Inhibition Shapes Acoustic Responsiveness in Spherical Bushy Cells

ANF-SBC synapse shows low reliability in vivo

Each unit's spontaneous activity was acquired for periods of 60–120 s in the absence of acoustic stimulation. Hierarchical clustering of voltage signals (Fig. 1) yielded two distinct types of signals: PEA (PP-EPSP-AP, Fig. 1B left, black) and PE (PP-EPSP, Fig. 1B middle, gray). In all analyzed cells, this procedure yielded two distinct clusters of these compound signals and no transitional signal complexes in between these two entities. The analysis yielded 97.2–99.2% of the triggered events to be sorted in either of the two signal categories. The first two components of both complex voltage signals were similar in shape, suggesting that the PE compounds are indicative of signals in which the EPSPs failed to trigger a postsynaptic AP (Fig. 1B, middle). The additional occurrence of the postsynaptic AP establishes the PEA compound. Based on the observation that both complex signals shared the first two components, their cooccurrence (PE + PEA) was considered the auditory nerve input to the recorded SBC.

In all units, both PE and PEA signals were recorded during spontaneous activity, but their proportion varied greatly between different units. Quantified as failure fraction (PE/[PE + PEA]), the mean incidence of the complex signals lacking APs amounted to 25.8 ± 12.5% and varied between 4.5% and 46.9%. To test for potential presynaptic effects on synaptic transmission reliability, we compared the size of the PPs in PE and PEA signals in units with sufficiently high signal-to-noise ratios to identify the PP clearly (n = 19). The comparison yielded no significant difference in average PP size (PE = 0.16 ± 0.17 mV vs PEA = 0.17 ± 0.19, p = 0.84, paired t test, n = 19, U1 = 0.08), which suggests that postsynaptic spike failures are not likely attributable to the varying convergence of presynaptic inputs (see Discussion for a consideration of this issue). However, EPSPs that triggered APs (PEA) and those that did not (PE) showed discernible differences with respect to the EPSP rising slopes; the average EPSP rising slopes were calculated between 20% and 80% of the EPSP amplitudes. According to Kuenzel et al. (2011), this measure is a reliable in vivo estimate of EPSP strength. In the present study, the EPSP slopes were systematically shallower in PE compared with PEA complexes (PE = 2.06 ± 0.4 V/s vs PEA = 2.9 ± 0.7 V/s, p < 0.001, paired t test, n = 23, U1 = 0.47, Fig. 1B, right).

Frequency and intensity-specific effects of inhibition

For each cell, the FRA was analyzed both jointly and separately for the PE and PEA complexes to quantify how acoustically evoked inhibition shapes both the intensity and frequency response profiles of SBCs. The sum of the PE and PEA complexes (Fig. 2A, red and black) was considered the ANF input to the recorded SBC. Figure 2B shows an example of the respective FRA with a mean spontaneous activity of 74 spikes/s, a typical v-shaped area of excitation with a maximum discharge rate of 264 spikes/s at the highest stimulus intensity, and a clearly defined CF at 1.9 kHz with a threshold value of −3 dB SPL. The FRA calculated from the PEA complexes, indicating the SBC output signaling, differed considerably with respect to spike rates, as did the frequency and intensity response profiles (Fig. 2C). Spontaneous discharge activity was lower (43.9 spikes/s), as were the spike rates in the excitatory response area (maximum 156.1 spikes/s). Maximum discharge rates were reached at stimulus intensities of 40 dB SPL and were reduced at higher and lower intensities. The most prominent difference was the occurrence of a coherent frequency-intensity area adjacent to the high-frequency border of the excitatory response area, where SBC spiking was reduced to levels below that for spontaneous activity forming a high-frequency inhibitory sideband. The present analysis focused on units characterized by such inhibitory sidebands, which constitute ∼60% of the low-frequency SBCs (Typlt et al., 2012). In our sample, the reduction in postsynaptic AP firing was most prominent 1.4 ± 1.1 octaves above the units' CFs and was always accompanied by a significant increase in the failure fraction (Fig. 2D; p < 0.001, one-way RM-ANOVA, Greenhouse–Geisser corrected, n = 23, η² = 0.83, spontaneous = 22.1 ± 13.7% vs CF = 45.4 ± 16.3%: p < 0.001; spontaneous vs inhibitory = 67.4 ± 15.3%, p < 0.001).

