In humans and other animals, small differences in the time at which a sound arrives at each ear are crucial for determining the location of the sound. Neurons in the first processing station of the brain – the cochlear nucleus – receive information about sounds (or “inputs”) from the ears. They then produce electrical signals that relay this information to other areas of the brain. Some of these inputs increase the activity of the neurons and so are known as “excitatory” inputs, while other “inhibitory” inputs decrease the activity of the neurons. The balance between these two inputs determines what information is passed to other parts of the brain, but it is not clear how these inputs interact.

Keine, Rübsamen and Englitz studied electrical activity in the brains of Mongolian gerbils while being exposed to sounds with more natural properties than previously studied. The experiments reveal that inhibitory inputs play an important role in controlling the activity of neurons in the cochlear nucleus. By decreasing the neurons’ activity, inhibitory inputs allow these cells to respond to many different levels of sound, from very loud to very quiet. The experiments also show that excitatory and inhibitory inputs are triggered by similar sounds so that the two processes quickly balance each other. This means that the brain is equally able to work out where a sound is coming from regardless of whether it is loud or quiet.

Further work is now needed to understand responses to natural sounds and to determine how experimentally removing the inhibitory inputs affects hearing.

Dynamic processing in neural networks is controlled by an interplay of excitation and inhibition. In cortical processing, the dominant excitatory neurons interact reciprocally with inhibitory neurons, which serve key functions in shaping the responses (reviewed in Isaacson and Scanziani, 2011). In the auditory cortex, recent work has emphasized the role of inhibition in dynamically balancing excitation via a high degree of co-tuning (e.g. Wehr and Zador, 2003; Renart et al., 2010) that serves to shape and accelerate network dynamics. Similarly, in other modalities, inhibition was found to be co-tuned with excitation in the cortex, typically with a wider tuning, generating the well-described inhibitory sidebands (auditory: Wang et al., 2002; Wu et al., 2008, visual: Sohya et al., 2007; Niell and Stryker, 2008; Liu et al., 2009, 2011; Katzner et al., 2011, olfactory: Poo and Isaacson, 2009). Temporally, inhibition often follows excitation closely (auditory: Wehr and Zador, 2003, somatosensory: Wilent and Contreras, 2004).

In the auditory brainstem, the role of inhibition has also been studied, however, from a more fundamental perspective, without a focus on its functional role during complex stimulation. Various studies have shown prominent inhibitory influences on signal processing in the cochlear nucleus (Caspary et al., 1994; Kopp-Scheinpflug et al., 2002; Gai and Carney, 2008), in the medial and lateral superior olive (Grothe and Sanes, 1993; Brand et al., 2002; Myoga et al., 2014), and in the dorsal and ventral nuclei of the lateral lemniscus (Yang and Pollak, 1994, 1998; Burger and Pollak, 2001; Nayagam et al., 2005; Pecka et al., 2007; Spencer et al., 2015).

The cochlear nucleus (CN), the first stage of the central auditory system, is the starting point of distinct neuronal circuits involved in sound source localization. Spherical bushy cells (SBC) in the anteroventral division of the CN (AVCN) provide the temporally precise excitatory inputs to binaural neurons in the medial superior olive (MSO), where interaural time differences are computed (Yin and Chan, 1990). These SBCs receive suprathreshold excitatory input from auditory nerve fibers (ANF) via large axosomatic terminals, the endbulbs of Held (Brawer and Morest, 1975; Schwartz and Gulley, 1978; Ryugo and Sento, 1991; Nicol and Walmsley, 2002). In addition, inhibitory inputs on SBCs have been reported, which provide surprisingly slow acoustically evoked inhibition mediated by glycine and GABA (Wu and Oertel, 1986; Kolston et al., 1992; Juiz et al., 1996; Lim et al., 2000; Mahendrasingam et al., 2004; Xie and Manis, 2013), with glycine dominating (Nerlich et al., 2014b).

Due to the requirements of high-fidelity acoustic processing underlying sound localization, many studies focused on the fast and temporally precise signal transmission in auditory brainstem circuits. With respect to changes in temporal precision from ANF to neurons in the CN some studies – comparing population data – reported a general increase in temporal precision (Joris et al., 1994a, 1994b), while others found no change (Bourk, 1976; Blackburn and Sachs, 1989; Winter and Palmer, 1990), or reported decreased temporal precision at certain stimulation frequencies (Paolini et al., 2001; Fukui et al., 2006).

More recent studies advanced the analysis to the single-cell level, by comparing the endbulb of Held evoked excitatory postsynaptic potentials (EPSP) with the action potentials (AP) of the SBCs allowing for a direct comparison of ANF input and SBC output (Typlt et al., 2010). This enabled a direct assessment of the input-output function under the condition of acoustic stimulation, also in combination with pharmacological manipulations. The respective experiments revealed a slight increase in temporal precision of signal coding, attributed to the influence of acoustically evoked inhibition (Dehmel et al., 2010; Kuenzel et al., 2011; Keine and Rübsamen, 2015). It may be argued that the stimulus conditions employed were rather static and did not adequately reflect the challenge of processing the dynamics of spectrotemporal complex acoustic signals. While fast inhibition in T stellate cells has been attributed to a role in comodulation masking release (Pressnitzer et al., 2001), the inhibitory dynamics in SBCs seem to be too slow for such an effect (Xie and Manis, 2013). Previous studies investigated inhibition using pure tone stimulation, and this is why the functional role of inhibition in signal processing at the ANF-SBC synapse during complex acoustic stimulation has not been fully resolved.

In the present study, we set out to elucidate the functional role of acoustically evoked inhibition at the ANF-SBC synapse using combined in vivo loose-patch recordings with direct iontophoretic manipulation of inhibitory receptors and a detailed input-output signal analysis based on spectrotemporal receptive fields in responses to complex acoustic stimulation. Our results indicate a reliable co-tuning of inhibition with the main excitatory input. While we observed some sharpening of the response in time and frequency, our results suggest that inhibition functions as a gain control that renders the postsynaptic response sparser in time and more reproducible across trials. Temporal sparsity, i.e. a response restricted to fewer time-points, can increase the information per spike while reducing the energy expenditure. Reproducibility, i.e. a more consistent response to identical stimuli, can provide reliable stimulus encoding.

These improvements are a consequence of the combined subtractive/divisive action of glycine (Kuenzel et al., 2011, 2015): The subtractive component enhances the temporal sparsity by raising the threshold for spiking. The divisive component acts primarily as a gain control, which - in conjunction with the co-tuning - maintains the SBC output rate in a smaller range across different stimulus levels. Together these two effects focus the SBC output onto well-timed stimulus events across a wide range of stimulus levels. Thus, inhibition improves the basis for the high-fidelity signal processing in downstream nuclei crucial for sound localization irrespective of the prevailing stimulus levels.

The interaction between acoustically evoked excitation and inhibition is a key constituent at the initial stages of signal processing in the auditory brainstem (Kopp-Scheinpflug et al., 2002; Dehmel et al., 2010; Kuenzel et al., 2011; Keine and Rübsamen, 2015). This study aimed for an investigation of sound-evoked inhibition on the processing of complex structured signals (mimicking broadband acoustic conditions) at the auditory nerve-to-spherical bushy cell synapse (ANF-SBC). A total of 85 units were recorded from the rostral pole of the anteroventral cochlear nucleus (AVCN), the location of large, low-frequency coding SBCs (Bazwinsky et al., 2008). The identification of SBCs was based on the following physiological properties: a discernible prepotential in addition to the complex waveform (Pfeiffer, 1966; Englitz et al., 2009; Typlt et al., 2010), short AP duration (Typlt et al., 2012), high spontaneous firing rates (Smith et al., 1993), and the primary like response pattern to pure-tone stimulation (Blackburn and Sachs, 1989). From these 85 cells, 23 were recorded while simultaneously applying glycine receptor agonists and antagonists. Units had a characteristic frequency (CF) of (mean ± standard deviation) 2.1 ± 0.6 kHz and a minimal threshold of (median [first quartile, third quartile]) 7.5 [0.8, 14.9] dB SPL.

To understand how acoustically evoked inhibition shapes SBC output, the present report focuses on the differential analysis between SBC EPSPs that trigger a postsynaptic AP, i.e. EPSP_succ and EPSPs that fail to trigger an AP, i.e. EPSP_fail. Previous studies showed that during spontaneous activity, EPSP amplitudes are close to threshold, such that not all ANF input spikes trigger an SBC output spike. Also, acoustically evoked inhibition interacts dynamically with the EPSPs and prevents output spikes (Kuenzel et al., 2011; Keine and Rübsamen, 2015).

The respective differences between ANF input and SBC output can be analyzed from the complex waveform of SBC signals consisting of the presynaptic action potential (prepotential, PP) and the excitatory postsynaptic potential (EPSP) which may or may not be followed by an AP (Figure 1A). The fast EPSP rising slope served for the detection of both types of signals, while the dynamics of the signals’ falling slopes reliably allowed to distinguish between the two: (i) EPSP_succ, i.e. EPSPs that successfully trigger postsynaptic APs, and (ii) EPSP_fail, i.e. EPSPs that fail to trigger APs. The maximum falling slope was consistently higher in EPSP_succ than in EPSP_fail (EPSP_succ = 21.1 ± 4.9 vs. EPSP_fail = 4.4 ± 1.2 V/s, difference [Δ] = 16.8 ± 4.3 V/s, p<0.001, paired t-test, n = 62, U1 = 1, Figure 1B left, see also Figure 1—source data 1). The sum of EPSP_fail and EPSP_succ was defined as the ANF input to the SBC, while the subset of EPSP_succ indicated the output ascending to the next level of processing, i.e. the superior olivary complex.

Figure 1

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Figure 1—source data 1

Rising and falling slope for EPSP_fail and EPSP_succ, and failure fractions during spontaneous activity and acoustic stimulation.

Shown are the average maximum rising slopes of the EPSPs and the maximum falling slopes of the events for EPSP_fail and EPSP_succ for each cell in the dataset (n = 62). Data are in V/s. The data set also contains the average failure fraction in the absence of sound and during acoustic stimulation.

https://doi.org/10.7554/eLife.19295.004

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Unlike the falling slopes of the signals, the maximal EPSP rising slopes showed considerable overlap between EPSP_fail and EPSP_succ (Figure 1B middle and right). Still, EPSP_succ had higher average EPSP rising slopes than EPSP_fail (EPSP_succ = 9.9 ± 2.2 V/s vs. EPSP_fail = 7.5 ± 2.2 V/s, Δ = 2.4 ± 1.5 V/s, p<0.001, paired t-test, n = 62, U1 = 0.1). Considering this difference, the EPSP rising slope can – to some degree – predict the probability of AP generation. During spontaneous activity, the failure fraction, defined as the proportion of EPSP_fail of the ANF input (EPSP_succ + EPSP_fail) amounted to 0.28 [0.11, 0.54] with considerable variability between cells (range: 0.01 to 0.91). Acoustic stimulation at the unit’s CF at 50 dB SPL, i.e. within the excitatory response area, increased the failure fraction to 0.49 [0.41, 0.59] rendering the ANF-SBC synapse less reliable during acoustic stimulation (Δ = 0.18 ± 0.29, p<0.001, paired t-test, n = 62, U1 = 0.2, Figure 1C).

Synaptic depression alone fails to account for increased failure rates

The increased incidence of failures during acoustic stimulation has been attributed to the activation of inhibitory inputs (Kopp-Scheinpflug et al., 2002; Kuenzel et al., 2011, 2015; Keine and Rübsamen, 2015). However, also in vitro experiments need to be considered, which showed strong depression at the ANF-SBC synapse (Wang and Manis, 2008; Yang and Xu-Friedman, 2008, 2009; Wang et al., 2010) affecting SBC responsiveness for up to tens of milliseconds (Yang and Xu-Friedman, 2015). Such depression might also suppress SBC spiking in vivo and result in an increased failure fraction during acoustic stimulation. Still, in vivo the impact of depression was shown to be smaller, since ongoing spontaneous activity – completely absent in slice recordings – seems to keep the synapse in a chronically depressed state (Hermann et al., 2007; Lorteije et al., 2009; Yang and Xu-Friedman, 2015). Also, the in vivo calcium concentration was reported to be lower than in the artificial cerebrospinal fluid usually used in slice studies resulting in lower vesicle release probabilities and thus smaller depression (Borst, 2010; Kuenzel et al., 2011; Friauf et al., 2015).

