate the timing of CbN neuron firing (Figure 9a–c).

The main consequences of having variable-size Purkinje cell–cerebellar nuclei (PC-CbN) synaptic inputs.

(a) Left: schematic showing that CbN neurons are innervated by numerous PCs that have very different input strengths. Right: examples of unitary inhibitory conductances from PCs that range in size from very weak (green), medium-sized (blue), to strong (red) (adapted from Figure 1a–c). (b) CbN neuron firing rate as a function of input PC firing rate showing linear rate code conveyed by a weak (green) and a strong (red) PC input. (c) PC-CbN neuron cross-correlograms show the effects of a weak (green) and a strong (red) PC input. A single strong PC input is highly effective at controlling CbN neuron firing on rapid time scales. As expected, PCs transiently suppress CbN neuron firing, but there is also a preceding increase in CbN firing that arises from the statistics of PC firing and their refractory periods. (d) For the same total inhibition, the variability of the total inhibitory conductance is much larger for variable-size inputs than for uniform input sizes. (e) Compared to uniform-sized inputs, variable PC input sizes increase the baseline firing rate of CbN neurons as a result of elevated variability in the total inhibitory conductance (d). This has important secondary consequences. For variable PC input sizes, elevated basal CbN neuron firing rates reduce the ability of synchronizing or transiently pausing PC firing to promote CbN neuron firing.

Comparison to previous studies

The approach we took to examine the influence of PCs on CbN neuron firing is similar to that used in previous studies of the influence of PCs on CbN neuron firing (Gauck and Jaeger, 2000; Pedroarena and Schwarz, 2003; Person and Raman, 2012a), but we build upon those studies in several ways. As in earlier studies, we determined the properties of individual PC inputs and confirmed that they had very rapid kinetics (Person and Raman, 2012a). We provide a much more extensive characterization of input sizes (n = 157) than previous studies and show that the distribution of input sizes is skewed, with the largest inputs almost 100 times larger than the smallest inputs (Figure 1). The high Cl- concentration internal we used in our recordings provides superior stability and sensitivity to detect such variability in input size. We then used dynamic clamp to study how PCs control the firing of CbN neurons as in Person and Raman, 2012a, but instead of assuming uniform PC input sizes, we based the PC input sizes on the measured PC-CbN conductances (Figure 2). We first confirmed that many small desynchronized PC inputs are highly effective at suppressing CbN firing, and that synchrony effectively controls the timing and rate of PC firing for small uniform-size PC inputs (Person and Raman, 2012a). We then revealed that the variable PC input sizes have important implications for PC-CbN transmission. A single large input is functionally equivalent to many small, perfectly synchronized inputs. This allows PC inputs to influence the rate and timing of CbN firing as previously proposed (Person and Raman, 2012a), but without requiring a high degree of PC synchrony. We also extended previous studies by using realistic PC firing patterns in our dynamic clamp experiments. This allowed us to determine cross-correlograms for individual inputs and led to the finding that individual inputs have a surprisingly large effect on CbN firing. Previously it was shown that synchronous PC inputs initially suppress CbN neuron firing, which is followed by a subsequent increase in firing (Person and Raman, 2012a). Here we found that for spontaneously firing PCs, a period of strong disinhibition precedes suppression by a PC input (Figure 3). Lastly, we complemented our dynamic clamp studies with simple simulations that faithfully reproduced our experimental findings, which allowed us to explore the influence of different parameters.

The variable distribution of PC-CbN input sizes

We showed that there are developmental differences in the distributions of PC-CbN synapse sizes. In older animals (P23–32, Figure 1a–c and e–f, n = 83), the PC input sizes are more variable and there are more large inputs. This developmental transition indicates that PC-CbN synapses are refined during maturation, potentially because of plasticity mechanisms (Aizenman et al., 1998; Broersen et al., 2023; Morishita and Sastry, 1996; Ouardouz and Sastry, 2000). The implications of such variability in the strength of PC-CbN synapses in cerebellar computation can be shown by a recent study describing how the cerebellum encodes vestibular and neck proprioceptive information (Zobeiri and Cullen, 2022). Each PC encodes both vestibular and proprioceptive information, but some CbN neurons exclusively encode body motion and others encode vestibular information (Brooks and Cullen, 2009; Zobeiri and Cullen, 2022). It was proposed that this arises from the linear summation of converging PCs with different weights (Zobeiri and Cullen, 2022). Our observation of the variable distribution of PC-CbN inputs provides evidence for the requisite different weights of PC inputs.

The developmental changes in synaptic strengths we observe are reminiscent of other synapses. For retinal ganglion cell inputs to lateral geniculate nucleus, in young mice, numerous weak inputs innervate each LGN neuron, but in mature mice, this gives way to highly skewed input sizes that include some very strong inputs (Hooks and Chen, 2020; Jiang et al., 2022; Liang and Chen, 2020; Litvina and Chen, 2017). Excitatory inputs in the superior colliculus are refined in a comparable manner (Lu and Constantine-Paton, 2004). For inhibitory inputs to LSO neurons, in P1–5 mice there are many small and no large inputs, but by P9–14 the input sizes are highly skewed, and some inputs are very large (Gjoni et al., 2018; Kim and Kandler, 2003). In the cortex, input sizes are highly variable and are often approximated by a lognormal distribution (Buzsáki and Mizuseki, 2014; Dorkenwald et al., 2022; Melander et al., 2021). Thus, the distribution of PC to CbN input sizes we observe in P23–32 mice is similar in many ways to the distribution of input sizes of other types of synapses. Moreover, network simulations of balanced excitatory and inhibitory circuits have shown that adding variability to the distribution of input sizes disrupts the classical asynchronous irregular state of network activity and leads to rich, nonlinear responses (Brunel, 2000; Wardak and Gong, 2021).

Nonuniform PC input sizes elevate the basal firing rate of CbN neurons

An important consequence of having highly variable PC-CbN inputs is that it elevates the basal firing rates of CbN neurons (Figures 4, 6,, 9d and e). With the same total inhibition, uniform small PC inputs are particularly effective at suppressing the firing of CbN neurons. Nonuniform-size PC inputs elevate the CV of inhibition, increasing the fluctuations of membrane potential and elevating the firing of CbN neurons. Simulations based on an integrate-and-fire model with a passive cell and a well-defined firing threshold were able to replicate the findings of dynamic clamp experiments. This suggests that CbN neuron firing is effectively driven by the membrane potential fluctuations generated by PC inputs, despite the presence of many excitatory inputs and intrinsic conductances. The high basal firing rate of CbN neurons generated by nonuniform-size inputs helps explain the relatively high firing rates of CbN neurons in vivo amid strong PC inhibition (10–50 Hz) (Eccles et al., 1974; LeDoux et al., 1998; McDevitt et al., 1987; Rowland and Jaeger, 2008; Rowland and Jaeger, 2005; Thach, 1975; Thach, 1970; Thach, 1968). Membrane potential fluctuations have also been shown to be important to spike generation in other cell types (Kuhn et al., 2004; Tiesinga et al., 2000; Yarom and Hounsgaard, 2011).

Implications of nonuniform input sizes on rate codes

Our findings established that there is an inverse linear relationship between the firing rates of each PC input and the targeted CbN neuron (Figures 5 and 9b; Person and Raman, 2012b; Wu and Raman, 2017). The extent to which the firing rate of an individual PC input regulates the firing rate of a CbN neuron simply depends on the size of the input, and our simulations suggest that single PCs are capable of regulating the firing of a CbN neuron from 35 to 125 spikes/s (Figure 5g; for a 37 nS input varied from 0 to 160 spikes/s). Regardless of the sizes of the input that are varied, the changes in the firing rate of CbN neurons can be readily explained by the alterations in the total inhibitory conductance. It was not obvious prior to these experiments that there would be such a simple relationship between input size, input firing rate, and output firing rate, given that CbN firing rates depend on both the amplitude and CV of inhibition. In practice, fluctuations in the inhibitory conductance are quite large for variable-size inputs, and changing the rate of one input has a relatively small influence on the CV of the inhibition, so the magnitude of the inhibitory conductance dominates the effect. This model allows all different-size PC inputs to reliably convey a simple rate code, with the large PC inputs being particularly effective.

Implications of nonuniform inputs on temporal codes

Previously it was also thought that an individual PC input has a small influence on the spike timing of a CbN neuron, and that it is necessary to synchronize the firing of many PCs to precisely entrain CbN firing (Heck et al., 2013; Person and Raman, 2012a). Our cross-correlation analysis shows that this is not the case: large inputs (30 nS) eliminated CbN neuron firing for several milliseconds, and even small inputs (3 nS) transiently reduced CbN neuron firing by almost 40% (Figure 2). The sizes of PC inputs, the rapid kinetics of PC-CbN IPSCs (Person and Raman, 2012a), the high firing rate of PCs (Thach, 1968; Zhou et al., 2014), and the strong tendency of CbN neurons to depolarize (Raman et al., 2000) together shape the pattern of cross-correlograms and contribute to precise control of CbN neuron spike timing (Najac and Raman, 2015; Özcan et al., 2020). Thus, PC-CbN synapses are well suited to regulating both the rate and precise timing of CbN firing (Figure 9b and c).