Effect of inhibition differs across the FRA

Repetitive acoustic stimulation (100 ms tone bursts, 300 ms interstimulus intervals, 200–300 repetitions) was used to quantify the dynamics of the units' excitatory and inhibitory responses. As exemplified in Figure 3, acoustic stimulation outside of a unit's receptive field (0.15 kHz, 50 dB SPL; same cell as in Fig. 2, stimulus indicated by dot on “SP” line) did not change the overall input or the incidence of PE or PEA. In the dot raster display in Figure 3A, the whole of PE and PEA—symbolized by red and black dots, respectively—shows the input to the SBC, and their uniform distribution indicates that the acoustic stimulation did not change the discharge rate or the proportion between the two signal types. This is illustrated in the top of Figure 3A highlighting stimulus onset and offset. The histogram in Figure 3B shows that the rate of the input to the SBC (PE + PEA, gray line) underwent some fluctuations, as did the incidence of SBC APs (black) and AP failures (red), but the rates were not affected by the stimulus. In addition, normalization of the rates of the two signals to the overall input showed no significant variations (Fig. 3C). During the 200 stimulus repetitions, short-term fluctuations in the incidence of spike failures in the range of 0–0.3 were observed. However, these instantaneous changes were not correlated with the stimulus onset or offset (Fig. 3C). To verify that the patterns of occurrence of events is not influenced by acoustic stimulation, we compared the cumulative distribution of interevent intervals (IEIs) during stimulation (5–95 ms) and spontaneous activity (150–230 ms) and did not find any differences (IEI_Spont = 20 ± 17.2 ms vs IEI_Stim = 18.2 ± 13.6 ms, p = 0.65, two-sample Kolmogorov–Smirnov test, D = 0.06).

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Figure 3.

SBC discharges during repetitive acoustic stimulation outside the receptive field, same unit as in Figure 2. A, Bottom, During repetitive acoustic stimulation at 0.15 kHz, 50 dB SPL (mark on the “Sp” line in Fig. 2) PE (red), and PEA (black) signal complexes were both uncorrelated to the acoustic stimulus. Insets in the top show magnifications of stimulus onset (left) and offset (right). B, Binned data from A (bin width 1 ms, moving average 3 ms). The acoustic stimulus did not change the overall input to the SBC (gray line, PE + PEA) nor the incidence of PE (red line) or PEA (black line). C, The probabilities of PE (red) and PEA (black) were normalized to the overall input (gray). Probability of ANF input failing to trigger SBC APs (red line) showed short-term fluctuations in the range of 0–0.3, but was not correlated with the stimulus.

To test the influence of neuronal inhibition on spontaneously occurring spike failures, we recorded from SBCs using piggy-back electrodes and applied the glycine antagonist strychnine (2.5 mm, pH 6). Although it reliably blocked the effect of coapplied glycine, sole application of strychnine had no effect on either the AP rate or on the ratio between the overall input (PE + PEA) and postsynaptic APs (PEA) (data not shown).

Acoustic stimulation at the unit's CF 20–30 dB above the threshold (same cell as in Fig. 2, stimulus indicated by dot on “CF” line) strongly increased the incidence of both PE and PEA (Fig. 4A, bottom). It is striking that this stimulation not only triggers a jump in AP rates, but also an increase in the rates of AP failures. The enlargements of the stimulus onset and offset periods elucidate additional response characteristics (Fig. 4A, top) as follows: (1) at the onset of the tone burst, the PEA rates grow faster than the PE rates; (2) PE rates predominate toward the end of the stimulus; and (3) during the period of poststimulus rate depression, both signal types occur, albeit with reduced incidence.

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Figure 4.

Responses to repetitive acoustic stimulation at unit's CF 1.9 kHz, 30 dB SPL (marked by dot on the “CF” line Fig. 2). A, Dotplot during repetitive CF stimulation. Both PE (red) and PEA (black) signal complexes were present before and after the stimulation. Stimulation at CF resulted in a considerable increase of both signal complexes with increasing incidences of PE with ongoing stimulation. Magnification of the discharge activity at stimulus onset (top, left) and offset (right) at CF stimulation. B, Binned data from A; binwidth 1 ms, moving average 3 ms. Acoustic stimulation at the unit's CF increased the overall input to the SBC (PE + PEA, gray line), as well as SBC spiking (PEA, black line). Both display the typical primary-like response pattern. Although PE modestly increased during the sustained response phase, PEA showed a slight reduction, matched by an increase in PE incidence (red line). C, Probability of PE and PEA normalized to the overall input. Incidence of PE increased gradually with ongoing stimulation and surpassed the AP probability 20 ms after stimulus onset. Dashed lines indicate bins that contain fewer than five events and therefore probabilities were not calculated.

Binning the events in a PSTH emphasizes the dynamics of the respective changes (Fig. 4B). The overall input to the SBC (Fig. 4B, gray line, PE + PEA) and the postsynaptic AP activity (black line, PEA) shows a robust, stimulus-evoked absolute rate increase. Both activities display the primary-like response patterns typical of tone-burst-triggered ANF and SBC recordings. In addition, the recordings yielded a pronounced absolute increase in the PE rate (Fig. 4B, red line), indicating that, whereas the acoustically evoked ANF excitation on SBCs is strongly increased, additional physiological processes limit the postsynaptic spike generation. Nevertheless, the increase in postsynaptic APs and AP failures followed very different time courses at stimulus onset. Starting with a delay of ∼3 ms, PE + PEA and PEA rates increased steeply to form the characteristic phasic onset of the primary-like response pattern. With a slightly longer delay of ∼4 ms, the rates of AP failures (PE) also increased, although with a lower growth rate. This increase in AP failures occurs during the transition phase from the excitatory onset peaks toward the respective sustained response levels (Fig. 4B, gray and black lines). Therefore, the onset peak of the PEA histogram shows a sharper decay than the peak of the input activity. The increase in the AP failure rate continues for ∼20 ms after stimulus onset, outlasting the onset peak of the primary-like PSTH. This is evidenced by normalizing the PE and PEA rates to the SBC input (PE + PEA; Fig. 4C), which shows that CF stimulation caused a substantial increase in AP failures. In the present example (and in 5/16 recorded units), the incidence of AP failures during CF stimulation surpassed the incidence of APs. Significant increases in AP failures during CF stimulation were found in 13/16 recorded SBCs, albeit with different failure rates. During the subsequent sustained phase of the PSTH up to the stimulus offset, a continuous gradual increase in PE probability at the expense of PEA complexes was observed (Fig. 4C). Because of the almost complete poststimulus suppression of neuron spiking, it is difficult to judge the dynamic changes between PE and PEA signals at the stimulus offset (Fig. 4C, dashed lines). The recording indicates that 50–100 ms passed before the unit resumed the prestimulus level of spontaneous activity (Fig. 4A,B).