To determine the cause of altered reliability of synaptic transmission at the ANF-SBC synapse, and to dissect the effect of acoustically evoked inhibition from synaptic depression, we first quantified the dependence of the EPSP rising slope on the preceding spontaneous activity. As indicated above, the rising slopes of EPSP_fail and EPSP_succ differ, but still show a considerable range of overlap. For each unit, the EPSP rising slopes were pooled for EPSP_succ and EPSP_fail and binned. Then, the fraction of EPSP_succ was calculated for each bin, and a Boltzmann function was fitted to the EPSP_succ probability distribution (Figure 2A left). The symmetric inflection point of this function indicates the threshold EPSP, i.e. the EPSP slope necessary to trigger an AP with >50% probability. EPSP rising slopes showed strong depression for inter-event intervals (IEI) < 2 ms resulting in high AP failure rates. But, already for IEIs > 5 ms, the preceding activity had only a minor influence on EPSP rising slopes (Figure 2A, middle). Averaging the normalized EPSP slopes across cells showed a facilitating effect for IEI between 2 ms and 20 ms (green markers, Figure 2A right). The threshold EPSP was increased for IEIs < 2 ms, but not for longer IEIs (black line) and the increase in threshold EPSP resulted in an increased failure fraction for IEIs < 2 ms (orange histogram). While IEIs up to 20 ms resulted in increased EPSP slopes, the effect of IEIs on threshold EPSP and failure fraction was limited to short IEIs < 2 ms. These data are consistent with previous studies, suggesting the presence of short-term facilitation rather than depression of synaptic events. Considering only the last preceding IEI, however, disregards the potential impact of previous medium- and short-term afferent activity. Also, in vitro studies yielded the influence of short-term depression at the ANF-SBC synapse to extend well beyond the last IEI (Yang and Xu-Friedman, 2015).

Figure 2

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Figure 2—source data 1

Correlation between EPSP slopes and signal amplitudes on preceding ANF activity.

Shown are the Spearman correlation coefficient values obtained from the correlations of maximum EPSP slopes and signal amplitude vs. preceding ANF activity levels, separately for EPSP_succ and EPSP_fail (n = 62).

https://doi.org/10.7554/eLife.19295.006

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To determine the impact of preceding activity on EPSP strength and AP generation in vivo, the preceding activity of each event was quantified as a weighted sum of all previous events, using an exponentially decaying kernel with a time constant of 60 ms, emphasizing temporally closer events over more distant ones (Figure 2B). The analysis yielded only minor influences of preceding activity on EPSP rising slopes on both EPSP_fail (gray) and EPSP_succ (blue) as shown in a representative cell in Figure 2C (left and middle) and also evidenced for the population of recorded units (Spearman’s rho EPSP_fail = 0.22 ± 0.16 vs. EPSP_succ = 0.24 ± 0.11, Δ = 0.02 ± 0.12, p=0.27, paired t-test, n = 62, U1 = 0.09, Figure 2D, see also Figure 2—source data 1). A small but consistent effect, seen in 61/62 recorded units (98%), was a positive correlation (p<0.001) between preceding ANF activity and EPSP rising slopes indicating a facilitating rather than a depressive influence of higher activity levels in vivo.

Postsynaptic spike depression may also contribute to the increase in postsynaptic spike failures (EPSP_fail). When analyzing the dependence of AP amplitude on preceding SBC spiking activity (exemplary unit shown in Figure 2C right) a significant negative correlation was observed in 92% of the cells (57/62) indicating smaller AP amplitudes after periods of higher SBC activity (Figure 2D). The representative unit shown in Figure 2C (right) shows the respective change in AP amplitude and an inverse effect on the amplitudes of EPSP_fail, consistent with the facilitating influence on EPSP rising slopes. These results are in agreement with previous reports (Kuenzel et al., 2011, 2015) suggesting that endbulbs are mostly in a close-to-threshold state and show low synaptic depression in vivo.

These results suggest that the increased failure fraction during acoustic stimulation in vivo is not explainable by endbulb depression evoked by high firing rates, highlighting the role of acoustically evoked inhibition on the input-output relationship at the ANF-SBC synapse.

Broadband on-CF inhibition shapes SBC tuning

Frequency response areas (FRA) of SBCs show prominent inhibitory sidebands and reduced firing activity in the excitatory field compared to the ANF input (Kopp-Scheinpflug et al., 2002; Kuenzel et al., 2011; Keine and Rübsamen, 2015). Also, about half of the SBCs show pronounced non-monotonic rate-level functions pointing to an impact of inhibition (Kopp-Scheinpflug et al., 2002; Keine and Rübsamen, 2015; Kuenzel et al., 2015) which has been further classified as ‘on-CF inhibition’ and ‘broadband inhibition’ (Winter and Palmer, 1990; Caspary et al., 1994; Kopp-Scheinpflug et al., 2002). In vitro and modeling studies showed that glycinergic inhibition can elevate the threshold EPSP for AP initiation (Xie and Manis, 2013; Kuenzel et al., 2015). Thus the threshold EPSP can serve as a suitable indicator for the activation of inhibitory inputs.

In the present loose-patch recordings, elevation in threshold EPSP (Figure 3Aii/iii) was observed throughout the FRAs (Figure 3Ai/iii) accompanied by an increase in failure fraction (Figure 3Bii). The frequency profile of threshold EPSP elevation closely matched the one of increased failure fraction (Figure 3Aii/Bii, Spearman correlation r_s = 0.7 [0.4, 0.76], p<0.001, Wilcoxon signed rank test, n = 62, U1 = 0.98, population data not shown). The FRA of threshold EPSP elevation was used to quantify the inhibitory influence, which was then compared to the SBC’s excitatory FRA. Both FRAs had similar CFs, defined as the stimulus frequency at which the lowest sound intensity resulted in a significant increase in ANF firing rate (excitatory) or threshold EPSP (inhibitory) (2.2 ± 0.6 kHz vs. 2.2 ± 0.9 kHz, respectively, Δ = 0.03 ± 0.83 kHz, p=0.77, paired t-test, n = 62, U1 = 0.05, Figure 3Ci), but inhibitory FRAs exhibited higher thresholds (excitatory = 4.8 ± 6.1 dB SPL vs. inhibitory = 19.8 ± 16.6 dB SPL, Δ = 15 ± 15.5 dB SPL, p<0.001, paired t-test, n = 62, U1 = 0.2, Figure 3Cii, see also Figure 3—source data 1).

Figure 3

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Figure 3—source data 1

Tuning properties of excitatory and inhibitory inputs onto SBCs.

Shown are the characteristic frequency (in kHz), threshold (in dB SPL), Q-values, asymmetry index, and rate-level gain for the excitatory ANF input and the inhibitory area determined by an elevation in threshold EPSP (n = 62).

https://doi.org/10.7554/eLife.19295.008

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The width of inhibitory and excitatory FRA was determined by calculating Q₁₀ and Q₄₀ values. Both measures were smaller for the inhibitory FRA compared to the excitatory FRA, i.e. inhibition showed reduced frequency selectivity compared to excitation (Q₁₀: excitatory = 2.5 ± 0.6 vs. inhibitory = 1.9 ± 1.1, Δ = 0.5 ± 1.2, p<0.01; Q₄₀: excitatory = 0.96 ± 0.1 vs. inhibitory = 0.6 ± 0.3, Δ = 0.4 ± 0.3, p<0.001, two-way RM ANOVA, Bonferroni-adjusted, n = 62, η² = 0.06, Figure 3Ciii).

Q-values provide information about the sharpness of tuning, but not about the actual shape of the FRA. The tuning shape was evaluated using the asymmetry index (AI, see Materials and methods), with values of 0 indicating symmetric, <0 for low-frequency extended and >0 for high frequency extended tuning curves. While excitatory FRAs showed distinct low-frequency tails, typical for ANF, inhibitory FRAs were mostly symmetrically arranged around CF, partly covering high-frequency ranges above the excitatory response area (AI excitation = –0.96 ± 0.42 vs AI inhibition = –0.26 ± 0.88, Δ = 0.7 ± 0.97, p<0.001, paired t-test, n = 62, U1 = 0.21, Figure 3Civ).

The rate-level function (RLF) of the SBC output was markedly flatter and thus less variable with respect to level than the rate-level function of the excitatory ANF input. The gain of the neuronal response across stimulus level was quantified as rate level gain (RLG), defined as ${\displaystyle \text{RLG}=lo{g}_{10}\left(\frac{F{R}_{max}-F{R}_{min}}{F{R}_{spont}}\right)}$, with ${\displaystyle F{R}_{max}}$ and ${\displaystyle F{R}_{min}}$ being the maximal and minimal firing rate in the RLF and ${\displaystyle F{R}_{spont}}$, the spontaneous firing rate in the absence of acoustic stimulation (see also supplementary Matlab code). This way, overall changes in firing rates are taken into account (e.g. due to spontaneous failures). The output’s rate level function had a gain of 1 ± 0.4 which was significantly less than the input’s (1.4 ± 0.4, Δ = 0.35 ± 0.3, p<0.001, paired t-test, n = 62, U1 = 0.1, Figure 3D).

Taken together, these data demonstrate that inhibition in SBC is co-tuned with excitation and shows a broader and more symmetric frequency profile, which results in flatter rate-level functions and high-frequency inhibitory sidebands at the fringes of the tuning curve (frequently observed in SBC output activity). These findings are consistent with previous reports (Caspary et al., 1994; Kopp-Scheinpflug et al., 2002; Kuenzel et al., 2011). The lower rate-level dependence suggests a gain-normalization function of inhibition, discussed in detail below.

Acoustically evoked inhibition elevates threshold for AP generation

As shown above, the tuning of the inhibitory input on SBCs largely matches the ANF excitation. In vitro studies and modeling suggested inhibition to prevent AP generation in SBC by elevating the threshold EPSP (Xie and Manis, 2013; Kuenzel et al., 2015). Slice studies reported a predominately glycinergic inhibition with a smaller GABAergic contribution (Nerlich et al., 2014a, 2014b), and in vivo studies showed an effective, dose-dependent block of SBC spiking by iontophoretic application of glycine (Keine and Rübsamen, 2015). Consequently, we tested if the activation of glycinergic inputs can directly cause the observed elevation of threshold EPSP. Glycine was applied iontophoretically, mimicking the putative role of glycinergic inhibition, while monitoring the SBC’s spontaneous activity. Indeed, glycine caused an increase in the number of EPSPs that failed to trigger APs, and this specific effect could be blocked by simultaneous application of the glycine receptor antagonist strychnine (Figure 4A). The iontophoretic current for glycine application was adjusted to cause an increase in the spontaneous failure fraction from 0.3 ± 0.17 to 0.64 ± 0.18 (Δ = 0.34 ± 0.12, p<0.001, paired t-test, n = 11, U1 = 0.5) to match the range observed under acoustic stimulation. This increase in failure fraction was accompanied by an elevation in threshold EPSP (threshold EPSP spont = 6.1 ± 2.2 V/s vs threshold EPSP glycine = 8.4 ± 1.8 V/s, Δ = 2.3 ± 0.9 V/s, p<0.001, paired t-test, n = 11, U1 = 0.32, Figure 4B, see also Figure 4—source data 1). The application of the carrier alone had neither an effect on threshold EPSPs (threshold EPSP spont = 6.8±1.6 V/s vs. threshold EPSP carrier = 6.8 ± 1.7, Δ = 0 ± 0.3, p=0.89, paired t-test, n = 9, U1 = 0.1, data not shown) nor on failure fraction (failure fraction spont = 0.29 ± 0.17 vs. failure fraction carrier = 0.27 ± 0.16, Δ = 0.03 ± 0.06, p=0.79, paired t-test, n = 9, U1 = 0.11, data not shown).