Surprisingly, there was an excitatory response in CbN neuron firing preceding the suppression by PCs in the cross-correlogram of PC inputs and CbN neuron firing (Figure 2). This excitation is a consequence of the autocorrelation function in PC firing (Ostojic et al., 2009), and it will be most prominent for rapidly firing cells (Figure 3). When PCs fire at frequencies lower than 50 spikes/s, the excitatory component is small and lasts for several milliseconds, but when PCs fire at over 100 spikes/s, the excitatory component increases in amplitude and decreases in duration. This indicates the firing rates of PCs will affect the way they control the timing of CbN neuron firing. The observation that PCs in zebrin– regions of the cerebellum tend to fire faster than PCs in zebrin+ regions suggests different timing control of CbN neurons by zebrin+ and zebrin– PCs (Zhou et al., 2014). The effective excitation might also be present for other inhibitory synapses (Arlt and Häusser, 2020; Blot et al., 2016).

The observed distribution of PC to CbN synapse amplitudes, combined with the firing properties of PCs in vivo, allow us to make several predictions about PC-CbN neuron cross-correlograms in vivo. It is expected that PCs to CbN synapses are sufficiently large that prominent suppression should be apparent in cross-correlograms in vivo. The variability of PC-CbN synapses amplitudes suggests that there will be considerable variability in the extent and duration of spike suppression that is comparable to Figure 2. Single large inputs are expected to eliminate CbN neuron firing for several milliseconds, but smaller, weaker connections are expected to be more prevalent. Our findings also predict a disinhibitory component preceding the suppression that will be particularly large for rapidly firing PCs.

Implications for the influence of synchronous firing and synchronous pauses of PCs

Variable PC input sizes have important implications for how PC synchronous firing and pauses regulate the firing of CbN neurons. First, the relative influence of PC synchrony on the firing rate of CbN neurons is reduced because variable input sizes elevate the basal firing rates of CbN neurons (Figures 4, 6,, 9d and e). Second, the effect of PC synchrony depends upon which inputs are synchronized. Synchronizing 50% of the small inputs (corresponding to 31% of the total inputs) increased CbN firing by just 13%, whereas synchronizing the two largest PC inputs to a CbN neuron increased the CbN firing rate by 21% (Figure 6k and l). These findings suggest that a high degree of synchrony is not prerequisite for an appreciable influence. Therefore, studies that fail to detect a high degree of synchrony in PC simple spike firing in vivo (Herzfeld et al., 2023b) do not exclude the physiological relevance of PC synchrony in regulating CbN neurons. Similarly, we show that the relative influence of PC pauses is also reduced with variable-size inputs as a secondary effect of the high baseline firing rate, and is also determined by the total amplitude of paused inputs. Thus, the influence of PC inputs on their CbN targets depends upon the amplitudes of the different inputs, the firing patterns of the PCs, and the degree to which they are synchronized or paused.

Overall summary

Our initial findings that PC to CbN synapses are highly variable in size (Figure 9a), and that some are extremely large, have important implications for how PCs regulated the firing of CbN neurons and the output of the cerebellum. We found that individual large PC inputs strongly suppress the average firing rates of CbN neurons (Figure 9b) and also transiently silence them for several milliseconds (Figure 9c), thereby allowing them to convey both rate information and precise temporal information. An important characteristic of the strong temporal control of CbN neuron firing by individual PC inputs and the statistics of PC firing is that in PC-CbN cross-correlograms, inhibition is preceded by an apparent increase in CbN firing that looks like excitation. We also found that nonuniform input sizes elevate baseline firing rates by increasing the variability of the inhibitory conductance (Figure 9d and e). This has the secondary effect of reducing the efficacy of synchronous firing and synchronous pauses in elevating CbN firing.

Methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Strain, strain background (Mus musculus)C57BL/6Charles River Laboratories N/A
Chemical compound, drugNBQX disodium saltAbcam Ab120046
Chemical compound, drug(R,S)-CPPAbcam Ab120160
Chemical compound, drugStrychnine hydrochlorideAbcam Ab120416
Chemical compound, drugSR95531 (gabazine)Abcam Ab120042
Chemical compound, drugCGP 55845 hydrochlorideAbcam Ab120337
Chemical compound, drugQX-314 chlorideAbcam Ab120118
Software, algorithmIgor ProWaveMetrics (https://www.wavemetrics.com/order/order_igordownloads6.htm) RRID:SCR_000325 Version 6.37

Animals

C57BL/6 wild-type mice (Charles River Laboratories) of both sexes aged P10–P32 were used for acute slice experiments (for dynamic clamp experiments, P26–P32). All animal procedures were conducted in accordance with the National Institutes of Health and Animal Care and Use Committee guidelines and protocols approved by the Harvard Medical Area Standing Committee on Animals (animal protocol #1493).

Slice preparation

Mice were anesthetized with ketamine/xylazine/acepromazine and transcardially perfused with warm choline ACSF solution (34°C) containing in mM 110 choline Cl, 2.5 KCl, 1.25 NaH2PO4, 25 NaHCO3, 25 glucose, 0.5 CaCl2, 7 MgCl2, 3.1 Na pyruvate, 11.6 Na ascorbate, 0.002 (R,S)-CPP, 0.005 NBQX, oxygenated with 95% O2/5% CO2. To prepare sagittal CbN slices, the hindbrain was removed, a cut was made down the midline of the cerebellum and brainstem, and the halves of the cerebellum were glued down to the slicing chamber. Sagittal slices (170 μm) were cut using a Leica 1200S vibratome in warm choline ACSF (34°C). Slices were transferred to a holding chamber with warm ACSF solution (34°C) containing in mM 127 NaCl, 2.5 KCl, 1.25 NaH2PO4, 25 NaHCO3, 25 glucose, 1.5 CaCl2, 1 MgCl2, and were recovered at 34°C for 10 min before being moved to room temperature for another 20–30 min until recordings begin.

Electrophysiology

Whole-cell voltage/current-clamp recordings were performed on large neurons (>70 pF) in the lateral and interposed DN. These large cells are primarily glutamatergic projection neurons (Baumel et al., 2009; Turecek et al., 2016; Uusisaari et al., 2007).

For voltage-clamp recordings of unitary PCs inputs to CbN neurons, Borosilicate glass electrodes (1–2 MΩ) were filled with a high-chloride (ECl = 0 mV) internal containing in mM 110 CsCl, 10 HEPES, 10 TEA-Cl, 1 MgCl2, 4 CaCl2, 5 EGTA, 20 Cs-BAPTA, 2 QX314, and 0.2 D600, adjusted to pH 7.3 with CsOH. BAPTA was included to prevent long-term plasticity (Ouardouz and Sastry, 2000; Pugh and Raman, 2006; Zhang and Linden, 2006). The osmolarity of internal solution was adjusted to 290–300 mOsm. Series resistance was compensated up to 80%, and the calculated liquid junction potentials were around 5 mV and were left unsubtracted. CbN neurons were held at –30 to –40 mV. All experiments were performed at 34–35°C in the presence of 5 μM NBQX to block AMPARs, 2.5 μM (R,S)-CPP to block NMDARs, 1 μM strychnine to block glycine receptors, and 1 μM CGP 55845 to block GABABRs, with a flow rate of 3–5 ml/min. A glass monopolar stimulus electrode (2–3  MΩ) filled with ACSF was placed in the white matter between the CbN and the cerebellar cortex to activate PC axons. Minimal stimulation was used to determine the amplitudes of single PC-CbN inputs. The stimulus intensity was adjusted so that synaptic inputs were activated in approximately half the trials in a stochastic manner. The sample size was achieved by performing as many recordings as possible within a limited period of time.

For dynamic clamp experiments, Borosilicate glass electrodes (3–4 MΩ) were filled with an internal containing (in mM) 145 K-gluconate, 3 KCl, 5 HEPES, 5 HEPES-K, 0.5 EGTA, 3 Mg-ATP, 0.5 Na-GTP, 5 phosphocreatine-tris2, and 5 phosphocreatine-Na2, adjusted to pH 7.2 with KOH. The osmolarity of internal solution was adjusted to 290–300 mOsm. Series resistances were less than 15 MΩ and were not compensated. Voltages were corrected for a liquid junction potential of 10 mV. Cells were held at –65 to –75 mV between trials. All experiments were performed at 34–35°C in the presence of 5 μM NBQX, 2.5 μM (R,S)-CPP, 5 μM SR 95531 (Gabazine), and 1 μM strychnine to block most synaptic transmission.