An overall assessment of SBC discharge activity during CF stimulation suggests that, in addition to the strong excitation, other stimulus-triggered physiological mechanisms reduce neuronal spiking. To determine whether postsynaptic depression contributes to the increase in spike failures, the interevent interval distributions were analyzed. A respective reduction would indicate the potential impact of depression. Indeed, during CF stimulation, the distribution of interevent intervals of PE signals was shifted toward lower values (IEI_Spont = 20.7 ± 16 ms vs IEI_Stim = 5.5 ± 3.9 ms, p < 0.001, two-sample Kolmogorov–Smirnov test, D = 0.59), suggesting that postsynaptic depression contributes to the increase in spike failures. However, the effectiveness of depression in preventing AP spiking was shown to be low compared with that of inhibition (Kuenzel et al., 2015).

As presently shown and also reported by Kuenzel et al. (2011), acoustic stimulation at the unit's CF triggers superposed effects of excitation and inhibition on SBC activity, which made it difficult to draw differentiating conclusions from the above analysis. To dissect the effects of such interactions on the recorded activity, we used pure-tone stimulation within the unit's high-frequency inhibitory sideband (“Inh” in Figs. 2 and 5, which shows the same cell as in Fig. 2). Such stimulation did not change the ANF input to SBCs, as indicated by the overall steady rate of input to the SBC before, during, and after tone burst presentation (PE + PEA; Fig. 5A, red and black dots, B, gray line) or the interevent intervals (ISI_Spont = 20.6 ± 16.8 ms vs ISI_Stim = 19.6 ± 15.7 ms, p = 0.79, two-sample Kolmogorov–Smirnov test, D = 0.03). This demonstrates that there was no stimulus-evoked excitatory drive on the SBC, and the observed overall activity can be regarded as the unit's spontaneous discharge activity. Nevertheless, the acoustic stimulation had a prominent effect on SBC activity. It caused an enduring reduction in postsynaptic APs (Fig. 5A); that is, PE complexes greatly outnumbered PEA complexes after stimulus onset (Fig. 5A, top left), whereas PEAs again prevailed after stimulus offset (Fig. 5A, top right). The histogram shows that PEA reduction is matched with respect to both dimension and temporal dynamics by an increase in PEs (Fig. 5B,C). For stimulation within the inhibitory sideband compared with CF stimulation, the PEA reduction occurs earlier and proceeds faster. After stimulus offset, the release from reduction lasts for ∼10 ms (Fig. 5C). Unlike for CF stimulation, there was neither a general activity-reducing aftereffect of acoustic stimulation nor enduring modifications of the PE/PEA ratio.

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Figure 5.

Response to repetitive acoustic stimulation within inhibitory sideband (4.8 kHz, 50 dB SPL; dot on the 'Inh' line in Fig. 2. A, Bottom, Dotplot of PEA (black) and PE (red) during repetitive acoustic stimulation. This graph and the magnifications of activity at stimulus onset (top left) and offset (top right) indicate that, whereas the overall activity is not altered by the stimulus, the ratio between PE and PEA is shifted toward higher incidence of PE. B, Binned data from A elucidate sustained overall input activity (gray line, PE + PEA), but considerably increased failure rates (red line, PE) during stimulation, which is in contrast to the concurrent reduction of postsynaptic APs (black line, PEA). C, Probability of PE and PEA normalized to the overall input (PE + PEA). Reduction in postsynaptic APs (black) is matched with respect to dimension and temporal dynamics by an increase in AP failures (red).