Figure 4

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Figure 4—source data 1

Iontophoretic application of glycine and strychnine.

Shown are the threshold EPSPs (in V/s) during control condition and during glycine application (n = 11). The data set also contains the ANF input firing rates, SBC output firing rates (in Hz) and threshold EPSPs during spontaneous activity and acoustic stimulation for both control conditions and during strychnine application (n = 11).

https://doi.org/10.7554/eLife.19295.010

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Next, the contribution of inhibition-mediated threshold EPSP elevation on spike failures during acoustic stimulation was tested. The specific glycine receptor antagonist strychnine was iontophoretically applied to block the acoustically evoked glycinergic inhibition. The effectiveness of the glycine block was tested before sound stimulation by simultaneously applying glycine and strychnine, with the application current for strychnine adjusted to block the effect of iontophoretically applied glycine. The ANF input firing rates were not influenced by the block of inhibition (control = 282 ± 48 Hz vs. strychnine = 283 ± 50 Hz, Δ = 1.2 ± 26.7 Hz, p=0.88, paired t-test, n = 11, U1 = 0.18, Figure 4C). The SBC output rates in the excitatory field, however, were substantially increased under glycine block (control = 122 ± 49 Hz vs. strychnine = 192 ± 39 Hz, Δ = 70 ± 47.9 Hz, p<0.001, paired t-test, n = 11, U1 = 0.5, Figure 4D). We next tested if the block of glycinergic inhibition differentially affects the threshold EPSP during spontaneous activity and during acoustic stimulation. When glycinergic inhibition was blocked, the threshold EPSP was only affected during acoustic stimulation, but not during spontaneous activity (interaction drug × stimulus condition, p<0.01, η² = 0.13, two-way RM ANOVA, Greenhouse-Geisser corrected, n = 11, Figure 4E). Acoustic stimulation at CF under control condition resulted in a significant threshold EPSP elevation (threshold EPSP spont = 5.4 ± 1.6 V/s vs. stim = 8.8 ± 3.1 V/s, Δ = 3.5 ± 2.9 V/s, p<0.01, two-way RM ANOVA, Bonferroni-adjusted, n = 11, U1 = 0.41) and this shift was absent when the inhibition was blocked (threshold EPSP spont = 5.9 ± 2 V/s vs. stim = 5.8 ± 1.8 V/s, Δ = 0.1 ± 1.3 V/s, p=0.82, two-way RM ANOVA, Bonferroni-adjusted, n = 11, U1 = 0.09, Figure 4D). The effects observed under acoustic stimulation were very different from the respective manipulations performed during spontaneous activity. In the absence of acoustic stimulation, the block of glycinergic inhibition had no effect on output rates (control = 51 ± 26 Hz vs. strychnine = 49 ± 20 Hz, Δ = 1.6 ± 18.5 Hz, p=0.79, two-way RM ANOVA, Bonferroni-adjusted, n = 11, U1 = 0.14, data not shown), and threshold EPSP (control = 5.5 ± 2.5 V/s vs strychnine = 5.6 ± 2.6 V/s, Δ = 0.03 ± 0.16 V/s, p=0.53, two-way RM ANOVA, Bonferroni-adjusted, n = 11, U1 = 0.09, Figure 4Eii). Similar to acoustic stimulation, the input rates were not altered during inhibition block (control = 85 ± 26 Hz vs. strychnine = 86 ± 22 Hz, Δ = 0.4 ± 16.6 Hz, p=0.94, two-way RM ANOVA, Bonferroni-adjusted, n = 11, U1 = 0.09, data not shown).

These data suggest a major role of glycinergic inhibition in acoustically evoked signal processing, but a negligible impact during spontaneous activity. Taken together, the data confirms previous reports of broadly tuned, predominantly glycinergic inhibition (Kopp-Scheinpflug et al., 2002; Kuenzel et al., 2011), which decreases and potentially normalizes SBC output firing across different stimulus conditions by an increase in threshold EPSP for spike generation.

Temporal precision improves from ANF to SBC during amplitude and frequency-modulated tones

The results above suggest that acoustically evoked inhibition can considerably influence SBC spiking by increasing the threshold for AP generation. Previous studies directly comparing the ANF input and SBC output showed an increase in temporal precision which has been attributed to the impact of inhibition (Dehmel et al., 2010; Kuenzel et al., 2011; Keine and Rübsamen, 2015). These studies focused on the responses to static pure-tone stimulation leaving the question for a potential influence of acoustically evoked inhibition on signal transmission in a more complex, i.e. a more naturalistic acoustic environment unaddressed.

Considering this issue, we first tested the responses of SBCs to sinusoidal amplitude-modulated (SAM) and frequency-modulated (SFM) acoustic stimuli. SAM stimuli were presented at the respective units’ CF 30 dB above the excitatory threshold with modulation frequencies between 50 Hz and 400 Hz (modulation depth = 100%, Figure 5). The discharge activity of the units showed different degrees of modulation congruent with the SAM for both the ANF input and the SBC output (Figure 5B,C). The AP failure fraction increased from 0.27 ± 0.22 in the absence of acoustic stimulation to 0.43 ± 0.18 during SAM stimulation (Δ = 0.16 ± 0.18, p<0.01, two-way RM ANOVA, Greenhouse-Geisser corrected, n = 14, η² = 0.11, data not shown) and was independent of modulation frequency (factor frequency: p=0.19, η² < 0.01; interaction stimulus type × frequency: p=0.27, two-way RM ANOVA, Greenhouse-Geisser corrected, η² < 0.01, n = 14). The temporal precision of ANF input and SBC output to SAM stimulation was estimated by calculating the vector strength (VS) at different modulation frequencies. The SBC output exhibited consistently higher VS compared to its ANF input (Δ = 0.06 ± 0.04, p<0.001, two-way RM ANOVA, Greenhouse-Geisser corrected, n = 14, η² = 0.09, Figure 5D, see also Figure 5—source data 1), and decreased for modulation frequencies above 200 Hz (factor frequency: p<0.001, η² = 0.09; interaction signal type × frequency: p<0.05, η² < 0.01, two-way RM ANOVA, Greenhouse-Geisser corrected, n = 14). To estimate the degree of modulation of the neural response, the modulation depth was estimated by calculating the standard deviation of the first cycle of the normalized cross-correlation function. Modulation depth was considerably higher at the SBC output (Δ = 0.04 ± 0.04, p<0.001, η² = 0.09) and decreased with modulation frequency (factor frequency: p<0.001, η² = 0.08; interaction signal type × frequency: p=0.12, η² < 0.01, two-way RM ANOVA, Greenhouse-Geisser corrected, n = 14).

Figure 5

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Figure 5—source data 1

Metrics of temporal precision and reproducibility during SAM stimulation.

Shown are the vector strength, modulation depth, reproducibility and Corr_Norm values for each modulation frequency separately for ANF input and SBC output. (n = 14).

https://doi.org/10.7554/eLife.19295.012

Download elife-19295-fig5-data1-v2.mat

The neuronal response to a given stimulus can vary between identical stimulus presentations. This trial-to-trial variability was quantified by calculating the within-cell, across-trial crosscorrelations separately for the ANF input and SBC output. The peak height of the crosscorrelation was termed reproducibility (Joris et al., 2006). It provides a measure of how repeatable the neural response is across trials, given identical stimulus presentations. If the reproducible features of the response encode stimulus properties, e.g. certain salient events, then an increased reproducibility corresponds to more trustable encoding of stimulus information across trials.

The analysis revealed higher reproducibility in the SBC output compared to the ANF input (Δ = 0.4 ± 0.25, p<0.001, η² = 0.09) and also showed a systematic decrease with increasing modulation frequency (factor frequency: p<0.001, η² = 0.04; interaction signal type × frequency: p<0.01, η² < 0.01, two-way RM ANOVA, Greenhouse-Geisser corrected, n = 14). To obtain a better understanding of how precisely the neuronal response reproduces the stimulus envelopes, the delay-adjusted period histograms were correlated to the stimulus envelope resulting in a Corr_Norm between 0.82 and 1 (see Materials and methods for explanation). The analysis revealed higher Corr_Norm for the SBC output compared to the ANF input (Δ = 0.01 ± 0.01, p<0.05, η² = 0.02), which for both signal types increased with modulation frequency (factor frequency: p<0.05, η² = 0.08; interaction signal type × frequency: p=0.19, η² < 0.01, two-way RM ANOVA, Greenhouse-Geisser corrected, n = 14). These analyses show that the increase in temporal precision observed during pure-tone stimulation is maintained during amplitude-modulated sounds across a wide range of modulation frequencies.

In a next step, the modulation of unit discharges to periodic frequency modulations (SFM) was explored. For that purpose, the stimulus intensity was fixed at 30–40 dB above the unit’s threshold and the stimulus frequency modulated between one octave below and two octaves above the unit’s CF. This frequency range covers the whole excitatory area as well as the inhibitory sideband. Similar to the SAM stimulation, the SFM resulted in prominent modulations of the units’ firing rates (Figure 6A) and increased failure fractions (Figure 6B) (spont = 0.34 ± 0.25 vs. stim = 0.6 ± 0.13, Δ = 0.26 0.23, p<0.001, η² = 0.29, data not shown). In contrast to SAM stimulation, SFM led to increased failure rates at higher modulation frequencies (e.g. 0.52 ± 0.13 at 20 Hz vs. 0.65 ± 0.12 at 400 Hz modulation frequency, p<0.001, two-way RM ANOVA, Greenhouse-Geisser corrected, η² = 0.02, n = 19, data not shown). For SFM stimulation – same as for SAM - the SBC output showed higher VS compared to their ANF input (Δ = 0.14 ± 0.09, p<0.001, η² = 0.21, see also Figure 6—source data 1) (Figure 6D left) (factor frequency: p<0.001, η² = 0.48; interaction signal type × frequency: p<0.001, η² = 0.03, two-way RM ANOVA, Greenhouse-Geisser corrected, n = 19). Still, overall the VS of ANF input and SBC output decreased with increasing modulation frequency (Figure 6D left). Notably, the VS of the SBC output deteriorated to a lesser degree than the ANF input. At modulation frequencies of 20 Hz, the output VS was not significantly different between ANF input and SBC output (ANF input = 0.61 ± 0.06 vs. SBC output = 0.63 ± 0.1, Δ = 0.04 ± 0.07, p=0.17, two-way RM ANOVA, Bonferroni-adjusted, n = 19, U1 = 0.16). For modulation frequencies of 400 Hz, however, the VS of the SBC output was considerably higher than the ANF input (ANF input = 0.25 ± 0.08 vs. SBC output 0.4 ± 0.09, Δ = 0.15 ± 0.06, p<0.001, two-way RM ANOVA, Bonferroni-adjusted, n = 19, U1 = 0.5). It has to be considered that the interpretation of VS values is difficult when the period histogram of the neuronal response shows multiple peaks (Figure 6C). We, therefore, used a set of additional measures to describe the neuronal response to SFM stimuli when comparing ANF input and SBC output. The modulation depth was considerably higher for the SBC output than the ANF input (Figure 6D midleft; Δ = 0.24 ± 0.16, p<0.001, η² = 0.28) and strongly depended on the modulation frequency (factor frequency: p<0.001, η² = 0.37; interaction signal type × frequency: p<0.001, η² = 0.03, two-way RM ANOVA, Greenhouse-Geisser corrected, n = 19). The same holds for signal reproducibility (Figure 6D midright; Δ = 1.9 ± 1.2, p<0.001, η² = 0.38) which also showed prominent frequency dependency (factor frequency: p<0.001, η² = 0.26; interaction signal type × frequency: p<0.001, η² = 0.02, two-way RM ANOVA, Greenhouse-Geisser corrected, n = 19). Unlike the previous measures, the normalized correlation between SFM stimulus envelope and neural response revealed a lower reproducibility for the SBC output compared to the ANF input (Figure 6D right; Δ = 0.02 ± 0.03, p<0.001, η² = 0.06), and the difference also holds with respect to the effect of modulation frequency (factor frequency: p<0.001, η² = 0.43; interaction signal type × frequency: p<0.001, η² = 0.03, two-way RM ANOVA, Greenhouse-Geisser corrected, n = 19). Overall, these data suggest that – same as for the SAM stimuli – the SBC output shows temporally increased precision and higher response reproducibility during SFM stimulation.