Dynamic clamp experiments

The total inhibitory conductance (200 nS) from all converging PCs in each CbN neuron was based on previous estimation of 40 PCs with a size of 5 nS (after depression) (Person and Raman, 2012a). Uniform-size inputs are studied in Figures 3, 4c–g,6. For the uniform inputs with different firing frequencies used in Figure 3, the number of the inputs was adjusted so that the total inhibitory conductances were the same for all groups (after depression, 12 × 20 nS at 49 Hz, 9 × 20 nS at 83 Hz, 6 × 20 nS at 122 Hz, and 9 × 20 nS for Poisson inputs). For the uniform inputs with different sizes used in Figure 4c–g, the input sizes varied from 2.5 nS to 40 nS (after depression), and the number of inputs was varied to maintain a total inhibitory conductance of 200 nS. Forty PC inputs with a size of 5 nS were used in Figure 6.

Dynamic clamp experiments with different-size inputs were performed in Figures 2a–e4h–i6,8. To generate different-size inputs reflecting the distribution of the unitary PC-CbN input conductances measured in P23–32 animals, the amplitudes of the unitary conductances were corrected for depression (× 0.4) and the effects of high Cl- internal (scale down by a factor of 2.3) (Bormann et al., 1987; Gjoni et al., 2018; Sakmann et al., 1983). In experiments where the effects on average firing frequency were determined (Figure 4h and i) and in Figure 8, input sizes were randomly drawn from the experimentally determined distributions of input sizes (Figure 2f, red) until the total inhibitory conductance reached 200 nS. In experiments where the spike-triggered averages were to be determined, we used a simplified distribution. We estimated the simplified distribution by computing the ratio and weighted averages for different ranges of input sizes. In Figures 2a–e5, we approximated the distribution with small (16 × 3 nS), medium (10 × 10 nS), and large (2 × 30 nS) inputs. Approximating the small inputs with 16 inputs of the same size made it possible to determine the spike-triggered average with a much better signal to noise than if the inputs were different sizes. We took a similar approach in Figure 6, but we adjusted the number and size of medium-size inputs to make it easier to assess the effects of 50 and 25% synchrony (16 × 3 nS small, 8 × 12 nS medium, and 2 × 30 nS large inputs).

We based the timing of PC firing (Figure 2—figure supplement 1) on in vivo recordings from our previous study (Han et al., 2020). The average firing frequency ranges from 61 spikes/s to 180 spikes/s (Figure 2—figure supplement 1a and b). The distribution of ISIs of firing in the 10 PCs was well approximated with lognormal functions (Figure 2—figure supplement 1c), and the relationship between the mean and the standard deviation (sd) of lognormal distributions fits was well approximated with a linear function: sd = –0.00154 + 0.583 * mean (Figure 2—figure supplement 1d). Therefore, we used this linear function to calculate the σ of a desired firing frequency (1/mean) and generated an artificial ISI distribution of PC firing based on lognormal function with the designated mean and sd. The ISI distributions of PC firing used in Figures 2 and 6 were artificial lognormal distributions with a firing frequency of 83 Hz (Figure 2—figure supplement 1c) and 80 Hz (Figure 2—figure supplement 1e and f), respectively. The three ISI distributions of PC firing with different firing frequencies used in Figure 3 are from in vivo recordings (Figure 2—figure supplement 1a and b), and the Poisson distribution without a refractory period was generated with an exponential function aiming at a desired frequency (80 Hz). The ISI distribution of PC firing used in Figure 4 is from in vivo recordings with a firing frequency of 100 Hz (Figure 2—figure supplement 1a and b). The ISI distributions of PC firing with different firing frequencies used in Figure 5 are artificial lognormal distributions generated from the μ and σ of desired frequencies. The approach is shown for artificial lognormal distributions of 40, 80, 120, and 160 spikes/s (Figure 2—figure supplement 1e and f). Individual PC spike trains were generated by randomly drawing ISIs from the designated ISI distribution, and spike trains from each PC were combined as a final spike train with the inputs from all PCs. This spike train was then convolved with a unitary PC input with a rise time of 0.1 ms and a decay time of 2.5 ms (Khan et al., 2022). The reversal potential for inhibitory conductances was set at –75 mV (after correcting for junction potential).

Excitatory conductances were based on the AMPA component of mossy fiber (MF) excitatory postsynaptic currents (EPSCs) characterized in previous studies (Wu and Raman, 2017), with a rise time of 0.28 ms, a decay time of 1.06 ms, and an amplitude of 0.4 nS (reflecting depression). They estimated that 20–600 MFs converged on each CbN neuron, with unknown firing frequencies, so the excitatory conductances were relatively unconstrained. We adjusted the frequency of MF EPSCs so that the basal firing rate of CbN neurons was maintained at 20–40 Hz in the presence of the inhibitory conductance. The average baseline excitatory conductance was 20–30 nS.

To avoid a drastic increase in spike frequency and the following adaptation of CbN neurons resulting from big changes in the variability of conductances, we ramped up the CV of the inhibitory conductances in the beginning of each trial so that the firing rate of CbN increased gradually from the hyperpolarization state. Each conductance was repeated in the same neuron for 3–4 trials as technical replicates. For synchrony experiments, PC inputs were synchronized 100% in their spike times. Therefore, synchronizing 10 5 nS inputs is equivalent to having one big input with a size of 50 nS.

Analysis

Recordings were obtained using Multiclamp 700B (Molecular Devices), sampled at 50 kHz and filtered at 4 kHz, and collected in Igor Pro (WaveMetrics). Dynamic clamp recordings were performed with an ITC-18 computer interface controlled by mafPC in Igor Pro (WaveMetrics). Data were analyzed using custom-written scripts in MATLAB (MathWorks) and Igor Pro (WaveMetrics). Autocorrelation and cross-correlation analyses were performed by generating accumulative histograms of spikes distribution within a 20 ms time window centering all spikes from the reference file (self-reference for autocorrelation, and PCs spike times for cross-correlation), and normalized by the total spikes number of the reference file and the bin size of the histograms (i.e., the Δt). All summary data are shown as the mean ± SEM unless otherwise indicated. The distributions of unitary PC input sizes in young and juvenile animals in Figure 1f were compared with a Kolmogorov–Smirnov test. The unpaired t-test was performed with Welch’s correction.

Simulations

Simulations were performed (Figures 2f–h4j8f, g) to complement dynamic clamp experiments. A point-conductance, single-compartment model was generated to model the CbN neuron and its synaptic inputs. The membrane potential (V) of a CbN neuron with a membrane capacitance (Cm) follows the equation

CmdVdt=gEtVE-V+gItVI-V+gLVL-V

where VE=0 mV and VI=75 mV are the excitatory and inhibitory reversal potentials, and gEt and gIt are time-dependent excitatory and inhibitory conductances. The leak was modeled by a constant leak conductance gL with a reversal potential VL . A spike occurs when the membrane potential V of the model neuron reaches the threshold θ, at which point there is a refractory period of 2 ms during which the model neuron remains inactive, and the neuron is then reset to Vr . gEt and gIt were generated as described for dynamic clamp experiments. Simulations were performed using the BRIAN simulation environment in Python. The simulation code is available on GitHub at https://github.com/asemptote/PC-DCN-different-size-inputs (Wardak, 2023).

In Figure 2f–h, simulations were performed for 10 cells with input distributions drawn randomly from the corrected empirical input sizes (Figure 2f, red) such that the total conductance was 200 nS. Each cell was run for 160,000 s and cross-correlograms were computed for each input. The parameters used were Cm=50 pF, 20,000 excitatory events per second, θ=-50 mV, Vr=-60 mV, gL=8.8 nS, and VL=-40 mV. Inhibitory spike trains were randomly chosen by drawing ISIs from the empirically fitted lognormal distribution such that the mean rate was 83 Hz. These were repeated for input distributions drawn from a rescaled version of the input sizes with scaling factors of 0.5 (Figure 2—figure supplement 3a) and 1.5 (Figure 2—figure supplement 3b).

In Figure 4j, simulations were performed for 100 cells with input distributions drawn randomly from the corrected empirical input sizes, along with 200 cells with varying numbers of uniform-size inputs such that the total conductance was 200 nS. Each cell was run for 10 s, and the firing rates and inhibitory conductance CV were recorded. The generated inhibitory conductance waves were saved for use in experiments in Figure 4i. The parameters used were Cm=200 pF, 23,650 excitatory events per second, θ=-50 mV, Vr=-60 mV, gL=5 nS, and VL=-10 mV. For the cells with varying input sizes, inhibitory spike trains were randomly chosen by drawing ISIs from the empirically fitted lognormal distribution such that the mean rate was 80 Hz, while for the cells with uniform-size inputs, the ISIs were drawn from an in vivo recording with a firing frequency of 100 Hz (Figure 2—figure supplement 1).

In Figure 5, simulations were performed for 10 cells with input distributions drawn randomly from the corrected empirical input sizes such that the total conductance was 200 nS. For each input to each cell, 16 simulations were run for 100 s where the rate of a given input was varied between 0 and 160 Hz while keeping the other inputs at a fixed rate (80 Hz). The average firing rate and mean inhibitory conductance were recorded. These were then repeated with input scaling factors of 0.5 (Figure 5—figure supplement 1A) and 1.5 (Figure 5—figure supplement 1B). Parameters used were as in Figure 4. Inhibitory spike trains were randomly chosen by drawing ISIs from the empirically fitted lognormal distribution such that the mean rate was 80 Hz.