To evaluate whether the data of the exemplary unit provides a valid description of the impact of excitatory and inhibitory stimulation on AP reduction in SBC, a summary analysis was performed. For that, the PSTHs for all units recorded under the different stimulus conditions were calculated and averaged across cells (Fig. 6). Similar to the exemplary unit, the activity outside the acoustic response area (n = 18, indicated as “stimulus 2 oct < CF”), at the units' CFs (n = 16, indicated as “CF stimulus”), and within the high-frequency inhibitory sidebands (n = 23, indicated as “Inh stimulus”) were compared. Acoustic stimulation outside of the acoustic response area did not affect the overall input to the SBC [stimulation (10–90 ms) = 56.1 ± 15.4 Hz vs spontaneous (150–230 ms) = 56.9 ± 16 Hz p = 0.34, paired t test, U1 = 0.08, n = 18, Fig. 6A, top] or the failure fraction [10–90 ms vs 150–230 ms, stimulation (10–90 ms) = 25.3 ± 17.3% vs spontaneous (150–230 ms) = 25.5 ± 16.9%, p = 0.85, paired t test, U1 = 0.08, n = 18, Fig. 6A, bottom]. Acoustic stimulation at the units' CFs resulted in PSTHs displaying primary-like response patterns of the ANF input to the SBCs [Fig. 6B, top, stimulation (10–90 ms) = 212.4 ± 75.4 Hz vs spontaneous (150–230 ms) = 69.3 ± 38.7 Hz, p < 0.001, paired t test, U1 = 0.86, n = 16], as well as the SBC AP discharges (data not shown). However, the reliability of signal transmission at the ANF-SBC synapse decreased during CF stimulation, as shown by the higher failure fraction with slow onset dynamics [stimulation (10–90 ms) = 43.3 ± 17.5% vs spontaneous (150–230 ms) = 30.4 ± 17.5%, p < 0.01, paired t test, U1 = 0.13, Fig. 6B, bottom] and a gradual increase throughout the continuous stimulation (25–90 ms, r_s = 0.57, p < 0.001). When cells were stimulated within their inhibitory sideband, no change in SBC input was observed [stimulation (10–90 ms) = 68.7 ± 36.4 Hz vs spontaneous (150–230 ms) = 71.2 ± 39.7, p = 0.29, U1 = 0.07, paired t test, n = 23, Fig. 6C, top]. However, the incidence of AP failures increased considerably and surpassed the probability of AP generation [stimulation (10–90 ms) = 58.1 ± 16.8% vs spontaneous (150–230 ms) = 26.1 ± 12.7%, p < 0.001, paired t test, U1 = 0.61, n = 23, Fig. 6C, bottom]. The increase in failure fraction showed slow onset and offset dynamics and a decrease with continuing stimulation (25–90 ms, r_s = −0.57, p < 0.001).

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Figure 6.

Averaged data of 16–23 cells. A, Acoustic stimulation (100 ms, black icon on top of the graph) outside of the receptive field did not change the auditory nerve input to the SBC (n = 18; gray line, top) nor did it alter the proportion between PEA (black line, bottom) and PE (red line). The average failure fraction of about 1/4 was uncorrelated with the acoustic stimulation. B, CF stimulation resulted in an excitatory primary-like response pattern of the auditory nerve input (PE) to the SBCs (n = 16; gray, top). In the course of excitatory acoustic stimulation, the probability of AP generation (black, bottom) decreased and AP failure fraction increased (red). C, Acoustic stimulation within the inhibitory sideband did not alter the ANF input to the SBCs (n = 23; gray, PE + PEA, top), but considerably changed the PE/PEA ratio; the failure fraction increased from 25.3% to 58.9% on average. **p < 0.01, ***p < 0.001, paired t test.

Inhibition is delayed relative to excitation and persists after stimulation ceases

For an in-depth analysis of the time course of the onset and offset of acoustically evoked excitation and inhibition, PSTH data for all recorded cells were averaged (Fig. 7). The time course of the increase in the AP failure fraction at stimulus onset was compared with the time course of stimulus-evoked AP firing. For the failure fraction, stimulation within the inhibitory sideband showed slow onset dynamics (thin red lines, data from the different units) with an average half-rise time (HRT) of 8.4 ms (onset of stimulation to 50% of failure fraction; Fig. 7, bottom left, thick red line). The respective increase in average AP firing triggered by CF stimulation was considerably faster (HRT = 4 ms; Fig. 7, bottom left, black line). In addition, after the end of the acoustic stimulation, the dynamics of release from inhibition differed considerably from the reduction of CF-triggered AP firing (Fig. 7, bottom right). When fitting a mono-exponential function of the form A · e^(−x/τ) + c to the data, the failure fraction showed a 10-fold slower decay (τ_Inh = 9.38 ms, 95% confidence interval: [8.9, 9.8] ms) for the release from inhibition than for the offset of excitation (τ_Ex = 0.86 ms, 95% confidence interval: [0.67, 1.06] ms).

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Figure 7.

Comparison of onset and offset dynamics of acoustically evoked inhibition and excitation. Mean values (thick red line, n = 23) and failure fractions from individual units (thin red lines) are shown and contrasted with the average AP firing upon CF stimulation (black line, right ordinate, n = 16). Units showed slow inhibitory onset dynamics (bottom left, half-rise time 8.4 ms) compared with the faster increase in AP firing during CF stimulation (black line, HRT = 4 ms). After the end of stimulation (bottom right), the failure fraction decayed slower (τ = 9.38 ms) for stimulation in the inhibitory sideband than AP firing at the offset of CF stimulation (τ = 0.86 ms).