Figure 6

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Figure 6—source data 1

Metrics of temporal precision and reproducibility during SFM stimulation.

Shown are the vector strength, modulation depth, reproducibility and Corr_Norm values for each modulation frequency separately for ANF input and SBC output. (n = 19).

https://doi.org/10.7554/eLife.19295.014

Download elife-19295-fig6-data1-v2.mat

Spectrotemporal input-output comparison indicates broad, co-tuned, long-lasting inhibition

Above, we demonstrated an improvement in temporal precision and reproducibility in response to SAM and SFM acoustic stimuli. In natural environments, however, the auditory system has to cope with simultaneous dynamic changes in both frequency and amplitude embedded in ambient background noise. To mimic such conditions, while preserving the possibility for a quantifying data analysis, dynamic acoustic stimuli composed of gamma-tones randomly placed in the spectrogram were used (Figure 7A top, randomized gamma-tone sequence, RGS, see Materials and methods for details). The SBC activity can then be characterized using spectrotemporal receptive fields (STRFs). In the present context, STRFs can also be used to quantify the spectrotemporal transformation of response properties across the ANF-SBC synapse, since the respective analysis can be performed for both the ANF input and SBC output. SBC activity was recorded while presenting 20–30 repetitions of identically structured RGS sequences of 30 s duration each. In Figure 7A, the upper panel shows a 200 ms-section of an RGS stimulus used for stimulation of an SBC with a CF of 2 kHz; the middle panel depicts the spike raster plot differentiating between EPSP_succ and EPSP_fail.

Figure 7

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Figure 7—source data 1

Metrics of STRFs obtained with RGS stimulation.

Shown are the excitation, inhibition, spectral half width (in octaves), and temporal half width (in ms) obtained from the STRFs (n = 34).

https://doi.org/10.7554/eLife.19295.016

Download elife-19295-fig7-data1-v2.mat

The neural response to gamma-tones of both the ANF input and SBC output were temporally structured (Figure 7A second and third panel). Failures of signal transmission (Figure 7A gray dots in second panel and histogram in fourth panel) were found to be increased following sequences of activation, suggesting a long-lasting action of inhibition (e.g. Figure 7A fourth panel, where EPSP_fail shows a considerable increase in the responses to the second of the first two peaks).

Separate STRFs were computed for the ANF input (Figure 7B middle left) and the SBC output (Figure 7B middle right). As expected, both STRFs showed common features, e.g. frequency domain of excitation above and below the unit’s CF (in the present example 2 kHz, estimated from single tone tunings) and also the response latency (here 2.5 ms). Importantly, the reduction in the responses establishing a high-frequency sideband was already present in the ANF input to the SBCs and did not become more pronounced in SBC output. Since the ANF activity is not affected by acoustically evoked inhibition, the respective frequency-specific reduction observed in the ANF input to the SBCs likely reflects mechanical interactions in the cochlea, previously described as two-tone suppression (Engebretson and Eldredge, 1968; Sachs and Kiang, 1968; Sellick and Russell, 1979).

To evaluate the signal processing at the ANF-SBC junction, the two STRFs were subtracted from each other after normalizing each by its standard deviation (to compensate for overall firing rate differences, see Materials and methods for details; Figure 7B bottom). This normalization allows a quantification of changes in the tuning shape. The increase in EPSP_fail in the STRF of the SBC output manifests itself as a broad field of negativity in the difference-STRF around CF extending up to ~10 ms after the onset of the effective signal components around 2 kHz. The respective differences between ANF input and SBC output were quantified in all recorded SBCs (n = 34) separately for the positive (red) and negative (blue) regions in the STRF (corresponding to influential spectrotemporal locations in the stimulus prior to the response). Summing all the positive regions revealed a significant reduction from ANFs to SBCs (ANF = 0.38 [0.37, 0.41] vs. SBC = 0.33 [0.31, 0.36], Δ = 0.05 [0.03, 0.07], p<0.001, Wilcoxon signed rank test, n = 34, U1 = 0.16, Figure 7C, see also Figure 7—source data 1). Similarly, the summed negative region in the SBC output was significantly larger in magnitude than the ANF input (ANF = 0.18 [0.15, 0.23] vs. SBC = 0.25 [0.18, 0.29], Δ = 0.04 [0.02, 0.07], p<0.001, Wilcoxon signed rank test, n = 34, U1 = 0.1, Figure 7D). Together, this suggests an inhibitory influence acting broadly with respect to the neuron’s tuning.

We further quantified changes in the shape of the main excitatory peak. The spectral tuning, measured as half-width of the excitatory region, was reduced at the SBC output compared to ANF input, suggesting a spectrally sharper tuning at the SBC output (ANF = 1.2 [0.9, 1.4] octaves vs. SBC = 1 [0.8, 1.2] octaves, Δ = 0.1 [0, 0.2], p<0.001, Wilcoxon signed rank test, n = 34, U1 = 0.01, Figure 7E). Temporal precision, measured correspondingly as the half-width of the excitatory region, was somewhat higher for the SBC output, but did not reach statistical significance (ANF = 2.2 [1.8, 3.4] ms vs. SBC = 2.0 [1.8, 3.1] ms, Δ = 0.08 [-0.1, 0.18], p=0.16, Wilcoxon signed rank test, n = 34, U1 = 0.03, Figure 7F).

The overall shape of the difference-STRF of all units was studied by aligning all STRFs to the peak excitation and averaging them (Figure 7G). As mentioned above, the reduction in the STRF outlasted the excitatory region for up to ~10 ms relative to the onset of the excitatory signal component. Significance was assessed point-wise using t-tests, followed by the Benjamini and Hochberg (1995) algorithm for multiple comparisons applied to the p-values of the t-tests. At a false discovery rate of 0.01, the gray line shows the region of significant deviation.

Overall, the STRF analysis confirmed the presence of inhibition co-tuned with excitation, exhibiting a longer-lasting time-course of about 10 ms with respect to the onset of the excitatory signal component. Consequently, also under dynamic broadband stimulation, inhibition is confirmed to only marginally act above or below the neuron’s excitatory receptive field and results in only a slight spectral sharpening of the SBC output. Next, we addressed the functional consequences of this co-tuned, prolonged inhibition.

Glycinergic inhibition renders SBC responses sparser, more reliable and temporally more precise

The functional consequences of co-tuned inhibition appear less evident than those of narrow, sideband inhibition. The latter can diversely shape the response properties, by reducing responses only for small, off-CF regions. Co-tuned inhibition, on the other hand, has been proposed to contribute to a precisely timed balancing of excitation to keep neurons within their dynamic ranges (Renart et al., 2010). To test for such a mechanism, we quantified properties of the SBC output in comparison to the ANF input with respect to the temporal sparsity of the response and reproducibility across trials. Efficient neural codes have been proposed to show high sparsity, i.e. respond only rarely but then with high firing activity (Field, 1994). Again, the RGS stimulus was used to test the effect of acoustically evoked inhibition under complex acoustic conditions. The results yielded reduced mean firing rates of the SBC output compared to the ANF input (Figure 8Ai, data from an exemplary SBC) and increased sparsity in 28/32 cells (units above line of equality, Figure 8Aii). Sparsity was calculated by relating the variance of the neuronal response to its mean firing rate. The population analysis revealed significantly larger temporal sparsity in the SBC output than in its ANF input (ANF = 0.22 ± 0.08 vs. SBC = 0.31 ± 0.13, Δ = 0.09 ± 0.08, p<0.001, paired t-test, n = 34, U1 = 0.15, Figure 8Aiii, see also Figure 8–source data). Sparsity was calculated by relating the variance of the neuronal response to its mean firing rate (Rolls and Tovee, 1995; Willmore and Tolhurst, 2001), but other measures for sparsity yielded qualitatively similar results (see Materials and methods and Supporting Figure 8).

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Sparsity, reproducibility and temporal dispersion for ANF input and SBC output.

Shown are the sparsity, reproducibility, and temporal dispersion (in ms) separately for ANF input and SBC output for both control condition (n = 34) and during the block of glycinergic inhibition (n = 12).

https://doi.org/10.7554/eLife.19295.018

Download elife-19295-fig8-data1-v2.mat

The reproducibility of the temporal response pattern was quantified by computing across-trial cross-correlations (Figure 8B). For this analysis, the obtained correlograms were divided by the product of the individual firing rates, rendering the results independent of absolute firing rates. Reproducibility was then calculated as the peak of the correlograms measured at 0 ms lag (Figure 8Bi). In 97% (33/34) of all recorded cells, the SBC output exhibited a higher level of reproducibility (units above line of equality, Figure 8Bii). Also, the population analysis yielded a significantly higher reproducibility of the SBC output than the ANF input (ANF = 0.46 [0.37, 0.65] vs. SBC = 0.83 [0.48, 1.3], Δ = 0.35 [0.12, 0.73], p<0.001, Wilcoxon signed rank test, n = 34, U1 = 0.18, Figure 8Biii).

To estimate the temporal precision across trials, the temporal dispersion was quantified as the half-width of the across-trial cross-correlation (Figure 8Ci). Temporal precision across trials improved from ANF input to SBC output in two-thirds of the recorded SBCs (24/34, units below line of equality, Figure 8Cii). Still, population analysis yielded a significant improvement in temporal precision (temporal dispersion: ANF = 7.04 [5.25, 7.93] ms vs SBC = 4.96 [3.47, 6.9] ms, Δ = 1.1 [0, 1.98] ms, p<0.01, Wilcoxon signed rank test, n = 34, U1 = 0.03, Figure 8iii). Response reproducibility across trials renders the response more identifiable for downstream processing stages which rely on precisely timed inputs. The increased sparsity reduces the energy expense by removing spikes which reflect the constant part of the response. Temporal precision of encoding also improved, although this was only observed in about 70% of the cells.

Finally, we directly tested whether the observed changes in response properties were indeed caused by acoustically evoked, glycinergic inhibition. Another set of 12 units were recorded under RGS stimulation, and glycinergic inhibition was blocked by iontophoretic application of strychnine (Figure 8Aiv, Biv, Civ). Like in the experiments reported above, the analysis differentiated between the ANF input to SBCs and the respective SBC output. Under control conditions, cells showed the above-described increase in sparsity and reproducibility at the ANF-to-SBC transition. Blocking the glycinergic inhibition resulted in decreased sparsity of the SBC output (Figure 8Aiv; control = 0.34 ± 0.1 vs. strychnine = 0.27 ± 0.07, Δ = 0.07 ± 0.06, p<0.01, two-way RM ANOVA, Bonferroni–adjusted, n = 12, U1 = 0.33). Also, the block of inhibition caused a decrease in response reproducibility (Figure 8Biv; control = 0.79 ± 0.39 vs. strychnine = 0.53 ± 0.19, Δ = 0.26 ± 0.23, p<0.05, two-way RM ANOVA, Bonferroni-adjusted, n = 12, U1 = 0.21) and an increase in temporal dispersion at the SBC output (Figure 8Civ; control = 5.6 ± 1.6 ms vs. strychnine = 7.5 ± 1.7 ms, Δ = 1.9 ± 1.6 ms, p<0.01, two-way RM ANOVA, Bonferroni-adjusted, n = 12, U1 = 0.21). In summary, the block of inhibition reduced the observed improvements from the ANF input to the SBC output, rendering both more similar. Importantly, the ANF input was not affected by the block of inhibition (Figure 8Aiv, Biv, Civ) (sparsity: control = 0.18 ± 0.05 vs. strychnine = 0.18 ± 0.05, Δ = 0 ± 0.01, p=0.5, U1 = 0.13; reproducibility: control = 0.32 ± 0.11 vs. strychnine = 0.32 ± 0.1, Δ = 0 ± 0.03, p=0.8, U1 = 0.08; temporal dispersion: control = 8.5 ± 0.9 ms vs strychnine = 8.5 ± 1 ms, Δ = 0 ± 0.6 ms, p=0.99, U1 = 0.08, n = 12, two-way RM ANOVA, Bonferroni-adjusted). In comparison with the pre-post data, the pharmacological dataset shows smaller variability across cells, which may be due to the lack of outliers in the latter, smaller dataset. In summary, these data directly show that glycinergic inhibition is a critical factor for the observed improvements from ANF input to the SBC output during complex acoustic stimulation.