In Figures 6g–j and 7e and f, simulations were performed for 10 cells with input distributions drawn randomly from the corrected empirical input sizes such that the total conductance was 200 nS. For each cell, 100 simulations were run for 1600s where a random subset of the input sizes was synchronized, and the firing rate (Figure 6g–j) and cross-correlation statistics (Figure 7e and f) were obtained as in Figure 2. Forty additional simulations were run in this manner for a cell with 40 uniform-size inputs, synchronizing a different number of inputs in each simulation. Parameters used were as in Figure 5.

In Figure 8 and g, simulations were performed with an input distribution drawn randomly from the corrected empirical input sizes such that the total conductance was 200 nS. Each cell was run for 16,000 s where spiking was eliminated from one of 100 random subsets of inputs for 2 ms in each 20 ms time period. The normalized peak CbN firing rate was determined by the averaged increase in firing relative to the baseline defined as the average rate over the 5 ms periods before the pause. The peak time used for each cell was determined by the histogram corresponding to 100% of the inputs paused. The parameters used were Cm=70 pF, 23,650 excitatory events per second, θ=-50 mV, Vr=-60 mV, gL=20 nS, and VL=-49.9 mV. A refractory period of 1 ms was used. Inhibitory spike trains were randomly chosen by drawing ISIs from the empirically fitted lognormal distribution such that the mean rate was 80 Hz. These simulations were repeated for a cell with uniform-size inputs where the parameters were kept the same apart from an excitatory rate of 25,000 events per second.

Code availability

The simulation code is available on GitHub at https://github.com/asemptote/PC-DCN-different-size-inputs.

Data availability

All data used in this study are originally generated, with the exception of some of the PC-CbN unitary input sizes in Figure 1d (Turecek et al., 2017) and Figure 1e (Khan and Regehr, 2020), and PC spike times in Figure 2 - figure supplement 1 (Han et al., 2020). The data for the new recordings of PC-CbN unitary inputs in Figure 1 and the data for dynamic clamp studies and simulations in Figure 2 to Figure 8 can be found in the source data for the respective figure. Code availability: The simulation code is available on GitHub at https://github.com/asemptote/PC-DCN-different-size-inputs (copy archived at Wardak, 2023).

References

  1. Conference
    1. Sakmann B
    2. Bormann J
    3. Hamill OP
    (1983) Ion transport by single receptor channels
    Cold Spring Harbor Symposia on Quantitative Biology. pp. 247–257.
    https://doi.org/10.1101/sqb.1983.048.01.027

Decision letter

  1. Sacha B Nelson
    Senior and Reviewing Editor; Brandeis University, United States

Our editorial process produces two outputs: i) public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Implications of variable synaptic weights for rate and temporal coding of cerebellar outputs" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by Sacha Nelson as the Reviewing and Senior Editor.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

As you can see from the comments below, most of the concerns raised by reviewers can be addressed through textual changes. Both reviewers felt that the scholarship of the manuscript could be improved and that additional quantitative summary and statistical testing was warranted (for example the degree of increase of skewness).

There was less clear cut agreement over the conductance measurements. The concern was raised that the high chloride used distorts this measurement in ways that were not very convincingly controlled or justified. Possible solutions to this issue raised included:

1) Redoing a subset of the measurements with more physiological chloride

2) Providing a more rigorous analysis of the overestimate of the conductance (i.e. a more detailed justification of the correction factor used.

Reviewer #1 (Recommendations for the authors):

I detail some of the main problems with this study below. Most of these issues are fixable.

1. Scholarship issues. Many of the claims about background knowledge in the paper lack citations, and many of these "claims" are strawmen. A few, but not exhaustive examples:

a. "Previously it was thought that PC-CbN synapses are uniformly small and that single PC inputs have little influence on the firing of CbN neurons." This statement is made repeatedly in the abstract, introduction, and results, yet no citation is provided but is essential. Undermining the point of the authors, the Person and Raman 2012 study shows a skewed distribution of unitary synaptic strengths of PCs. It also shows that a 10 nS input, equivalent to one input, is sufficient to suppress firing of a nuclear cell consistently (Figure S2 in that study). Person and Raman 2012 b discuss the potential anatomical underpinnings of skewed synaptic strengths. Here is the relevant text in 2012b: "Perhaps more importantly, as the authors state explicitly, their "apparently excessive" numerical estimate for average convergence is based on the simplifying assumption that each Purkinje cell ramifies to the same extent on all nuclear cells. They point out, however, that Purkinje neurons consistently make non-uniform contacts onto nuclear cells, occasionally "erupting" into "numerous (~50) boutons all in contact with the same cell body." Ramón y Cajal similarly observed that each Purkinje neuron axon formed six to eight "nests" onto as many cells (Chan-Palay, 1977). Such dense terminal perisomatic plexes are also mentioned in other descriptions of Purkinje axonal arbors (Chan-Palay, 1977; Bishop et al., 1979; De Zeeuw et al., 1994; Wylie et al., 1994; Teune et al., 1998; Sugihara et al., 2009), suggesting that they are a common specialization of Purkinje neuron terminals. Palkovits et al. (1977) propose that each Purkinje neuron may have 3-6 primary targets, while providing weak input to many more, and conversely that nuclear cells may only receive strong somatic input from "several"-that is, a relatively small number of- Purkinje cells (see also Sugihara et al., 2009)."

b. The either-or nature of the synchrony vs rate coding hypotheses is not backed up by a citation "Leading theories" which ones?

c. "Individual PCs are thought to have limited influence on CbN firing" this is another statement that is not backed up by any literature I am familiar with. Indeed Person and Raman show in a supplemental figure that the equivalent of single inputs effectively inhibit CbN cells.

d. Paragraph 2 Introduction: "a rate code does not apply" – this is sloppy language that does not appropriately frame the state of the field. There are many possible explanations for deviations from inverse firing rate relationships, one of which is synchrony, but others include diverse excitatory afferents to the nuclei and cortex, neuromodulatory influence or other mechanisms.

2. Some of the main experimental claims are either not novel, mischaracterize previous literature, or lack rigor themselves:

A. "The distribution of input sizes we observed in P10-20 animals was similar to previous studies (P13-29, n=30) (Person and Raman, 2012a), and the skewed distribution of input sizes was only apparent when many individual inputs were characterized in P23-32 mice." This conclusion is unconvincing. There is skew in both the young and the old populations, and if it's more skewed in the old is it more skewed than the overlapping age population shown before. I find this language borderline disingenuous. Moreover the cited study shows a skewed distribution of synaptic strengths.

B. The estimate of the synaptic conductances is problematic. The use of high Cl- interferes with the measurement, then they engage in some hand-waving arguments to come to an unconstrained estimate of 3-30 nS. This is not well constrained and will introduce unnecessary confusion into the literature.

C. There is too little statistical and quantitative information in the main text. For example, the first full paragraph of page 4 states: "All inputs had effects on the timing of CbN neuron firing, but larger inputs had much bigger effects. As shown in the cross-correlograms of input timing and CbN neuron spiking, small inputs produced a small transient decrease in CbN neuron spiking, while medium sized inputs strongly reduced CbN neuron spiking, and large inputs transiently shut down CbN neurons for approximately 2 ms. No average or statistics are provided in the subsequent text either. This style makes it difficult to grasp the magnitude of any of these differences, and in any event, it seems obvious that graded inhibition would produce graded effect sizes.

D. The text states that the refractory period is correlated with the amount of CbN firing and refers to Figure 3. I see no evidence that a correlation was computed from this figure, nor are the statistics of any correlation reported in the text. That said, the potential relationship between CbN firing and PC refractoriness is interesting, but poorly explained in the text. A clearer mechanistic insight would be valuable, beyond "This excitation is a consequence of the autocorrelation function in PC firing", which does not shed much light on the matter. It might be helpful to show the raster plots of spike-triggered inputs to better decipher relationships here. (Rasters for Figure 2D would also be valuable).

E. For the data in Figure 5, what accounts for the very high firing rates of CbN neurons (200 spikes/s)? The plots in Figure 5 could be more intuitive with some work.

Reviewer #1 (Recommendations for the authors - second review):

The authors have thoroughly addressed my concerns. The revision beter frames the findings, both in terms of novelty and significance than the original submission. It will be of value to the field.

Reviewer #2 (Recommendations for the authors):

Main concerns for the authors:

The manuscript is very dense and constitute an important dataset for the field. The use of dynamic clamp is very interesting and enhances experimental options. I have however important concerns that I think could help clarify the manuscript:

– My first concern is the choice of the distribution with a different number of small, medium and large input size. I understand that it is necessary to keep the total conductance constant, but it raises a doubt on the relevance of the data in Figures 2 and 4c for example. Indeed, a few large inputs could be considered as many small inputs synchronized. Similarly, few large inputs obviously lead to a high CV by construction, while many small inputs reduce the CV. What is really tested in all these different protocols is a bit confusing.