Strength of inhibition depends on stimulus intensity

The auditory system works along five orders of magnitude with respect to intensity, with sound intensity being encoded by the spike rate (Sachs and Abbas, 1974; Palmer and Evans, 1980; Winter et al., 1990). Here, we tested whether the strength of acoustically evoked inhibition also depends directly on sound intensity at the first processing stage of the central auditory system. As done before, we analyzed RLFs under three different conditions: (1) outside of the receptive field, (2) at the CF, and (3) within the inhibitory sideband. Figure 8, A–C, shows the respective analysis for the exemplary unit shown in Figure 2. Acoustic stimulation outside of the receptive field (“SP,” dotted line; see also respective line in Fig. 2) did not change the overall input (PE + PEA; Fig. 8A) or the AP firing (PEA; Fig. 8B) of the SBC, resulting in a stable failure fraction (Fig. 8C). When stimulating at CF, the overall input rate increased monotonically up to a level of 40 dB SPL and showed constant firing toward higher SPLs (“CF,” solid line, Fig. 8A). Such monotonicity of the input activity was found in all 23 recordings. However, the RLFs of SBC AP firing differed considerably from the input activity. Of the 23 units, 12 showed pronounced nonmonotonic RLFs, defined as a minimum 20% decrease of postsynaptic firing at high stimulus levels (“CF” in Fig. 8B for the exemplary unit). Correspondingly, the failure fractions in the units with nonmonotonic RLFs were markedly increased at high stimulus levels (Fig. 8C). For stimulation within the inhibitory sideband, altering the stimulus level did not affect the overall input (“Inh,” dashed line, Fig. 8A), but did result in monotonously decreased SBC firing with increasing stimulation intensity (Fig. 8B), which was accompanied by a steadily increasing failure fraction (Fig. 8C). All analyzed cells (n = 23) showed decreased SBC firing within the inhibitory sideband (−10 to 0 dB SPL = 48 ± 16.1 Hz vs 70–80 dB SPL = 18.9 ± 12 Hz, p < 0.001, paired t test, U1 = 0.61, n = 23) and a corresponding increase in the failure fraction (−10 to 0 dB SPL = 22.5 ± 14.2 Hz vs 70–80 dB SPL = 68 ± 14.2, p < 0.001, paired t test, U1 = 0.83, n = 23; Fig. 8D). The efficiency of acoustically triggered AP inhibition correlated with the level of acoustic stimulation in a unit-specific manner and amounted to spontaneous AP rate reductions by 19–94% at a 70–80 dB SPL tone burst intensity. The absolute increase in failure fraction across units varied between 8% and 77% and was negatively correlated with the units' spontaneous failure fraction; that is, units with lower spontaneous failure fractions showed higher absolute increase during inhibitory stimulation (r_s = −0.52, p < 0.05, n = 23). For stimulation within the inhibitory sideband, both the decrease in SBC firing (Fig. 8D, left) and the increased PE probability (Fig. 8D, right) were correlated with the stimulus intensity (APs: r_s = −0.59, p < 0.001; failure fraction: r_s = 0.7, p < 0.001, n = 23).

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Figure 8.

Rate level functions of ANF input and SBC firing at different frequencies (same unit as in Fig. 2). A, RLFs of SBC input (PE + PEA) neither showed changes in firing rate outside of the receptive field (short dashed line, “Sp” in Fig. 2) nor for stimulation within the inhibitory sideband (long dashed line; “Inh” in Fig. 2). The firing rate at the unit's CF increased monotonically up to 35 dB SPL and flattened toward higher SPLs (solid line, “CF” in Fig. 2). B, RLF of AP rates (PEA) showed overall decreased activity for all stimulus conditions. Nevertheless, as for the input activity, the rates did not vary with intensity outside of the unit's response area (short dashed line). At CF, the RLF had a nonmonotonic course (solid line) and stimulation within the inhibitory sideband increasingly reduced rates >25 dB SPL (long dashed line, “Inh” in Fig. 2). C, Respective failure fractions were calculated as PE/(PE + PEA). The decrease in SBC AP rates compared with the ANF input is accompanied by an increase in failure fraction at CF as well as the inhibitory sideband. Note that for inhibitory sideband stimulation, the increase of failure fraction occurs at lower intensities than that of CF stimulation and the overall effect was larger. D, SBC firing and failure fraction with increasing stimulus intensity at the inhibitory sideband. Data are from 23 units. Units differed in their spontaneous AP rates and failure fraction (gray lines; 0 dB at left and right). Nevertheless, with increasing stimulus intensity, AP rates decreased (left; mean r_s = 0.7, p < 0.001, black line) and failure fraction increased in all units (right; r_s= −0.59, p < 0.001, mean: black line).