Subtractive inhibition suffices to explain the improvement in sparsity, reproducibility, and temporal precision

SBCs have been shown to be influenced by both hyperpolarizing and shunting effects of inhibition (Kuenzel et al., 2011, 2015; Nerlich et al., 2014a). While hyperpolarization has been attributed to a subtractive effect on firing rates (Doiron et al., 2001; Silver, 2010), shunting inhibition has mainly divisive effects (Mitchell and Silver, 2003; Prescott and De Koninck, 2003; Capaday and van Vreeswijk, 2006; Ly and Doiron, 2009). We investigated the functional effect of either type on the response via a simple simulation: either a fixed fraction (divisive, relative to the instantaneous firing rate) or a fixed number (subtractive) of spikes was removed from the ANF spike trains, matching the experimentally observed SBC output rates. Purely divisive inhibition, corresponding to a scaling of the PSTH, does not improve sparsity, reproducibility or temporal precision (Figure 9, purple, Δsparsity = 0 ± 0, p=0.99, Δreproducibility = 0.01 ± 0.02, p=0.25, Δtemporal dispersion = 0.05 ± 0.41, p=0.89, n = 34, one-way RM ANOVA, Bonferroni-adjusted, see also Figure 9—source data 1). On the other hand, a purely subtractive inhibition matches the qualitative effects in the data well, i.e. improves all three properties (Figure 9, green, Δsparsity = 0.24 ± 0.08, p<0.001, Δreproducibility = 1.06 ± 0.73, p<0.001, Δtemporal dispersion = 1.8 ± 1.3 ms, p<0.001, n = 34, one-way RM ANOVA, Bonferroni-adjusted). Quantitatively, the simulated subtractive inhibition leads to larger improvements in sparsity and reproducibility than observed in the experimental data (Figure 9, blue, sparsity: data = 0.31 ± 0.13 vs. subtractive inhibition = 0.46 ± 0.12, Δ = 0.15 ± 0.07, p<0.001; reproducibility: data = 0.97 ± 0.61 vs. subtractive inhibition = 1.59 ± 0.9, Δ = 0.62 ± 0.6, p<0.001, n = 34, one-way RM ANOVA, Bonferroni-adjusted). We verified that similar relations hold for the SAM and SFM stimulation and the measures used in their analyses (Figure 9, Figure 9—figure supplements 1 and 2, respectively). A temporally unspecific, subtractive effect of inhibition might, therefore, be sufficient to explain the improvement in sparsity, reproducibility, and temporal precision. When combined with the divisive, co-tuned gain control, this improvement generalizes to a wide range of stimulus levels.

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Simulation of divisive and subtractive inhibition.

Shown are the values of sparsity, reproducibility and temporal dispersion (in ms) obtained from the simulation of pure subtractive or pure divisive inhibition. The data set also contains the experimentally obtained data separately for ANF input and SBC output.

https://doi.org/10.7554/eLife.19295.021

Download elife-19295-fig9-data1-v2.mat

In the present study, we demonstrate that glycinergic inhibition shapes SBC responses to become sparser and more reproducible for a broad range of stimulation conditions. As a consequence, many temporal measures improve such as vector strength and across-trial temporal precision. We find inhibition to act largely co-tuned with excitation, although its latency and duration exceed the excitatory input, similar to the respective relationship found in the cortex. Therefore, we propose glycinergic inhibition to take a functional role as a gain control and a signal quality enhancer, which optimizes the SBC output for the subsequent high-fidelity integration for sound localization in the MSO and LSO (see below).

1. 1. Aertsen AMHJ
   2. Johannesma PIM
   3. Hermes DJ

   (1980) Spectro-temporal receptive fields of auditory neurons in the grassfrog. II. Analysis of the stimulus-event relation for tonal stimuli

   Biological Cybernetics 38:235–248.

   https://doi.org/10.1007/BF00337016

   • Google Scholar
2. 1. Aertsen AMHJ
   2. Johannesma PIM

   (1980) Spectro-temporal receptive fields of auditory neurons in the grassfrog. I. Characterization of tonal and natural stimuli

   Biological Cybernetics 38:223–234.

   https://doi.org/10.1007/BF00337015

   • Google Scholar
3. 1. Aertsen AMHJ
   2. Olders JHJ
   3. Johannesma PIM

   (1981) Spectro-temporal receptive fields of auditory neurons in the grassfrog. III. Analysis of the stimulus-event relation for natural stimuli

   Biological Cybernetics 39:195–209.

   https://doi.org/10.1007/BF00342772

   • PubMed
   • Google Scholar
4. 1. Ahrens MB
   2. Linden JF
   3. Sahani M

   (2008) Nonlinearities and contextual influences in auditory cortical responses modeled with multilinear spectrotemporal methods

   Journal of Neuroscience 28:1929–1942.

   https://doi.org/10.1523/JNEUROSCI.3377-07.2008

   • PubMed
   • Google Scholar
5. 1. Alcami P
   2. Franconville R
   3. Llano I
   4. Marty A

   (2012) Measuring the firing rate of high-resistance neurons with cell-attached recording

   Journal of Neuroscience 32:3118–3130.

   https://doi.org/10.1523/JNEUROSCI.5371-11.2012

   • PubMed
   • Google Scholar
6. 1. Attwell D
   2. Laughlin SB

   (2001) An energy budget for signaling in the grey matter of the brain

   Journal of Cerebral Blood Flow and Metabolism 21:1133–1145.

   https://doi.org/10.1097/00004647-200110000-00001

   • PubMed
   • Google Scholar
7. 1. Baddeley R
   2. Abbott LF
   3. Booth MC
   4. Sengpiel F
   5. Freeman T
   6. Wakeman EA
   7. Rolls ET

   (1997) Responses of neurons in primary and inferior temporal visual cortices to natural scenes

   Proceedings Biological Sciences 264:1775–1783.

   https://doi.org/10.1098/rspb.1997.0246

   • PubMed
   • Google Scholar
8. 1. Barlow HB

   (1972) Single units and sensation: a neuron doctrine for perceptual psychology?

   Perception 1:371–394.

   https://doi.org/10.1068/p010371

   • PubMed
   • Google Scholar
9. 1. Barlow HB

   (2012)

   Sensory Communication

   216–234, Possible principles underlying the transformations of sensory messages, Sensory Communication, The MIT Press, 10.7551/mitpress/9780262518420.003.0013.

   • Google Scholar
10. 1. Bazwinsky I
    2. Härtig W
    3. Rübsamen R

    (2008) Characterization of cochlear nucleus principal cells of Meriones unguiculatus and Monodelphis domestica by use of calcium-binding protein immunolabeling

    Journal of Chemical Neuroanatomy 35:158–174.

    https://doi.org/10.1016/j.jchemneu.2007.10.003

    • PubMed
    • Google Scholar
11. 1. Bellingham MC
    2. Walmsley B

    (1999) A novel presynaptic inhibitory mechanism underlies paired pulse depression at a fast central synapse

    Neuron 23:159–170.

    https://doi.org/10.1016/S0896-6273(00)80762-X

    • PubMed
    • Google Scholar
12. 1. Benjamini Y
    2. Hochberg Y

    (1995)

    Controlling the false discovery rate: a practical and powerful approach to multiple testing

    Journal of the Royal Statistical Society Series B 57:289–300.

    • Google Scholar
13. 1. Blackburn CC
    2. Sachs MB

    (1989)

    Classification of unit types in the anteroventral cochlear nucleus: PST histograms and regularity analysis

    Journal of Neurophysiology 62:1303–1329.

    • PubMed
    • Google Scholar
14. 1. Bonferroni CE

    (1936)

    Teoria statistica delle classi e calcolo delle probabilità

    Pubbl Del R Ist Super Di Sci Econ E Commer Di Firenze 8:3–62.

    • Google Scholar
15. 1. Borst JG

    (2010) The low synaptic release probability in vivo

    Trends in Neurosciences 33:259–266.

    https://doi.org/10.1016/j.tins.2010.03.003

    • PubMed
    • Google Scholar
16. Thesis

    1. Bourk TR

    (1976) Electrical responses of neural units in the anteroventral cochlear nucleus of the cat

    Department of Electrical Engineering and Computer Science,Massachusetts Institute of Technology.

    http://dspace.mit.edu/handle/1721.1/58313

    • Google Scholar
17. 1. Brand A
    2. Behrend O
    3. Marquardt T
    4. McAlpine D
    5. Grothe B

    (2002) Precise inhibition is essential for microsecond interaural time difference coding

    Nature 417:543–547.

    https://doi.org/10.1038/417543a

    • PubMed
    • Google Scholar
18. 1. Brawer JR
    2. Morest DK

    (1975) Relations between auditory nerve endings and cell types in the cat's anteroventral cochlear nucleus seen with the Golgi method and Nomarski optics

    The Journal of Comparative Neurology 160:491–506.

    https://doi.org/10.1002/cne.901600406

    • PubMed
    • Google Scholar
19. 1. Burger RM
    2. Pollak GD

    (2001)

    Reversible inactivation of the dorsal nucleus of the lateral lemniscus reveals its role in the processing of multiple sound sources in the inferior colliculus of bats

    Journal of Neuroscience 21:4830–4843.

    • PubMed
    • Google Scholar
20. 1. Campagnola L
    2. Manis PB

    (2014) A map of functional synaptic connectivity in the mouse anteroventral cochlear nucleus

    Journal of Neuroscience 34:2214–2230.

    https://doi.org/10.1523/JNEUROSCI.4669-13.2014

    • PubMed
    • Google Scholar
21. 1. Capaday C
    2. van Vreeswijk C

    (2006) Direct control of firing rate gain by dendritic shunting inhibition

    Journal of Integrative Neuroscience 5:199–222.

    https://doi.org/10.1142/s021963520600115x

    • PubMed
    • Google Scholar
22. 1. Cariani PA
    2. Delgutte B

    (1996)

    Neural correlates of the pitch of complex tones. I. Pitch and pitch salience

    Journal of Neurophysiology 76:1698–1716.

    • PubMed
    • Google Scholar
23. 1. Caspary DM
    2. Backoff PM
    3. Finlayson PG
    4. Palombi PS

    (1994)

    Inhibitory inputs modulate discharge rate within frequency receptive fields of anteroventral cochlear nucleus neurons

    Journal of Neurophysiology 72:2124–2133.