– How is played this distribution on PC inputs? In episodes or continuously?

– A set of in vivo PC discharge is used, but where is it coming from?

– The correction made to approximate conductance is somehow arbitrary. I would tone down the assumption that this is the first demonstration that PC-DCN inputs are large and variable. Indeed, in preceding studies, authors already suggested that PC-DCN inputs are quite large in the recordings, albeit with different recording conditions.

In Person and Raman, Nature 2012 "The high unitary amplitude suggests that individual Purkinje-to-nuclear contacts are quite strong", referring to an article that should be cited here as well:

Pedroarena, C. M. and Schwarz, C. Efficacy and short-term plasticity at GABAergic synapses between Purkinje and cerebellar nuclei neurons. J. Neurophysiol. 89, 704-715 (2003).

Actually the interplay between rate coding and temporal coding has been also addressed in:

Özcan, O. O., Wang, X., Binda, F., Dorgans, K., De Zeeuw, C. I., Gao, Z., Aertsen, A., Kumar, A., and Isope, P. (2020). Differential Coding Strategies in Glutamatergic and GABAergic Neurons in the Medial Cerebellar Nucleus. The Journal of Neuroscience, 40(1), 159-170.

– I am not a specialist in modeling, but I would imagine that since CbN neurons have active conductances (Ih, Cav3 channels), it is surprising that a simple integrate and fire reproduce fully CbN spiking behavior. The choice of model could be more discussed. In the MS, the discussion around rebound firing is also very limited.

– The concept of the refractory period accounting for the excitatory effect preceding PC inputs is not well explained, and the simple hypothesis that spikes were elicited at the time of the lowest inhibitory conductance might be enough.

– Figure 4 The frequency observed in CbN neurons seems much higher than in any other study. Similarly, what is the actual firing rate of neurons recorded in Figure 5 without inhibitory inputs?

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Implications of variable synaptic weights for rate and temporal coding of cerebellar outputs" for further consideration by eLife. Your revised article has been evaluated by Sacha Nelson (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

Some additional modification of the text is warranted to address the remaining concern of Reviewer #2. In addition, a third reviewer was consulted who raised some of the same concerns raised earlier about statistical analyses of the underlying distribution and the validity of the extrapolation to measurements made in symmetrical chloride. Perhaps some of the points raised to address these issues in the previous reply to reviewers could be included in the manuscript to shore up these points.

Reviewer #2 (Recommendations for the authors):

The authors have definitely improved their manuscript, results are better explained and discussed. Based on my previous review, I would have two remaining requests:

– Related to Fig2g and my question about rebound firing, the authors answered

"With regard to the issue of rebound firing, under our experimental conditions, we see no indication of rebound firing". In Fig2g, it seems that a rebound firing occurs for large inputs. It could be important to mention it as synchronization (e.g. by climbing fiber inputs) would also give the same outcome.

– Related to the discussion about input size vs synchrony, uniform vs nonuniform inputs, I think that a summary figure would help catch the take-home message.

Reviewer #3 (Recommendations for the authors):

In Wu et al., the authors describe how inputs of variable synaptic weights influence rate and temporal output coding in neurons of the cerebellar nuclei. This is valuable research that advances our understanding of this important question within the field of cerebellar encoding, with experiments that give solid (or mostly solid) evidence for their claims.

In this manuscript, the authors focus on an important question in the cerebellar field: how do high-firing inhibitory Purkinje cells influence the output of neurons in the cerebellar nuclei (CbN) which are themselves spontaneously active? They use classical techniques: whole-cell electrophysiology combined with extracellular stimulation and dynamic-clamp experiments. They identify that there is a wide range of input strengths made onto CbN neurons from Purkinje cells and explore what this diversity of input strength might mean for CbN neuron output. Using dynamic clamp, they parametrically alter input synchrony, comparing how CbN output is altered when they alter inputs of different sizes. Some of their findings are counterintuitive, as they show that right before a strong inhibition of CbN firing caused by Purkinje cell activation, there is a transient enhancement of firing which is due to Purkinje cell refractory period. Other findings are more straight-forward – for e.g. they show that large inputs can strongly modulate firing and thus there is an inverse rate code. They look at synchrony and show that the impact on CbN firing depends on input variability, something that has not previously been addressed in detail. These findings are interesting and add to our understanding of how Purkinje cell synapses may influence CbN output.

1. One of the main claims of the paper is that they uncover that there is variable Purkinje cell input to CbN neurons. However, this finding is based on recordings with symmetrical Cl- internal that is not physiological. They use a calculation based on the literature (based on other synapses that may have different channel properties) to adjust their measurements to physiological values, but the appropriateness of this adjustment is not evident. This makes me question how solid this finding is, as there is no experimental validation. In fact, it is not entirely clear how much their finding differs from previous findings – they say that they show a different distribution, but they do not verify it is different statistically. I think they need to either strengthen their experimental evidence for this finding (which I acknowledge would be a challenging thing to do) or weaken their claims.

2. The authors talk about Purkinje cell autocorrelation as being the cause of the modulation of firing before the Purkinje cell spike reduces firing in CbN neurons. I find this section hard to follow, and given the general readership of eLife, I think the authors could do a better job explaining this in a more accessible way.

https://doi.org/10.7554/eLife.89095.sa1

Author response

Essential revisions:

Reviewer #1 (Recommendations for the authors):

I detail some of the main problems with this study below. Most of these issues are fixable.

1. Scholarship issues. Many of the claims about background knowledge in the paper lack citations, and many of these "claims" are strawmen. A few, but not exhaustive examples:

a. "Previously it was thought that PC-CbN synapses are uniformly small and that single PC inputs have little influence on the firing of CbN neurons." This statement is made repeatedly in the abstract, introduction, and results, yet no citation is provided but is essential. Undermining the point of the authors, the Person and Raman 2012 study shows a skewed distribution of unitary synaptic strengths of PCs. It also shows that a 10 nS input, equivalent to one input, is sufficient to suppress firing of a nuclear cell consistently (Figure S2 in that study). Person and Raman 2012 b discuss the potential anatomical underpinnings of skewed synaptic strengths. Here is the relevant text in 2012b: "Perhaps more importantly, as the authors state explicitly, their "apparently excessive" numerical estimate for average convergence is based on the simplifying assumption that each Purkinje cell ramifies to the same extent on all nuclear cells. They point out, however, that Purkinje neurons consistently make non-uniform contacts onto nuclear cells, occasionally "erupting" into "numerous (~50) boutons all in contact with the same cell body." Ramón y Cajal similarly observed that each Purkinje neuron axon formed six to eight "nests" onto as many cells (Chan-Palay, 1977). Such dense terminal perisomatic plexes are also mentioned in other descriptions of Purkinje axonal arbors (Chan-Palay, 1977; Bishop et al., 1979; De Zeeuw et al., 1994; Wylie et al., 1994; Teune et al., 1998; Sugihara et al., 2009), suggesting that they are a common specialization of Purkinje neuron terminals. Palkovits et al. (1977) propose that each Purkinje neuron may have 3-6 primary targets, while providing weak input to many more, and conversely that nuclear cells may only receive strong somatic input from "several"-that is, a relatively small number of- Purkinje cells (see also Sugihara et al., 2009)."

We thank the reviewer for the comment and have rephrased our statements to be more precise. Person and Raman made a simplifying assumption when they used 40 uniform size PC inputs in their dynamic clamp studies (Person and Raman 2012a). This was a reasonable strategy, based on the distribution of input sizes they observed (Person and Raman 2012a), which is less skewed compared to what we observed when studying a larger number of inputs at different developmental stages (see response to Review #1, point 2 below). In Person and Raman 2012b, there was a nice discussion of the anatomical studies suggesting heterogenous PC boutons on CbN neurons, but it was difficult to relate such anatomical studies to PC input strength. Here we want to point out that the heterogeneity of PC-CbN connections and its implications on PC-CbN transmission have not been addressed experimentally, and previous studies using dynamic clamp were based on 40 uniform size inputs (Han et al., 2020; Person and Raman, 2012; Wu and Raman, 2017). All of these studies (including our own) made the same simplifying assumption and ignored the issue of different size inputs.

McDevitt et al., 1987 is often cited to address whether individual PC can regulate CbN neuron firing, as by Person and Raman 2012b: “In decerebrate cats, for example, spontaneous activity of such Purkinje-nuclear pairs is not correlated, and firing rate modulation in response to periodic sensory stimuli is not consistently reciprocal, leading to the conclusion that single Purkinje afferents are insufficient to regulate the spiking behavior of nuclear cell targets (McDevitt et al., 1987).” In our study, we find that this conclusion is not necessarily true since large PC inputs powerfully influence the firing of CbN neurons, which supports the importance of studying the consequences of different input sizes. We predicted similar observations to be made in PC-CbN neuron cross-correlograms in vivo.