Inhibition reduces EPSP strength and increases EPSP-to-AP transition time

Previous in vivo studies showed that, within the cochlear nucleus, the duration of juxtacellularly recorded APs can vary between recorded cell types (Mongolian gerbils: Typlt et al., 2012). In addition, the synaptic transmission delay is progressively reduced during early postnatal development (mice: Sonntag et al., 2011), whereas in adults, the transmission delay becomes prolonged with increasing discharge rates (Mongolian gerbils: Tolnai et al., 2009). Here, we tested whether acoustically evoked inhibition also shapes the AP waveform and affects the timing of synaptic transmission. We analyzed APs recorded during spontaneous activity and during inhibitory stimulation. For each recording, 4405 ± 1767 APs were analyzed and the relevant parameters could be extracted from 98.7 ± 1.5% of all APs. Under both stimulus conditions, the EPSP-AP transition times were similarly correlated with the EPSP rising slopes (spontaneous: r_s = 0.43, p < 0.001, inhibitory: r_s = 0.41, p < 0.001, data not shown, but see Kuenzel et al., 2011; Nerlich et al., 2014) while displaying considerable within-unit variability (Fig. 9Ai,Aii). A Boltzmann function of the form 1/(1 + e^{−τ(x − d)}) was fitted to the cumulative distribution with decay constant τ and symmetric inflection point d (R² > 0.99). Under acoustically evoked inhibition, the shape of the distribution stayed constant, but the EPSP-AP transition times were shifted toward higher values (Fig. 9Aii). In all cells analyzed, inhibitory stimulation shifted the median EPSP-AP transition time toward higher values (spontaneous = 236 ± 17 μs vs inhibitory = 249 ± 17 μs, p < 0.001, paired t test, U1 = 0.28, n = 23, Fig. 9Aiii).

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Figure 9.

Inhibition flattened EPSP slopes and prolonged EPSP-AP transition times. Ai, Two representative single events with long (black) and short EPSP-AP transition time (gray) aligned at the PP. As indicated in the graph, the analysis quantifies the time from the peak of the EPSP or the respective inflection point of the EPSP rising slope to the peak of the AP. Aii, Representative cumulative distribution function of EPSP-AP transition times for spontaneous activity (light gray) and inhibitory stimulation (dark gray). The shape of the distribution remains unaltered, but inhibitory stimulation shifts the average transition time by 20 μs (median) toward higher values. Aiii, In all analyzed cells, inhibitory stimulation increased the median EPSP-AP transition time. Bi, Rising slope of EPSP as an indication of EPSP strength. Examples show constant PP signals, but EPSP slopes varying with stimulus condition, spontaneous activity (gray) and inhibitory stimulation (black). Bii, Inhibitory stimulation shifted the cumulative distribution function by 0.16 V/s (median) toward lower values, indicating a reduced EPSP strength. Biii, Stimulus evoked reduction in EPSP rising slopes were observed in 19 of 23 cells.**p < 0.01, ***p < 0.001.

The strength of the EPSPs was estimated by their rising slopes (Fig. 9Bi). Acoustically evoked inhibition reduced EPSP strength and shifted the cumulative distribution toward lower EPSP rising slopes (Fig. 9Bii). This was a small but robust effect observable in 19/23 cells (spontaneous = 2.8 ± 0.7 V/s vs inhibitory = 2.7 ± 0.6 V/s, p < 0.001, paired t test, U1 = 0.06, n = 23; Fig. 9Biii).

In addition, steeper EPSP rising slopes were correlated with shorter AP durations, both under the conditions of spontaneous activity as well as during acoustic stimulation of inhibition (spontaneous: r_s = −0.26, p < 0.001; inhibitory: r_s = −0.27; p < 0.001, data not shown). As described above, inhibitory stimulation caused more shallow EPSPs, but under these conditions, the AP durations were still shorter than the respective APs during spontaneous activity (spontaneous = 0.43 ± 0.07 ms vs inhibitory = 0.41 ± 0.06 ms, p < 0.001, paired t test, U1 = 0.04, n = 23).