    • PubMed
    • Google Scholar
24. 1. Clarke S
    2. Adriani M
    3. Bellmann A

    (1998) Distinct short-term memory systems for sound content and sound localization

    NeuroReport 9:3433–3437.

    https://doi.org/10.1097/00001756-199810260-00018

    • PubMed
    • Google Scholar
25. 1. Couchman K
    2. Grothe B
    3. Felmy F

    (2010) Medial superior olivary neurons receive surprisingly few excitatory and inhibitory inputs with balanced strength and short-term dynamics

    Journal of Neuroscience 30:17111–17121.

    https://doi.org/10.1523/JNEUROSCI.1760-10.2010

    • PubMed
    • Google Scholar
26. 1. David SV
    2. Fritz JB
    3. Shamma SA

    (2012) Task reward structure shapes rapid receptive field plasticity in auditory cortex

    PNAS 109:2144–2149.

    https://doi.org/10.1073/pnas.1117717109

    • PubMed
    • Google Scholar
27. 1. de Cheveigné A

    (1998) Cancellation model of pitch perception

    The Journal of the Acoustical Society of America 103:1261–1271.

    https://doi.org/10.1121/1.423232

    • Google Scholar
28. 1. Dehmel S
    2. Kopp-Scheinpflug C
    3. Weick M
    4. Dörrscheidt GJ
    5. Rübsamen R

    (2010) Transmission of phase-coupling accuracy from the auditory nerve to spherical bushy cells in the Mongolian gerbil

    Hearing Research 268:234–249.

    https://doi.org/10.1016/j.heares.2010.06.005

    • PubMed
    • Google Scholar
29. 1. Denham S

    (2005) Pitch detection of dynamic iterated rippled noise by humans and a modified auditory model

    Bio Systems 79:199–206.

    https://doi.org/10.1016/j.biosystems.2004.09.008

    • PubMed
    • Google Scholar
30. 1. Doiron B
    2. Longtin A
    3. Berman N
    4. Maler L

    (2001) Subtractive and divisive inhibition: effect of voltage-dependent inhibitory conductances and noise

    Neural Computation 13:227–248.

    https://doi.org/10.1162/089976601300014691

    • PubMed
    • Google Scholar
31. 1. Engebretson AM
    2. Eldredge DH

    (1968) Model for the nonlinear characteristics of cochlear potentials

    The Journal of the Acoustical Society of America 44:548–554.

    https://doi.org/10.1121/1.1911119

    • PubMed
    • Google Scholar
32. 1. Englitz B
    2. Ahrens M
    3. Tolnai S
    4. Rübsamen R
    5. Sahani M
    6. Jost J

    (2010) Multilinear models of single cell responses in the medial nucleus of the trapezoid body

    Network 21:91–124.

    https://doi.org/10.3109/09548981003801996

    • PubMed
    • Google Scholar
33. 1. Englitz B
    2. Tolnai S
    3. Typlt M
    4. Jost J
    5. Rübsamen R

    (2009) Reliability of synaptic transmission at the synapses of Held in vivo under acoustic stimulation

    PloS One 4:e7014.

    https://doi.org/10.1371/journal.pone.0007014

    • PubMed
    • Google Scholar
34. 1. Escabi MA
    2. Schreiner CE

    (2002)

    Nonlinear spectrotemporal sound analysis by neurons in the auditory midbrain

    Journal of Neuroscience 22:4114–4131.

    • PubMed
    • Google Scholar
35. 1. Field DJ

    (1994) What is the Goal of Sensory Coding?

    Neural Computation 6:559–601.

    https://doi.org/10.1162/neco.1994.6.4.559

    • Google Scholar
36. 1. Fischl MJ
    2. Weimann SR
    3. Kearse MG
    4. Burger RM

    (2014) Slowly emerging glycinergic transmission enhances inhibition in the sound localization pathway of the avian auditory system

    Journal of Neurophysiology 111:565–572.

    https://doi.org/10.1152/jn.00640.2013

    • PubMed
    • Google Scholar
37. 1. Fontaine B
    2. Peña JL
    3. Brette R

    (2014) Spike-threshold adaptation predicted by membrane potential dynamics in vivo

    PLoS Computational Biology 10:e1003560.

    https://doi.org/10.1371/journal.pcbi.1003560

    • Google Scholar
38. 1. Franken TP
    2. Bremen P
    3. Joris PX

    (2014) Coincidence detection in the medial superior olive: mechanistic implications of an analysis of input spiking patterns

    Frontiers in Neural Circuits 8:42.

    https://doi.org/10.3389/fncir.2014.00042

    • PubMed
    • Google Scholar
39. 1. Friauf E
    2. Fischer AU
    3. Fuhr MF

    (2015) Synaptic plasticity in the auditory system: a review

    Cell and Tissue Research 361:177–213.

    https://doi.org/10.1007/s00441-015-2176-x

    • PubMed
    • Google Scholar
40. 1. Fujino K
    2. Oertel D

    (2001)

    Cholinergic modulation of stellate cells in the mammalian ventral cochlear nucleus

     Journal of Neuroscience 21:7372–7383.

    • PubMed
    • Google Scholar
41. 1. Fukui I
    2. Sato T
    3. Ohmori H

    (2006) Improvement of phase information at low sound frequency in nucleus magnocellularis of the chicken

    Journal of Neurophysiology 96:633–641.

    https://doi.org/10.1152/jn.00916.2005

    • PubMed
    • Google Scholar
42. 1. Gai Y
    2. Carney LH

    (2008) Influence of inhibitory inputs on rate and timing of responses in the anteroventral cochlear nucleus

    Journal of Neurophysiology 99:1077–1095.

    https://doi.org/10.1152/jn.00708.2007

    • PubMed
    • Google Scholar
43. 1. Goldberg JM
    2. Brown PB

    (1969)

    Response of binaural neurons of dog superior olivary complex to dichotic tonal stimuli: some physiological mechanisms of sound localization

    Journal of Neurophysiology 32:613–636.

    • PubMed
    • Google Scholar
44. 1. Goyer D
    2. Kurth S
    3. Gillet C
    4. Keine C
    5. Rübsamen R
    6. Kuenzel T

    (2016) Slow Cholinergic Modulation of Spike Probability in Ultra-Fast Time-Coding Sensory Neurons

    eNeuro 3:.

    https://doi.org/10.1523/ENEURO.0186-16.2016

    • PubMed
    • Google Scholar
45. 1. Graham B
    2. Willshaw D

    (1997) Capacity and information efficiency of the associative net

    Network: Computation in Neural Systems 8:35–54.

    https://doi.org/10.1088/0954-898X_8_1_005

    • Google Scholar
46. 1. Graham DJ
    2. Field DJ

    (2007)

    Evolution of Nervous Systems

    181–187, Sparse coding in the neocortex, Evolution of Nervous Systems, Elsevier, 10.1016/b0-12-370878-8/00064-1.

    • Google Scholar
47. 1. Grothe B
    2. Sanes DH

    (1993)

    Bilateral inhibition by glycinergic afferents in the medial superior olive

    Journal of Neurophysiology 69:1192–1196.

    • PubMed
    • Google Scholar
48. 1. Gómez-Nieto R
    2. Rubio ME

    (2009) A bushy cell network in the rat ventral cochlear nucleus

    The Journal of Comparative Neurology 516:241–263.

    https://doi.org/10.1002/cne.22139

    • PubMed
    • Google Scholar
49. 1. Havey DC
    2. Caspary DM

    (1980) A simple technique for constructing 'piggy-back' multibarrel microelectrodes

    Electroencephalography and Clinical Neurophysiology 48:249–251.

    https://doi.org/10.1016/0013-4694(80)90313-2

    • PubMed
    • Google Scholar
50. 1. Hentschke H
    2. Stüttgen MC

    (2011) Computation of measures of effect size for neuroscience data sets

    The European Journal of Neuroscience 34:1887–1894.

    https://doi.org/10.1111/j.1460-9568.2011.07902.x

    • PubMed
    • Google Scholar
51. 1. Hermann J
    2. Pecka M
    3. von Gersdorff H
    4. Grothe B
    5. Klug A

    (2007) Synaptic transmission at the calyx of Held under in vivo like activity levels

    Journal of Neurophysiology 98:807–820.

    https://doi.org/10.1152/jn.00355.2007

    • PubMed
    • Google Scholar
52. 1. Huang C
    2. Resnik A
    3. Celikel T
    4. Englitz B

    (2016) Adaptive spike threshold enables robust and temporally precise neuronal Encoding

    PLoS Computational Biology 12:e1004984.

    https://doi.org/10.1371/journal.pcbi.1004984

    • Google Scholar
53. 1. Isaacson JS
    2. Scanziani M

    (2011) How inhibition shapes cortical activity

    Neuron 72:231–243.

    https://doi.org/10.1016/j.neuron.2011.09.027

    • PubMed
    • Google Scholar
54. 1. Joris PX
    2. Carney LH
    3. Smith PH
    4. Yin TC

    (1994a)

    Enhancement of neural synchronization in the anteroventral cochlear nucleus. I. Responses to tones at the characteristic frequency

    Journal of Neurophysiology 71:1022–1036.

    • PubMed
    • Google Scholar
55. 1. Joris PX
    2. Louage DH
    3. Cardoen L
    4. van der Heijden M

    (2006) Correlation index: a new metric to quantify temporal coding

    Hearing Research 216-217:19–30.

    https://doi.org/10.1016/j.heares.2006.03.010

    • PubMed
    • Google Scholar
56. 1. Joris PX
    2. Smith PH
    3. Yin TC
    4. Carney LH

    (1994b)

    Enhancement of neural synchronization in the anteroventral cochlear nucleus. II. Responses in the tuning curve tail

    Journal of Neurophysiology 71:1037–1051.

    • PubMed
    • Google Scholar
57. 1. Joris PX

    (2003)

    Interaural time sensitivity dominated by cochlea-induced envelope patterns

     Journal of Neuroscience  23:6345–6350.

    • PubMed
    • Google Scholar
58. 1. Joris PX

    (2016) Entracking as a Brain Stem Code for Pitch: The Butte Hypothesis

    Advances in Experimental Medicine and Biology 894:347–354.

    https://doi.org/10.1007/978-3-319-25474-6_36

    • PubMed
    • Google Scholar
59. 1. Juiz JM
    2. Helfert RH
    3. Bonneau JM
    4. Wenthold RJ
    5. Altschuler RA

    (1996) Three classes of inhibitory amino acid terminals in the cochlear nucleus of the guinea pig

    The Journal of Comparative Neurology 373:11–26.

    https://doi.org/10.1002/(SICI)1096-9861(19960909)373:1&amp;lt;11::AID-CNE2&amp;gt;3.0.CO;2-G

    • PubMed
    • Google Scholar
60. 1. Katzner S
    2. Busse L
    3. Carandini M

    (2011) GABAA inhibition controls response gain in visual cortex

    Journal of Neuroscience 31:5931–5941.

    https://doi.org/10.1523/JNEUROSCI.5753-10.2011

    • PubMed
    • Google Scholar
61. 1. Keine C
    2. Rübsamen R

    (2015) Inhibition shapes acoustic responsiveness in spherical bushy cells

    Journal of Neuroscience 35:8579–8592.

    https://doi.org/10.1523/JNEUROSCI.0133-15.2015

    • PubMed
    • Google Scholar
62. 1. Kim PJ
    2. Young ED

    (1994) Comparative analysis of spectro-temporal receptive fields, reverse correlation functions, and frequency tuning curves of auditory-nerve fibers

    The Journal of the Acoustical Society of America 95:410–422.

    https://doi.org/10.1121/1.408335

    • PubMed
    • Google Scholar
63. 1. Klein DJ
    2. Depireux DA
    3. Simon JZ
    4. Shamma SA

    (2000) Robust spectrotemporal reverse correlation for the auditory system: optimizing stimulus design

    Journal of Computational Neuroscience 9:85–111.

    https://doi.org/10.1023/A:1008990412183

    • PubMed
    • Google Scholar
64. 1. Kolston J
    2. Osen KK
    3. Hackney CM
    4. Ottersen OP
    5. Storm-Mathisen J

    (1992) An atlas of glycine- and GABA-like immunoreactivity and colocalization in the cochlear nuclear complex of the guinea pig

    Anatomy and Embryology 186:443–465.

    https://doi.org/10.1007/BF00185459

    • PubMed
    • Google Scholar
65. 1. Kopp-Scheinpflug C
    2. Dehmel S
    3. Dörrscheidt GJ
    4. Rübsamen R

    (2002)

    Interaction of excitation and inhibition in anteroventral cochlear nucleus neurons that receive large endbulb synaptic endings

    Journal of Neuroscience 22:11004–11018.

    • PubMed
    • Google Scholar
66. 1. Kowalski N
    2. Depireux DA
    3. Shamma SA

    (1996)

    Analysis of dynamic spectra in ferret primary auditory cortex. I. Characteristics of single-unit responses to moving ripple spectra

    Journal of Neurophysiology 76:3503–3523.