The reviewer also referred to Figure S2 of Person and Raman 2012a, in which they studied how the time-course of single IPSC inputs influences the firing activity of CbN neurons, in the absence of other inhibitory or excitatory inputs. That experiment was not designed to show the strength of single PC input, and more appropriate experiments (e.g. testing the inhibition by single PC input in the presence of many other inhibitory and excitatory inputs) were not performed. Furthermore, although there were dynamic clamp experiments in which 40 asynchronous PC inputs drove CbN neuron firing, the effects of individual inputs were not analyzed (Person and Raman 2012a). Instead, the paper emphasized the effects of synchrony: “With partial synchrony, nuclear neurons time-lock their spikes to the synchronous subpopulation of inputs, even when only 2 out of 40 afferents synchronize” (Person and Raman 2012a). Therefore, we do not think the effects of individual PC inputs have been characterized in previous studies.

b. The either-or nature of the synchrony vs rate coding hypotheses is not backed up by a citation "Leading theories" which ones?

We fully agree with the reviewer that rate and time codes are not mutually exclusive. We have modified the manuscript to clarify this point. However, there is controversy in the field, and we think it is appropriate to point this out. Below are a few examples:

“After all, years of research have shown that much of the information sent to motor areas from the cerebellum is conveyed by a rate code — a modulation in the firing frequency of neurons in which the precise timing of the spikes is irrelevant because the rate is averaged over tens to hundreds of milliseconds… So, at least in theory, this would allow inputs from different groups of Purkinje cells to become synchronized for brief periods of time, and, when a high degree of temporal precision is required (for instance, at the beginning or end of a movement), to control the exact timing of spikes in nuclear neurons. To control other aspects of movement, such as amplitude or speed, the cerebellum might switch Purkinje cells back to the asynchronous mode and modulate their firing frequency up or down, thus regulating the firing rate of nuclear neurons.” (Medina and Khodakhah, 2012).

“Through an ingenious set of experiments, Person and Raman describe one such computational principle: how a (nuclear) neuron decodes the temporal structure of its synchronous (Purkinje cell) inhibitory inputs while ignoring the asynchronous ones… But a confounding limitation of such temporal decoding is that the probability of firing entrained spikes is reduced so much that nuclear neurons cannot ratecode — that is, the persistent activity of the asynchronous inputs reduces the probability of a nuclear neuron firing entrained spikes to such an extent that it can no longer encode the firing rate of its synchronous inputs in its own firing rate. This is problematic, because it has been known for more than 40 years that the activity rates of individual nuclear and Purkinje neurons correlate with movement and thereby with each other.” (Medina and Khodakhah, 2012).

“Information transmission in the brain can occur via changes in firing rate or via the precise timing of spikes. Simultaneous recordings from pairs of Purkinje cells in the floccular complex reveals that information transmission out of the cerebellar cortex relies almost exclusively on changes in firing rates rather than millisecond-scale coordination of spike timing across the Purkinje cell population.” (Herzfeld et al., 2023)

Such controversy can also be found in many other studies, as concluded in the introduction (Abbasi et al., 2017; Brown and Raman, 2018; Cao et al., 2012; Gauck and Jaeger, 2000; Heck et al., 2013; Herzfeld et al., 2023a; Hoehne et al., 2020; Hong et al., 2016; Medina and Khodakhah, 2012; Payne et al., 2019; Person and Raman, 2012a, 2012b; Sedaghat-Nejad et al., 2022; Stahl et al., 2022; Sudhakar et al., 2015; Walter and Khodakhah, 2009; Wu and Raman, 2017).

Alternatively, Person and Raman 2012a have promoted a view that is compatible with a combination of a rate code and a synchrony code, or more generally that timing and rate can both matter.

We have tried to provide a more nuanced discussion of the issues of synchrony, timing and rate to address the reviewer’s concerns.

c. "Individual PCs are thought to have limited influence on CbN firing" this is another statement that is not backed up by any literature I am familiar with. Indeed Person and Raman show in a supplemental figure that the equivalent of single inputs effectively inhibit CbN cells.

Please refer to our response to point a above.

d. Paragraph 2 Introduction: "a rate code does not apply" – this is sloppy language that does not appropriately frame the state of the field. There are many possible explanations for deviations from inverse firing rate relationships, one of which is synchrony, but others include diverse excitatory afferents to the nuclei and cortex, neuromodulatory influence or other mechanisms.

We have revised the text to clarify this point.

2. Some of the main experimental claims are either not novel, mischaracterize previous literature, or lack rigor themselves:

We do not agree with this general statement. We have responded to the specific issues raised by the reviewer below.

A. "The distribution of input sizes we observed in P10-20 animals was similar to previous studies (P13-29, n=30) (Person and Raman, 2012a), and the skewed distribution of input sizes was only apparent when many individual inputs were characterized in P23-32 mice." This conclusion is unconvincing. There is skew in both the young and the old populations, and if it's more skewed in the old is it more skewed than the overlapping age population shown before. I find this language borderline disingenuous. Moreover the cited study shows a skewed distribution of synaptic strengths.

The distribution we observed in P23-32 mice is very different from that shown in a previous study (Person and Raman, 2012a). We therefore feel that our description of the results was fair and reasonable, and it was not “disingenuous.”

Although we have not changed the text regarding this issue, we are open to specific suggestions, provided it is consistent with the differences in the skewness of the distributions in Response Figure 1. The authors also provided a figure (Response Figure 1, that is not reproduced here) in which distributions of input sizes were directly compared for (a) 30 inputs from p13-29 mice (Person and Raman, 2012a, Fig. 1b lower), and (b) 83 inputs from p23-32 mice from this paper (Fig. 1e, corrected for high chloride internal). This direct comparison made the differences in the skew of the distributions obvious.

B. The estimate of the synaptic conductances is problematic. The use of high Cl- interferes with the measurement, then they engage in some hand-waving arguments to come to an unconstrained estimate of 3-30 nS. This is not well constrained and will introduce unnecessary confusion into the literature.

The reviewer’s concern provides us with another chance to address how we constrain the input sizes based on several factors. We used a high Cl- internal to determine the distribution of input sizes because it provided superior stability, better access resistance and higher sensitivity compared to using a low Cl- internal. We scaled down the conductance amplitudes by 2.3, guided by previous findings that the measured chloride conductances were 2-3 times larger for high chloride vs. physiological chloride internals (Bormann et al., 1987; Gjoni et al., 2018; Sakmann et al., 1983). Modest errors in the correction from high to physiological chloride concentrations will not affect our major conclusions but might have small quantitative effects on the influence of single inputs. We performed additional simulations to address this issue (Figure 2 —figure supplement 3; Figure 5 —figure supplement 1).

We also took into account the depression that occurs during high frequency activation of PC inputs (Turecek et al., 2017, 2016; Telgkamp and Raman, 2002; Pedroarena and Schwarz, 2003, reduced to 40% of the initial response), as in previous dynamic clamp studies (Han et al., 2020; Person and Raman, 2012; Wu and Raman, 2017). Based on the corrected distribution of input sizes, we took two major approaches: (1) For dynamic clamp studies we used a simplified distribution based on the experimentally determined distribution of input sizes (16 X 3 nS, 10 X 10 nS, and 2 X 30 nS). We have provided additional clarification in the revised paper, which we hope has addressed the issue. (2) We performed simulations in which we used different size inputs drawn from the experimentally determined distribution. Overall, these approaches were based on experimental data and reasonable assumptions.

C. There is too little statistical and quantitative information in the main text. For example, the first full paragraph of page 4 states: "All inputs had effects on the timing of CbN neuron firing, but larger inputs had much bigger effects. As shown in the cross-correlograms of input timing and CbN neuron spiking, small inputs produced a small transient decrease in CbN neuron spiking, while medium sized inputs strongly reduced CbN neuron spiking, and large inputs transiently shut down CbN neurons for approximately 2 ms. No average or statistics are provided in the subsequent text either. This style makes it difficult to grasp the magnitude of any of these differences, and in any event, it seems obvious that graded inhibition would produce graded effect sizes.

We thank the reviewer for the suggestion and have added more quantification. If there are any additional quantification or statistical tests you would like us to perform, please point them out and we will update the manuscript accordingly.

D. The text states that the refractory period is correlated with the amount of CbN firing and refers to Figure 3. I see no evidence that a correlation was computed from this figure, nor are the statistics of any correlation reported in the text. That said, the potential relationship between CbN firing and PC refractoriness is interesting, but poorly explained in the text. A clearer mechanistic insight would be valuable, beyond "This excitation is a consequence of the autocorrelation function in PC firing", which does not shed much light on the matter. It might be helpful to show the raster plots of spike-triggered inputs to better decipher relationships here. (Rasters for Figure 2D would also be valuable).

We thank the reviewer for their comment and we have provided additional quantification. We appreciate the reviewer’s interest on the issue and suggestions to improve the clarity of the manuscript. In short, the firing statistics of PCs determine their refractory period, which in turn result in different extents of excitation (e.g., amplitude, duration) prior to the inhibition. We have added more explanations in our text. We also think having the raster plots is a good idea and we have added this to the figure (Figure 3 —figure supplement 1). I hope the additional explanation and figures have clarified this point.