Temporal precision of SBC firing surpasses that of its excitatory input

Joris and Smith (2008) hypothesized an increase of temporal precision in spike timing from the auditory nerve to bushy cells based on a population comparison of ANF and BC recordings partly mediated by inhibitory inputs. Dehmel et al. (2010) observed a decrease in phase locking when pharmacologically blocking the inhibition. Here, we aimed at an immediate comparative analysis of phase locking of the SBCs' presynaptic and postsynaptic activity, which is necessary to draw conclusions about the mechanisms that cause these improvements. To determine whether the temporal precision is improved at the level of the SBCs compared with the ANF, we calculated the vector strength during low-frequency stimulation (0.94 ± 0.2 kHz, min = 0.5 kHz, max = 1.2 kHz, n = 23) separately for the following activities: (1) ANF input, that is, EPSP time points in both PE and PEA complexes; (2) the respective EPSP time points of the PEA complexes; and (3) AP time points of the respective signals. Twenty-three units that showed a significant degree of phase locking (p < 0.001, Rayleigh test) at the level of the SBC APs were included in the analysis (Fig. 10). The overall analysis shows a highly significant increase in the vector strength from the ANF input to the SBC APs (p < 0.001, RM-ANOVA, Greenhouse–Geisser corrected, η² = 0.36, Fig. 10A). The phase locking of the EPSPs differed from that of the APs (EPSP_succ = 0.61 ± 0.07 vs AP = 0.65 ± 0.07, p < 0.001, Bonferroni post hoc test, U1 = 0.34) and furthermore differed depending on whether the EPSPs preceded an AP or not (EPSP_fail = 0.53 ± 0.08 vs EPSP_succ = 0.61 ± 0.07, p < 0.001, Bonferroni post hoc test, U1 = 0.3). Whereas the EPSPs of PE complexes showed a vector strength similar to the ANF input (ANF = 0.51 ± 0.1 vs EPSP_fail = 0.53 ± 0.08, p = 0.59, Bonferroni post hoc test, U1 = 0.04), the temporal precision was substantially increased in EPSPs that triggered an AP (ANF = 0.51 ± 0.1 vs EPSP_succ = 0.61 ± 0.07, p < 0.001, Bonferroni post hoc test, U1 = 0.41). In follow-up analyses, the EPSP rising slope was used as an estimate of the EPSP strength. For each unit, EPSPs were sorted according to their rising slope and the vector strength calculated for large EPSPs (top 20% of the rising slopes) and small EPSPs (bottom 20%). Large EPSPs showed a considerably better phase locking than small EPSPs (EPSP_small = 0.56 ± 0.06 vs EPSP_large = 0.62 ± 0.06, p < 0.001, paired t test, U1 = 0.29, n = 23, data not shown) and are more likely to generate a postsynaptic AP (P_small = 0.31 ± 0.23 vs P_large = 0.83 ± 0.11, p < 0.001, paired t test, U1 = 0.7, n = 23, data not shown). An even greater improvement in phase locking was observed for the AP (EPSP_large = 0.62 ± 0.06 vs AP = 0.65 ± 0.07, p < 0.001, Bonferroni post hoc test, U1 = 0.14, n = 23). To investigate whether the improvement of AP phase locking can be accounted for by the variability in EPSP-to-AP transmission times, we correlated the latter with the phase position of successful, that is, AP-preceding EPSPs. For normalization of the phase angels between units, the maxima of the EPSP histograms were centered around 180 ° of the respective stimulus period. In all cells analyzed, the EPSP-to-AP transition was negatively correlated to the time point within the stimulus cycle (Fig. 10B, −0.53 < r_s < −0.19, p < 0.001); that is, events that occurred early in the cycle had longer EPSP-to-AP transition times than events that occurred later in the cycle. For further analysis, all triggered PEA complexes were split at the median EPSP-to-AP transition time into two groups, one with slower (EPSP_slow, blue circle, Fig. 10C, inset, same data as in Fig. 10B) and one with faster EPSP-to-AP transition times (EPSP_fast, red circles). Events of the EPSP_slow group (Fig. 10C, blue solid line) were, on average, triggered earlier in the cycle than events of the EPSP_fast group (Fig. 10C, red solid line). This contrasted with the AP time points in both groups, which showed almost homogeneous distributions (blue and red dashed lines). The distance of the respective distributions (EPSP_slow vs EPSP_fast and AP_slow vs AP_fast) was assessed by calculating the Jenson–Shannon distance (Rao, 1982; Wong and You, 1985; Lin and Wong, 1990). In 91.3% of the recordings (21/23), the JSD between AP groups was smaller than between the respective EPSP groups (JSD_EPSP = 0.015 ± 0.012 vs JSD_AP = 0.004 ± 0.006, p < 0.001, U1 = 0.09, n = 23, Fig. 10D), suggesting that the variability in EPSP-to-AP transition time facilitates precise SBC spiking.

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Figure 10.

Transformation of phase-locking accuracy in SBCs. A, Vector strength of ANF, EPSPs, and APs for 23 different units. The temporal precision is improved at the level of successful EPSPs and their subsequent APs. B, For PEA complexes, the EPSP-to-AP transition time was negatively correlated with the EPSP time point within the stimulus cycle; that is, signals that occurred early in the phase had longer EPSP-to-AP transition times. The black line represents the median trend line. C, For each recording, the dataset was split at the respective median of EPSP-to-AP transition times to form two groups of PEA complexes, one with slow (blue) and one with fast (red) EPSP-to-AP transition times (inset). Although the EPSP time points of events of the EPSP_slow group occurred, on average, earlier in the cycle than the ones of the EPSP_fast group (solid lines), the time points of the respective APs were much more overlapping (dashed lines). D, The divergence of EPSP and AP time points of both groups was assessed by calculating the Jenson–Shannon distance and yielded a substantial increase in overlap in AP time points compared with EPSP time points across the two groups. E, The period histogram of the ANF input (black) was centered at 180° and the incidence of PE complexes relative to this position analyzed (red, inset shows exemplary cell). The cumulative distribution of ANF (black, thin lines = single cells, thick line = mean of single cells) and PE (red lines) occurrence within the stimulus period revealed that the incidence of PE complexes in all cells lagged the ANF input; that is, EPSPs occurring after the preferred phase are unlikely to generate an AP. F, Acoustically stimulation at high SPL (40–50 dB above threshold) resulted in a larger improvement of AP timing than low-SPL stimulation (5–10 dB above threshold). ***p < 0.001.

However, varying EPSP-to-AP transition times can only be functionally linked to precisely phase-locked APs if just EPSPs that are leading the preferred phase are affected. EPSPs lagging behind the preferred phase should be precluded from triggering an AP. To test for this, we analyzed the phase position of PE complexes within the stimulus period relative to the overall ANF input (Fig. 10E). As before, the ANF input was set to be centered on 180° (PEA + PE, black lines, inset showing period histograms for an exemplary cell). In relation to this distribution, the incidence of PE complexes (Fig. 10E, red lines, see also inset) were shifted to larger phase angles, that is, EPSPs that arrive after the preferred phase are less likely to generate an AP. In all recorded cells, the incidence of PE complexes (Fig. 10E, red lines) was lagging behing the preferred phase of the ANF input (black lines, ANF = 180° vs PE = 210 ± 16° p < 0.001, paired t test, U1 = 0.98, n = 23).