    • PubMed
    • Google Scholar
67. 1. Kuenzel T
    2. Borst JG
    3. van der Heijden M

    (2011) Factors controlling the input-output relationship of spherical bushy cells in the gerbil cochlear nucleus

    Journal of Neuroscience 31:4260–4273.

    https://doi.org/10.1523/JNEUROSCI.5433-10.2011

    • PubMed
    • Google Scholar
68. 1. Kuenzel T
    2. Nerlich J
    3. Wagner H
    4. Rübsamen R
    5. Milenkovic I

    (2015) Inhibitory properties underlying non-monotonic input-output relationship in low-frequency spherical bushy neurons of the gerbil

    Frontiers in Neural Circuits 9:1–14.

    https://doi.org/10.3389/fncir.2015.00014

    • PubMed
    • Google Scholar
69. 1. Kössl M
    2. Vater M

    (1989)

    Noradrenaline enhances temporal auditory contrast and neuronal timing precision in the cochlear nucleus of the mustached bat

    Journal of Neuroscience 9:4169–4178.

    • PubMed
    • Google Scholar
70. 1. Levene H

    (1960)

    Contributions to Probability and Statistics

    278–292, Robust tests for equality of variances, Contributions to Probability and Statistics, Stanford University Press.

    • Google Scholar
71. 1. Levy WB
    2. Baxter RA

    (1996) Energy efficient neural codes

    Neural Computation 8:531–543.

    https://doi.org/10.1162/neco.1996.8.3.531

    • PubMed
    • Google Scholar
72. 1. Licklider JCR

    (1951) A Duplex Theory of Pitch Perception

    The Journal of the Acoustical Society of America 23:147.

    https://doi.org/10.1121/1.1917296

    • Google Scholar
73. 1. Lim R
    2. Alvarez FJ
    3. Walmsley B

    (2000) GABA mediates presynaptic inhibition at glycinergic synapses in a rat auditory brainstem nucleus

    The Journal of Physiology 525 Pt 2:447–459.

    https://doi.org/10.1111/j.1469-7793.2000.t01-1-00447.x

    • PubMed
    • Google Scholar
74. 1. Liu BH
    2. Li P
    3. Li YT
    4. Sun YJ
    5. Yanagawa Y
    6. Obata K
    7. Zhang LI
    8. Tao HW

    (2009) Visual receptive field structure of cortical inhibitory neurons revealed by two-photon imaging guided recording

    Journal of Neuroscience 29:10520–10532.

    https://doi.org/10.1523/JNEUROSCI.1915-09.2009

    • PubMed
    • Google Scholar
75. 1. Liu BH
    2. Li YT
    3. Ma WP
    4. Pan CJ
    5. Zhang LI
    6. Tao HW

    (2011) Broad inhibition sharpens orientation selectivity by expanding input dynamic range in mouse simple cells

    Neuron 71:542–554.

    https://doi.org/10.1016/j.neuron.2011.06.017

    • PubMed
    • Google Scholar
76. 1. Lorteije JA
    2. Rusu SI
    3. Kushmerick C
    4. Borst JG

    (2009) Reliability and precision of the mouse calyx of Held synapse

    Journal of Neuroscience 29:13770–13784.

    https://doi.org/10.1523/JNEUROSCI.3285-09.2009

    • PubMed
    • Google Scholar
77. 1. Louage DH
    2. van der Heijden M
    3. Joris PX

    (2005) Enhanced temporal response properties of anteroventral cochlear nucleus neurons to broadband noise

    Journal of Neuroscience 25:1560–1570.

    https://doi.org/10.1523/JNEUROSCI.4742-04.2005

    • PubMed
    • Google Scholar
78. 1. Ly C
    2. Doiron B

    (2009) Divisive gain modulation with dynamic stimuli in integrate-and-fire neurons

    PLoS Computational Biology 5:e1000365.

    https://doi.org/10.1371/journal.pcbi.1000365

    • PubMed
    • Google Scholar
79. 1. Maeder PP
    2. Meuli RA
    3. Adriani M
    4. Bellmann A
    5. Fornari E
    6. Thiran JP
    7. Pittet A
    8. Clarke S

    (2001) Distinct pathways involved in sound recognition and localization: a human fMRI study

    NeuroImage 14:802–816.

    https://doi.org/10.1006/nimg.2001.0888

    • PubMed
    • Google Scholar
80. 1. Mahendrasingam S
    2. Wallam CA
    3. Polwart A
    4. Hackney CM

    (2004) An immunogold investigation of the distribution of GABA and glycine in nerve terminals on the somata of spherical bushy cells in the anteroventral cochlear nucleus of guinea pig

     European Journal of Neuroscience 19:993–1004.

    https://doi.org/10.1111/j.1460-9568.2004.03193.x

    • PubMed
    • Google Scholar
81. Book

    1. Mardia KV

    (1972)

    Statistics of Directional Data

    London: Academic Press.

    • Google Scholar
82. 1. Mauchly JW

    (1940) Significance test for sphericity of a normal n-variate distribution

    The Annals of Mathematical Statistics 11:204–209.

    https://doi.org/10.1214/aoms/1177731915

    • Google Scholar
83. 1. Meddis R
    2. Hewitt MJ

    (1991) Virtual pitch and phase sensitivity of a computer model of the auditory periphery. I: Pitch identification

    The Journal of the Acoustical Society of America 89:2866–2882.

    https://doi.org/10.1121/1.400725

    • Google Scholar
84. 1. Mitchell SJ
    2. Silver RA

    (2003) Shunting inhibition modulates neuronal gain during synaptic excitation

    Neuron 38:433–445.

    https://doi.org/10.1016/S0896-6273(03)00200-9

    • PubMed
    • Google Scholar
85. 1. Mitchison GJ
    2. Durbin RM

    (1989) Bounds on the learning capacity of some multi-layer networks

    Biological Cybernetics 60:345–365.

    https://doi.org/10.1007/BF00204772

    • Google Scholar
86. 1. Myoga MH
    2. Lehnert S
    3. Leibold C
    4. Felmy F
    5. Grothe B

    (2014) Glycinergic inhibition tunes coincidence detection in the auditory brainstem

    Nature Communications 5:3790.

    https://doi.org/10.1038/ncomms4790

    • PubMed
    • Google Scholar
87. 1. Nayagam DA
    2. Clarey JC
    3. Paolini AG

    (2005) Powerful, onset inhibition in the ventral nucleus of the lateral lemniscus

    Journal of Neurophysiology 94:1651–1654.

    https://doi.org/10.1152/jn.00167.2005

    • PubMed
    • Google Scholar
88. 1. Nerlich J
    2. Keine C
    3. Rübsamen R
    4. Burger RM
    5. Milenkovic I

    (2014a) Activity-dependent modulation of inhibitory synaptic kinetics in the cochlear nucleus

    Frontiers in Neural Circuits 8:1–14.

    https://doi.org/10.3389/fncir.2014.00145

    • Google Scholar
89. 1. Nerlich J
    2. Kuenzel T
    3. Keine C
    4. Korenic A
    5. Rubsamen R
    6. Milenkovic I

    (2014b) Dynamic fidelity control to the central auditory system: synergistic glycine/gabaergic inhibition in the cochlear nucleus

    Journal of Neuroscience 34:11604–11620.

    https://doi.org/10.1523/JNEUROSCI.0719-14.2014

    • Google Scholar
90. 1. Nicol MJ
    2. Walmsley B

    (2002) Ultrastructural basis of synaptic transmission between endbulbs of Held and bushy cells in the rat cochlear nucleus

    The Journal of Physiology 539:713–723.

    https://doi.org/10.1113/jphysiol.2001.012972

    • PubMed
    • Google Scholar
91. 1. Niell CM
    2. Stryker MP

    (2008) Highly selective receptive fields in mouse visual cortex

    Journal of Neuroscience 28:7520–7536.

    https://doi.org/10.1523/JNEUROSCI.0623-08.2008

    • PubMed
    • Google Scholar
92. 1. Olshausen BA
    2. Field DJ

    (2004) Sparse coding of sensory inputs

    Current Opinion in Neurobiology 14:481–487.

    https://doi.org/10.1016/j.conb.2004.07.007

    • PubMed
    • Google Scholar
93. 1. Paolini AG
    2. FitzGerald JV
    3. Burkitt AN
    4. Clark GM

    (2001) Temporal processing from the auditory nerve to the medial nucleus of the trapezoid body in the rat

    Hearing Research 159:101–116.

    https://doi.org/10.1016/S0378-5955(01)00327-6

    • PubMed
    • Google Scholar
94. 1. Pecka M
    2. Zahn TP
    3. Saunier-Rebori B
    4. Siveke I
    5. Felmy F
    6. Wiegrebe L
    7. Klug A
    8. Pollak GD
    9. Grothe B

    (2007) Inhibiting the inhibition: a neuronal network for sound localization in reverberant environments

    Journal of Neuroscience 27:1782–1790.

    https://doi.org/10.1523/JNEUROSCI.5335-06.2007

    • PubMed
    • Google Scholar
95. 1. Pfeiffer RR

    (1966) Anteroventral cochlear nucleus: wave forms of extracellularly recorded spike potentials

    Science 154:667–668.

    https://doi.org/10.1126/science.154.3749.667

    • PubMed
    • Google Scholar
96. 1. Plauška A
    2. Borst JG
    3. van der Heijden M

    (2016) Predicting binaural responses from monaural responses in the gerbil medial superior olive

    Journal of Neurophysiology 115:2950–2963.

    https://doi.org/10.1152/jn.01146.2015

    • PubMed
    • Google Scholar
97. 1. Poo C
    2. Isaacson JS

    (2009) Odor representations in olfactory cortex: "sparse" coding, global inhibition, and oscillations

    Neuron 62:850–861.

    https://doi.org/10.1016/j.neuron.2009.05.022

    • PubMed
    • Google Scholar
98. 1. Prescott SA
    2. De Koninck Y

    (2003) Gain control of firing rate by shunting inhibition: roles of synaptic noise and dendritic saturation

    PNAS 100:2076–2081.

    https://doi.org/10.1073/pnas.0337591100

    • PubMed
    • Google Scholar
99. 1. Pressnitzer D
    2. Meddis R
    3. Delahaye R
    4. Winter IM

    (2001)

    Physiological correlates of comodulation masking release in the mammalian ventral cochlear nucleus

    Journal of Neuroscience 21:6377–6386.

    • PubMed
    • Google Scholar
100. 1. Renart A
     2. de la Rocha J
     3. Bartho P
     4. Hollender L
     5. Parga N
     6. Reyes A
     7. Harris KD

     (2010) The asynchronous state in cortical circuits

     Science 327:587–590.

     https://doi.org/10.1126/science.1179850

     • PubMed
     • Google Scholar
101. 1. Rolls ET
     2. Tovee MJ

     (1995)

     Sparseness of the neuronal representation of stimuli in the primate temporal visual cortex

     Journal of Neurophysiology 73:713–726.