E. For the data in Figure 5, what accounts for the very high firing rates of CbN neurons (200 spikes/s)? The plots in Figure 5 could be more intuitive with some work.

We agree with the reviewer that the previous Figure 5 was not very intuitive. We moved most of Figure 5 to a supplemental Figure, provided additional explanation, and retained the parts that were more intuitive. We hope this addresses the reviewer’s concerns.

Reviewer #1 (Recommendations for the authors - second review): The authors have thoroughly addressed my concerns. The revision beter frames the findings, both in terms of novelty and significance than the original submission. It will be of value to the field.

We are pleased that we were able to address all of the reviewer’s concerns.

Reviewer #2 (Recommendations for the authors):

Main concerns for the authors:

The manuscript is very dense and constitute an important dataset for the field. The use of dynamic clamp is very interesting and enhances experimental options. I have however important concerns that I think could help clarify the manuscript:

– My first concern is the choice of the distribution with a different number of small, medium and large input size. I understand that it is necessary to keep the total conductance constant, but it raises a doubt on the relevance of the data in Figures 2 and 4c for example. Indeed, a few large inputs could be considered as many small inputs synchronized. Similarly, few large inputs obviously lead to a high CV by construction, while many small inputs reduce the CV. What is really tested in all these different protocols is a bit confusing.

We understand the reviewer’s concern about our experimental design of the input sizes. Our priority purposes are to reproduce the physiological relevant sizes, ratio, and total inhibitory conductance. As we addressed above in our response to reviewer 1, we based our estimation on the corrected distribution and compute the ratio and weighted averages for different ranges of input sizes. The number of small, medium, and large inputs were chosen to represent their ratio from our recordings. The total inhibitory conductance (200 nS) was chosen based on previous measurement of the maximal evoked inhibitory conductance of CbN neuron in slices (Telgkamp and Raman, 2002; Person and Raman, 2012). We agree that different size inputs inevitably alter the CV, and we systematically showed how the CV of the inhibitory conductance affects CbN neuron firing (Figure 4).

– How is played this distribution on PC inputs? In episodes or continuously?

The different conductance waves were played in episodes, separated with intervals and alternated in sequence between different trials to minimize the effects of adaptation of CbN neuron firing. We have expanded the Methods section to provide additional details about the experiments.

– A set of in vivo PC discharge is used, but where is it coming from?

We thank the reviewer for point this out.

The firing pattern of 10 PCs recorded in vivo used in this study were from our previous studies (Han et al., 2020). In Figure 2 —figure supplement 1, we showed that the ISI distribution of these 10 PCs can be nicely described with lognormal distributions, and we used these artificial PC ISI distributions in most experiments.

– The correction made to approximate conductance is somehow arbitrary. I would tone down the assumption that this is the first demonstration that PC-DCN inputs are large and variable. Indeed, in preceding studies, authors already suggested that PC-DCN inputs are quite large in the recordings, albeit with different recording conditions.

In Person and Raman, Nature 2012 "The high unitary amplitude suggests that individual Purkinje-to-nuclear contacts are quite strong", referring to an article that should be cited here as well:

Pedroarena, C. M. and Schwarz, C. Efficacy and short-term plasticity at GABAergic synapses between Purkinje and cerebellar nuclei neurons. J. Neurophysiol. 89, 704-715 (2003).

Actually the interplay between rate coding and temporal coding has been also addressed in:

Özcan, O. O., Wang, X., Binda, F., Dorgans, K., De Zeeuw, C. I., Gao, Z., Aertsen, A., Kumar, A., and Isope, P. (2020). Differential Coding Strategies in Glutamatergic and GABAergic Neurons in the Medial Cerebellar Nucleus. The Journal of Neuroscience, 40(1), 159-170.

We thank the reviewer for the suggestion and revise our manuscript accordingly. We explained our correction in the response to reviewer 1 (see 2-B).

Please note that previous papers suggest that PC inputs were quite large, but they were still much smaller that the large inputs we observed (over 80 nS). With regard to input sizes, Pedroarena and Schwarz found an average input size of 11.7 nS prior to depression (P12-21 rats at 25-29°C; with high Cl- internal; after correction for high Cl- internal corresponds to an average input size of 3.8 nS prior to depression). Person and Raman found an average input size of 9.4 nS (P13-29 mice at 36°C, with physiological Cl- concentration internal), which is much smaller than the very large inputs we see (see Figure 1 and the response to reviewer 1). Özcan at al. is an interesting and relevant paper and we have cited it in the revised paper.

– I am not a specialist in modeling, but I would imagine that since CbN neurons have active conductances (Ih, Cav3 channels), it is surprising that a simple integrate and fire reproduce fully CbN spiking behavior. The choice of model could be more discussed. In the MS, the discussion around rebound firing is also very limited.

We agree that it is surprising that our simple model is in such good agreement with our dynamic clamp experiments. We would have explored more complex and realistic models had the integrate and fire model failed to recapitulate our experimental findings (p. 4). We think this indicates that our conclusions are not specific to the properties of the CbN neurons and have added to the discussion of this point. With regard to the issue of rebound firing, under our experimental conditions we see no indication of rebound firing.

– The concept of the refractory period accounting for the excitatory effect preceding PC inputs is not well explained, and the simple hypothesis that spikes were elicited at the time of the lowest inhibitory conductance might be enough.

This is an important finding, and we agree with this concern by both reviewers that this part of the manuscript needs clarification. We have revised the figure and provided addition discussion of this point. We hope this issue has been addressed, but we welcome suggestions for ways to clarify this point.

– Figure 4 The frequency observed in CbN neurons seems much higher than in any other study. Similarly, what is the actual firing rate of neurons recorded in Figure 5 without inhibitory inputs?

The high firing frequency of CbN neurons in these experiments likely resulted from the drastic changes in the inhibitory conductance, since we matched the excitatory conductances in the beginning of these experiments to reach a basal CbN firing rate of 30-50 spikes/s. Some of the scenarios we tested in these experiments are extreme cases (e.g., half of the PC inputs pause their firing) and therefore showed very high firing rate of CbN neurons (e.g., 200 spikes/s). The range of the firing rate of CbN neurons have also been observed in other dynamic clamp studies (Wu and Raman, 2017). The firing rate of CbN neurons in our other experiments mostly fall in physiological ranges (less than 100 spikes/s).

[Editors’ note: what follows is the authors’ response to the second round of review.]

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

Some additional modification of the text is warranted to address the remaining concern of Reviewer #2. In addition, a third reviewer was consulted who raised some of the same concerns raised earlier about statistical analyses of the underlying distribution and the validity of the extrapolation to measurements made in symmetrical chloride. Perhaps some of the points raised to address these issues in the previous reply to reviewers could be included in the manuscript to shore up these points.

Reviewer #2 (Recommendations for the authors):

The authors have definitely improved their manuscript, results are better explained and discussed. Based on my previous review, I would have two remaining requests:

– Related to Fig2g and my question about rebound firing, the authors answered

"With regard to the issue of rebound firing, under our experimental conditions, we see no indication of rebound firing". In Fig2g, it seems that a rebound firing occurs for large inputs. It could be important to mention it as synchronization (e.g. by climbing fiber inputs) would also give the same outcome.

The reviewer refers to an apparent rebound component in CbN firing for large PC inputs (Figure 2g). This might also arise from the autocorrelation function of the PC firing: if there is a PC spike at me t=0, then that PC will not fire for several milliseconds before or after that spike as a result of the refractory period, which then lead to disinhibition and transiently promote firing. This is clear in Figure 3d where the cross-correlogram generated by a Poisson input does not show the disinhibition either before and after the inhibition. We have provided an additional explanation to clarify this point (p. 5).

The issue of climbing fibers and different size inputs is interesting. We focused on simple spikes, and we have not examined the issue of climbing fibers and different size inputs either experimentally or with simulations. Hopefully we will be able to address this issue in great depth in future studies.

– Related to the discussion about input size vs synchrony, uniform vs nonuniform inputs, I think that a summary figure would help catch the take-home message.

This is an excellent suggestion. We have added a new figure to summarize the main take home messages (Figure 9).

Reviewer #3 (Recommendations for the authors):

In Wu et al., the authors describe how inputs of variable synaptic weights influence rate and temporal output coding in neurons of the cerebellar nuclei. This is valuable research that advances our understanding of this important question within the field of cerebellar encoding, with experiments that give solid (or mostly solid) evidence for their claims.