We have shown that the effect of inhibition depends on stimulus intensity, which suggests testing whether the improvement of temporal precision is also stimulus dependent, which would provide additional evidence that inhibition has an immediate effect on the quality of phase locking. Indeed, the comparison of phase locking between stimulation at low SPL (5–10 dB above threshold) and at high SPL (40–50 dB above threshold) yielded a substantial improvement in the high-SPL condition (low SPL = 0.03 ± 0.01 vs high SPL = 0.06 ± 0.02, p < 0.001, paired t test, U1 = 0.71, n = 7, Fig. 10F).

Iontophoretic application of glycine resembles the effects of inhibitory stimulation

In vitro studies suggest a predominant role of glycinergic inhibition in the central auditory system (Moore and Caspary, 1983; Caspary et al., 1994, 2008; Friauf et al., 2011; Nerlich et al., 2014). To evaluate these findings and to test for the impact of glycinergic inhibition in vivo, glycine was applied iontophoretically (100 mm, pH 6) with increasing current steps while recording spontaneous activity of SBCs (n = 6). Glycine increased the incidence of PE signals (Fig. 11A, red arrowheads; Fig. 11B, red line) and reversibly reduced the number of PEA signals (Fig. 11A,B, black stars and black line, respectively, p < 0.001, RM-ANOVA, Greenhouse–Geisser corrected, η² = 0.68, failure fraction: control = 25.9 ± 18.7% vs application = 76.1 ± 20%, p < 0.001, control vs recovery = 26.7 ± 19.2%, p = 0.79, n = 6). Nevertheless, the overall EPSP input to the SBC was not changed (Fig. 11B) (p = 0.34, RM-ANOVA, η² = 0.04, control = 69.5 ± 16.2 Hz vs application = 66.4 ± 18 Hz, n = 6, data not shown). The increase in failure fraction was directly correlated with the iontophoretic current, reflecting the amount of glycine applied (r_s = 0.89, p < 0.05, Fig. 11C). Glycine was potent enough to completely block the postsynaptic activity in all recorded cells when high enough currents were used. Application of the carrier alone (20 mm NaCl, pH 6) did not affect the input firing rate (control = 71.5 ± 9.2 Hz vs application = 72.8 ± 12.4 Hz, p = 0.29, RM-ANOVA, η² = 0.015, n = 6) or the proportion of PE and PEA signals (failure fraction: control = 22.3 ± 11.3% vs application = 23.3 ± 12.6%, p = 0.32, RM-ANOVA, η² = 0.002, n = 6, data not shown). In addition to affecting the reliability of signal transmission, glycine shaped the remaining APs in a way similar to the changes observed during inhibitory acoustic stimulation (Fig. 11D). The EPSP-AP transition time was prolonged during glycine application (Fig. 11D, left, p < 0.001, RM-ANOVA, η² = 0.45, control = 245 ± 9 μs vs application = 273 ± 14 μs, p < 0.001, n = 6) and the EPSP rising slopes were flattened (Fig. 11D, right, p < 0.01, RM-ANOVA, η² = 0.04, control = 3.46 ± 1.03 V/s vs application = 3.1 ± 0.95 V/s, n = 6). Both effects were reversible within seconds (EPSP-AP: control vs recovery = 248 ± 10 μs, p = 0.61; EPSP slope: control vs recovery = 3.35 ± 0.93 V/s, p = 0.64) and none of these effects was observed while applying the carrier (20 mm NaCl, pH 6) alone (EPSP-AP transition time: control = 247 ± 10 μs vs application = 250 ± 9 μs, p = 0.47, RM-ANOVA, η² = 0.02, n = 6; EPSP slopes: control = 2.98 ± 1.7 V/s vs application = 2.93 ± 1.4 V/s, p = 0.72, RM-ANOVA, η² = 0.003, n = 6). These data suggest that glycine alone can, to a great extent, mimic the effect of acoustically evoked inhibition, which is consistent with the suggested function of glycinergic inhibition reported by in vitro studies.

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Figure 11.

Effect of iontophoretic glycine application on spontaneous activity. A, Both APs (PEA, black star) and PE (red arrowheads) are present under control conditions (top trace). Iontophoretic application of glycine changed the PEA/PE ratio; that is, more EPSPs failed to trigger an AP (middle trace). This effect was reversible with the PEA/PE ratio turning back to preapplication conditions (bottom trace). B, Binned data from A. The overall input rate (PE + PEA, gray line, left ordinate) remained constant during glycine application. The failure fraction (red line, right ordinate) increased during application on the expenses of a decreased AP probability (black line). C, Failure fraction increase upon glycine application was correlated to the iontophoretic current used; that is, the amount of glycine ejected. D, Application of glycine did also affect the waveform of remaining APs. Similar to acoustic stimulation, the EPSP-AP transition time was prolonged (left) and the EPSP slopes flattened (right) during application. The magnitude of the effect resembled the inhibitory sideband stimulation. Dashed lines indicate cumulative distribution from Figure 9B.