     • PubMed
     • Google Scholar
102. 1. Rothman JS
     2. Manis PB

     (2003) The roles potassium currents play in regulating the electrical activity of ventral cochlear nucleus neurons

     Journal of Neurophysiology 89:3097–3113.

     https://doi.org/10.1152/jn.00127.2002

     • PubMed
     • Google Scholar
103. 1. Ryugo DK
     2. Sento S

     (1991) Synaptic connections of the auditory nerve in cats: relationship between endbulbs of held and spherical bushy cells

     The Journal of Comparative Neurology 305:35–48.

     https://doi.org/10.1002/cne.903050105

     • PubMed
     • Google Scholar
104. 1. Sachs MB
     2. Kiang NY

     (1968) Two-tone inhibition in auditory-nerve fibers

     The Journal of the Acoustical Society of America 43:1120–1128.

     https://doi.org/10.1121/1.1910947

     • PubMed
     • Google Scholar
105. 1. Saint Marie RL
     2. Benson CG
     3. Ostapoff EM
     4. Morest DK

     (1991) Glycine immunoreactive projections from the dorsal to the anteroventral cochlear nucleus

     Hearing Research 51:11–28.

     https://doi.org/10.1016/0378-5955(91)90003-R

     • PubMed
     • Google Scholar
106. 1. Schofield BR
     2. Cant NB

     (1992) Organization of the superior olivary complex in the guinea pig: II. Patterns of projection from the periolivary nuclei to the inferior colliculus

     The Journal of Comparative Neurology 317:438–455.

     https://doi.org/10.1002/cne.903170409

     • PubMed
     • Google Scholar
107. 1. Schwartz AM
     2. Gulley RL

     (1978) Non-primary afferents to the principal cells of the rostral anteroventral cochlear nucleus of the guinea pig

     The American Journal of Anatomy 153:489–508.

     https://doi.org/10.1002/aja.1001530402

     • PubMed
     • Google Scholar
108. 1. Sellick PM
     2. Russell IJ

     (1979) Two-tone suppression in cochlear hair cells

     Hearing Research 1:227–236.

     https://doi.org/10.1016/0378-5955(79)90016-9

     • Google Scholar
109. 1. Shapiro S
     2. Wilk M

     (1965) An analysis of variance test for normality

     Biometrika 52:591–611.

     https://doi.org/10.1093/biomet/52.3-4.591

     • Google Scholar
110. 1. Silver RA

     (2010) Neuronal arithmetic

     Nature Reviews. Neuroscience 11:474–489.

     https://doi.org/10.1038/nrn2864

     • PubMed
     • Google Scholar
111. 1. Smith PH
     2. Joris PX
     3. Carney LH
     4. Yin TC

     (1991) Projections of physiologically characterized globular bushy cell axons from the cochlear nucleus of the cat

     The Journal of Comparative Neurology 304:387–407.

     https://doi.org/10.1002/cne.903040305

     • PubMed
     • Google Scholar
112. 1. Smith PH
     2. Joris PX
     3. Yin TC

     (1993) Projections of physiologically characterized spherical bushy cell axons from the cochlear nucleus of the cat: evidence for delay lines to the medial superior olive

     The Journal of Comparative Neurology 331:245–260.

     https://doi.org/10.1002/cne.903310208

     • PubMed
     • Google Scholar
113. 1. Sohya K
     2. Kameyama K
     3. Yanagawa Y
     4. Obata K
     5. Tsumoto T

     (2007) GABAergic neurons are less selective to stimulus orientation than excitatory neurons in layer II/III of visual cortex, as revealed by in vivo functional Ca2+ imaging in transgenic mice

     Journal of Neuroscience 27:2145–2149.

     https://doi.org/10.1523/JNEUROSCI.4641-06.2007

     • PubMed
     • Google Scholar
114. 1. Sonntag M
     2. Englitz B
     3. Typlt M
     4. Rübsamen R

     (2011) The calyx of Held develops adult-like dynamics and reliability by hearing onset in the mouse in vivo

     Journal of Neuroscience 31:6699–6709.

     https://doi.org/10.1523/JNEUROSCI.0575-11.2011

     • PubMed
     • Google Scholar
115. 1. Spearman C

     (1904) The Proof and Measurement of Association between two Things

     The American Journal of Psychology 15:72.

     https://doi.org/10.2307/1412159

     • Google Scholar
116. 1. Spencer MJ
     2. Nayagam DAX
     3. Clarey JC
     4. Paolini AG
     5. Meffin H
     6. Burkitt AN
     7. Grayden DB

     (2015) Broadband onset inhibition can suppress spectral splatter in the auditory brainstem

     PLoS One 10:e0126500.

     https://doi.org/10.1371/journal.pone.0126500

     • Google Scholar
117. 1. Theunissen FE
     2. David SV
     3. Singh NC
     4. Hsu A
     5. Vinje WE
     6. Gallant JL

     (2001) Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli

     Network: Computation in Neural Systems 12:289–316.

     https://doi.org/10.1080/net.12.3.289.316

     • PubMed
     • Google Scholar
118. 1. Theunissen FE
     2. Sen K
     3. Doupe AJ

     (2000)

     Spectral-temporal receptive fields of nonlinear auditory neurons obtained using natural sounds

     Journal of Neuroscience 20:2315–2331.

     • PubMed
     • Google Scholar
119. 1. Tolnai S
     2. Englitz B
     3. Kopp-Scheinpflug C
     4. Dehmel S
     5. Jost J
     6. Rübsamen R

     (2008) Dynamic coupling of excitatory and inhibitory responses in the medial nucleus of the trapezoid body

     The European Journal of Neuroscience 27:3191–3204.

     https://doi.org/10.1111/j.1460-9568.2008.06292.x

     • PubMed
     • Google Scholar
120. 1. Typlt M
     2. Englitz B
     3. Sonntag M
     4. Dehmel S
     5. Kopp-Scheinpflug C
     6. Ruebsamen R

     (2012) Multidimensional characterization and differentiation of neurons in the anteroventral cochlear nucleus

     PloS One 7:e29965.

     https://doi.org/10.1371/journal.pone.0029965

     • PubMed
     • Google Scholar
121. 1. Typlt M
     2. Haustein MD
     3. Dietz B
     4. Steinert JR
     5. Witte M
     6. Englitz B
     7. Milenkovic I
     8. Kopp-Scheinpflug C
     9. Forsythe ID
     10. Rübsamen R

     (2010) Presynaptic and postsynaptic origin of multicomponent extracellular waveforms at the endbulb of Held-spherical bushy cell synapse

     The European Journal of Neuroscience 31:1574–1581.

     https://doi.org/10.1111/j.1460-9568.2010.07188.x

     • PubMed
     • Google Scholar
122. 1. van der Heijden M
     2. Louage DH
     3. Joris PX

     (2011) Responses of auditory nerve and anteroventral cochlear nucleus fibers to broadband and narrowband noise: implications for the sensitivity to interaural delays

     Journal of the Association for Research in Otolaryngology 12:485–502.

     https://doi.org/10.1007/s10162-011-0268-1

     • PubMed
     • Google Scholar
123. 1. Wang J
     2. McFadden SL
     3. Caspary D
     4. Salvi R

     (2002) Gamma-aminobutyric acid circuits shape response properties of auditory cortex neurons

     Brain Research 944:219–231.

     https://doi.org/10.1016/S0006-8993(02)02926-8

     • PubMed
     • Google Scholar
124. 1. Wang Y
     2. Manis PB

     (2008) Short-term synaptic depression and recovery at the mature mammalian endbulb of Held synapse in mice

     Journal of Neurophysiology 100:1255–1264.

     https://doi.org/10.1152/jn.90715.2008

     • PubMed
     • Google Scholar
125. 1. Wang Y
     2. Ren C
     3. Manis PB

     (2010) Endbulb synaptic depression within the range of presynaptic spontaneous firing and its impact on the firing reliability of cochlear nucleus bushy neurons

     Hearing Research 270:101–109.

     https://doi.org/10.1016/j.heares.2010.09.003

     • PubMed
     • Google Scholar
126. 1. Wehr M
     2. Zador AM

     (2003) Balanced inhibition underlies tuning and sharpens spike timing in auditory cortex

     Nature 426:442–446.

     https://doi.org/10.1038/nature02116

     • PubMed
     • Google Scholar
127. 1. Wickesberg RE
     2. Oertel D

     (1990)

     Delayed, frequency-specific inhibition in the cochlear nuclei of mice: a mechanism for monaural echo suppression

      Journal of Neuroscience 10:1762–1768.

     • PubMed
     • Google Scholar
128. 1. Wilent WB
     2. Contreras D

     (2004) Synaptic responses to whisker deflections in rat barrel cortex as a function of cortical layer and stimulus intensity

     Journal of Neuroscience 24:3985–3998.

     https://doi.org/10.1523/JNEUROSCI.5782-03.2004

     • PubMed
     • Google Scholar
129. 1. Willmore B
     2. Tolhurst DJ

     (2001) Characterizing the sparseness of neural codes

     Network 12:255–270.

     https://doi.org/10.1080/net.12.3.255.270

     • PubMed
     • Google Scholar
130. 1. Winter IM
     2. Palmer AR

     (1990) Responses of single units in the anteroventral cochlear nucleus of the guinea pig

     Hearing Research 44:161–178.

     https://doi.org/10.1016/0378-5955(90)90078-4

     • PubMed
     • Google Scholar
131. 1. Wu GK
     2. Arbuckle R
     3. Liu BH
     4. Tao HW
     5. Zhang LI

     (2008) Lateral sharpening of cortical frequency tuning by approximately balanced inhibition

     Neuron 58:132–143.

     https://doi.org/10.1016/j.neuron.2008.01.035

     • PubMed
     • Google Scholar
132. 1. Wu SH
     2. Oertel D

     (1986)

     Inhibitory circuitry in the ventral cochlear nucleus is probably mediated by glycine

     Journal of Neuroscience 6:2691–2706.

     • PubMed
     • Google Scholar
133. 1. Xie R
     2. Manis PB

     (2013) Target-specific IPSC kinetics promote temporal processing in auditory parallel pathways

     Journal of Neuroscience 33:1598–1614.

     https://doi.org/10.1523/JNEUROSCI.2541-12.2013

     • PubMed
     • Google Scholar
134. 1. Yang H
     2. Xu-Friedman MA

     (2008) Relative roles of different mechanisms of depression at the mouse endbulb of Held

     Journal of Neurophysiology 99:2510–2521.

     https://doi.org/10.1152/jn.01293.2007

     • PubMed
     • Google Scholar
135. 1. Yang H
     2. Xu-Friedman MA

     (2009) Impact of synaptic depression on spike timing at the endbulb of Held

     Journal of Neurophysiology 102:1699–1710.

     https://doi.org/10.1152/jn.00072.2009

     • PubMed
     • Google Scholar
136. 1. Yang H
     2. Xu-Friedman MA

     (2015) Skipped-stimulus approach reveals that short-term plasticity dominates synaptic strength during ongoing activity

     Journal of Neuroscience  35:8297–8307.

     https://doi.org/10.1523/JNEUROSCI.4299-14.2015

     • PubMed
     • Google Scholar
137. 1. Yang L
     2. Pollak GD

     (1994)

     The roles of GABAergic and glycinergic inhibition on binaural processing in the dorsal nucleus of the lateral lemniscus of the mustache bat

     Journal of Neurophysiology 71:1999–2013.

     • PubMed
     • Google Scholar
138. 1. Yang L
     2. Pollak GD

     (1998) Features of ipsilaterally evoked inhibition in the dorsal nucleus of the lateral lemniscus

     Hearing Research 122:125–141.

     https://doi.org/10.1016/S0378-5955(98)00088-4

     • PubMed
     • Google Scholar
139. 1. Yin TC
     2. Chan JC

     (1990)

     Interaural time sensitivity in medial superior olive of cat

     Journal of Neurophysiology 64:465–488.

     • PubMed
     • Google Scholar
140. 1. Yost WA

     (1996) Pitch strength of iterated rippled noise

     The Journal of the Acoustical Society of America 100:3329.

     https://doi.org/10.1121/1.416973

     • PubMed
     • Google Scholar
141. 1. Zhang X
     2. Heinz MG
     3. Bruce IC
     4. Carney LH

     (2001) A phenomenological model for the responses of auditory-nerve fibers: I. Nonlinear tuning with compression and suppression

     The Journal of the Acoustical Society of America 109:648–718.

     https://doi.org/10.1121/1.1336503

     • PubMed
     • Google Scholar

Author details

1. Christian Keine

   Faculty of Bioscience, Pharmacy and Psychology, University of Leipzig, Leipzig, Germany

   Present address

   Molecular Mechanisms of Synaptic Function, Max Planck Florida Institute for Neuroscience, Jupiter, United States

   Contribution

   CK, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   christian.keine@gmail.com

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-8953-2593

2. Rudolf Rübsamen

   Faculty of Bioscience, Pharmacy and Psychology, University of Leipzig, Leipzig, Germany

   Contribution

   RR, Conception and design, Drafting or revising the article

   Contributed equally with

   Bernhard Englitz

   Competing interests

   The authors declare that no competing interests exist.

3. Bernhard Englitz

   Department of Neurophysiology, Donders Center for Neuroscience, Radboud University, Nijmegen, Netherlands

   Contribution

   BE, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Rudolf Rübsamen

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0001-9106-0356

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