In this manuscript, the authors focus on an important question in the cerebellar field: how do high-firing inhibitory Purkinje cells influence the output of neurons in the cerebellar nuclei (CbN) which are themselves spontaneously active? They use classical techniques: whole-cell electrophysiology combined with extracellular stimulation and dynamic-clamp experiments. They identify that there is a wide range of input strengths made onto CbN neurons from Purkinje cells and explore what this diversity of input strength might mean for CbN neuron output. Using dynamic clamp, they parametrically alter input synchrony, comparing how CbN output is altered when they alter inputs of different sizes. Some of their findings are counterintuitive, as they show that right before a strong inhibition of CbN firing caused by Purkinje cell activation, there is a transient enhancement of firing which is due to Purkinje cell refractory period. Other findings are more straight-forward – for e.g. they show that large inputs can strongly modulate firing and thus there is an inverse rate code. They look at synchrony and show that the impact on CbN firing depends on input variability, something that has not previously been addressed in detail. These findings are interesting and add to our understanding of how Purkinje cell synapses may influence CbN output.

1. One of the main claims of the paper is that they uncover that there is variable Purkinje cell input to CbN neurons. However, this finding is based on recordings with symmetrical Cl- internal that is not physiological. They use a calculation based on the literature (based on other synapses that may have different channel properties) to adjust their measurements to physiological values, but the appropriateness of this adjustment is not evident. This makes me question how solid this finding is, as there is no experimental validation. In fact, it is not entirely clear how much their finding differs from previous findings – they say that they show a different distribution, but they do not verify it is different statistically. I think they need to either strengthen their experimental evidence for this finding (which I acknowledge would be a challenging thing to do) or weaken their claims.

The new reviewer shares some of the concerns with the initial reviewers, and we addressed the issue in response to the other reviewers (we assume that the new reviewer saw the revised paper but did not see our previous response).

Our response to the original reviews: The distribution we observed in P2332 mice is very different from that shown in a previous study (Person and Raman, 2012a). We therefore feel that our description of the results was fair and reasonable, and it was not “disingenuous.”

Although we have not changed the text regarding this issue, we are open to specific suggestions, provided it is consistent with the differences in the skewness of the distributions in Figure 1.

The reviewer’s concern provides us with another chance to address how we constrain the input sizes based on several factors. We used a high Cl- internal to determine the distribution of input sizes because it provided superior stability, better access resistance and higher sensitivity compared to using a low Cl- internal. We scaled down the conductance amplitudes by 2.3, guided by previous findings that the measured chloride conductances were 2-3 times larger for high chloride vs. physiological chloride internals (Bormann et al., 1987; Gjoni et al., 2018; Sakmann et al., 1983). Modest errors in the correction from high to physiological chloride concentrations will not affect our major conclusions but might have small quantitative effects on the influence of single inputs. We performed additional simulations to address this issue (Figure 2 —figure supplement 3; Figure 5 —figure supplement 1).

We also took into account the depression that occurs during high frequency activation of PC inputs (Turecek et al., 2017, 2016; Telgkamp and Raman, 2002; Pedroarena and Schwarz, 2003, reduced to 40% of the initial response), as in previous dynamic clamp studies (Han et al., 2020; Person and Raman, 2012; Wu and Raman, 2017). Based on the corrected distribution of input sizes, we took two major approaches: (1) For dynamic clamp studies we used a simplified distribution based on the experimentally determined distribution of input sizes (16 X 3 nS, 10 X 10 nS, and 2 X 30 nS). We have provided additional clarification in the revised paper, which we hope has addressed the issue. (2) We performed simulations in which we used different size inputs drawn from the experimentally determined distribution. Overall, these approaches were based on experimental data and reasonable assumptions.

Changes we made to respond to this issue in this latest revision:

We mentioned in the discussion that “This distribution is clearly different from that of Person and Raman (2012a)”. Although we think this is fair, we could not make this a statistically robust comparison without having access to the original data in that paper. We therefore think it is appropriate to simply remove this sentence from the discussion. Here we want to emphasize that based on our data, the PC inputs should be treated as a population of nonuniform size inputs, as opposed to the simplifying assumption of uniform inputs that was made in many previous studies.

2. The authors talk about Purkinje cell autocorrelation as being the cause of the modulation of firing before the Purkinje cell spike reduces firing in CbN neurons. I find this section hard to follow, and given the general readership of eLife, I think the authors could do a better job explaining this in a more accessible way.

We appreciate this comment. We very much want people to understand this important point, and we have therefore provided additional clarification (p. 5).

https://doi.org/10.7554/eLife.89095.sa2

Article and author information

Author details

  1. Shuting Wu

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Contribution
    Conceptualization, Formal analysis, Visualization, Methodology, Writing – original draft, Writing – review and editing
    Contributed equally with
    Asem Wardak
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2264-7393
  2. Asem Wardak

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Contribution
    Software, Formal analysis, Investigation, Methodology, Writing – review and editing
    Contributed equally with
    Shuting Wu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0048-1484
  3. Mehak M Khan

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5710-7421
  4. Christopher H Chen

    1. Department of Neurobiology, Harvard Medical School, Boston, United States
    2. Department of Neural and Behavioral Sciences, Pennsylvania State University College of Medicine, Hershey, United States
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  5. Wade G Regehr

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Contribution
    Conceptualization, Resources, Funding acquisition, Visualization, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    wade_regehr@hms.harvard.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3485-8094

Funding

National Institutes of Health (R35NS097284)

  • Wade G Regehr

NIH (R01NS032405)

  • Wade G Regehr

NIH (K99NS110978)

  • Christopher H Chen

The Lefler Center at Harvard Medical School

  • Shuting Wu

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank the members of the Regehr lab, Indira Raman and Nicolas Brunel, for comments on the manuscript. We also thank Josef Turecek and Skyler Jackman for their initial work on characterizing the unitary PC inputs to CbN neurons. This work was supported by grants from the NIH (R01NS032405 and R35NS097284 to WGR).

Ethics

All experiments were conducted in accordance with federal guidelines and protocols (IS124-6) approved by the Harvard Medical Area Standing Committee on Animals.

Senior and Reviewing Editor

  1. Sacha B Nelson, Brandeis University, United States

Version history

  1. Received: May 3, 2023
  2. Preprint posted: May 25, 2023 (view preprint)
  3. Accepted: December 27, 2023
  4. Version of Record published: January 19, 2024 (version 1)

Copyright

© 2024, Wu, Wardak et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 222
    Page views
  • 33
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Shuting Wu
  2. Asem Wardak
  3. Mehak M Khan
  4. Christopher H Chen
  5. Wade G Regehr
(2024)
Implications of variable synaptic weights for rate and temporal coding of cerebellar outputs
eLife 13:e89095.
https://doi.org/10.7554/eLife.89095

Share this article

https://doi.org/10.7554/eLife.89095

Further reading

    1. Developmental Biology
    2. Neuroscience
    Athina Keramidioti, Sandra Schneid ... Charles N David
    Research Article

    The Hydra nervous system is the paradigm of a ‘simple nerve net’. Nerve cells in Hydra, as in many cnidarian polyps, are organized in a nerve net extending throughout the body column. This nerve net is required for control of spontaneous behavior: elimination of nerve cells leads to polyps that do not move and are incapable of capturing and ingesting prey (Campbell, 1976). We have re-examined the structure of the Hydra nerve net by immunostaining fixed polyps with a novel antibody that stains all nerve cells in Hydra. Confocal imaging shows that there are two distinct nerve nets, one in the ectoderm and one in the endoderm, with the unexpected absence of nerve cells in the endoderm of the tentacles. The nerve nets in the ectoderm and endoderm do not contact each other. High-resolution TEM (transmission electron microscopy) and serial block face SEM (scanning electron microscopy) show that the nerve nets consist of bundles of parallel overlapping neurites. Results from transgenic lines show that neurite bundles include different neural circuits and hence that neurites in bundles require circuit-specific recognition. Nerve cell-specific innexins indicate that gap junctions can provide this specificity. The occurrence of bundles of neurites supports a model for continuous growth and differentiation of the nerve net by lateral addition of new nerve cells to the existing net. This model was confirmed by tracking newly differentiated nerve cells.

    1. Neuroscience
    Anna-Maria Grob, Hendrik Heinbockel ... Lars Schwabe
    Research Article

    Maintaining an accurate model of the world relies on our ability to update memory representations in light of new information. Previous research on the integration of new information into memory mainly focused on the hippocampus. Here, we hypothesized that the angular gyrus, known to be involved in episodic memory and imagination, plays a pivotal role in the insight-driven reconfiguration of memory representations. To test this hypothesis, participants received continuous theta burst stimulation (cTBS) over the left angular gyrus or sham stimulation before gaining insight into the relationship between previously separate life-like animated events in a narrative-insight task. During this task, participants also underwent EEG recording and their memory for linked and non-linked events was assessed shortly thereafter. Our results show that cTBS to the angular gyrus decreased memory for the linking events and reduced the memory advantage for linked relative to non-linked events. At the neural level, cTBS targeting the angular gyrus reduced centro-temporal coupling with frontal regions and abolished insight-induced neural representational changes for events linked via imagination, indicating impaired memory reconfiguration. Further, the cTBS group showed representational changes for non-linked events that resembled the patterns observed in the sham group for the linked events, suggesting failed pruning of the narrative in memory. Together, our findings demonstrate a causal role of the left angular gyrus in insight-related memory reconfigurations.