Inhibition plays a crucial role in neural signal processing, shaping and limiting responses. In the auditory system, inhibition already modulates second order neurons in the cochlear nucleus, e.g. spherical bushy cells (SBCs). While the physiological basis of inhibition and excitation is well described, their functional interaction in signal processing remains elusive. Using a combination of in vivo loose-patch recordings, iontophoretic drug application, and detailed signal analysis in the Mongolian Gerbil, we demonstrate that inhibition is widely co-tuned with excitation, and leads only to minor sharpening of the spectral response properties. Combinations of complex stimuli and neuronal input-output analysis based on spectrotemporal receptive fields revealed inhibition to render the neuronal output temporally sparser and more reproducible than the input. Overall, inhibition plays a central role in improving the temporal response fidelity of SBCs across a wide range of input intensities and thereby provides the basis for high-fidelity signal processing.DOI:http://dx.doi.org/10.7554/eLife.19295.001 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.DOI:http://dx.doi.org/10.7554/eLife.19295.002 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 , 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 . These SDue 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 , 1994b, 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 . This enIn 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 , 2015: TThe interaction between acoustically evoked excitation and inhibition is a key constituent at the initial stages of signal processing in the auditory brainstem . This stsucc and EPSPs that fail to trigger an AP, i.e. EPSPfail. 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  EPSPfail, i.e. EPSPs that fail to trigger APs. The maximum falling slope was consistently higher in EPSPsucc than in EPSPfail  and the excitatory postsynaptic potential (EPSP) which may or may not be followed by an AP . The fasfail and EPSPsucc . 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 EPSPfail of the ANF input (EPSPsucc + EPSPfail) amounted to 0.28  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  rendering the ANF-SBC synapse less reliable during acoustic stimulation  and EPSPsucc (blue) as shown in a representative cell in fail = 0.22 ± 0.16 vs. EPSPsucc = 0.24 ± 0.11, Δ = 0.02 ± 0.12, p=0.27, paired t-test, n = 62, U1 = 0.09, 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 . The anafail). When analyzing the dependence of AP amplitude on preceding SBC spiking activity  of SBCs show prominent inhibitory sidebands and reduced firing activity in the excitatory field compared to the ANF input . Also, as = 0.7 , 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) , 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  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 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 . The lowt-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  nor on failure fraction .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 . Slice st-test, n = 11, U1 = 0.18, t-test, n = 11, U1 = 0.5, 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  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 . The analysis revealed higher CorrNorm for the SBC output compared to the ANF input , which for both signal types increased with modulation frequency . 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.The analysis revealed higher reproducibility in the SBC output compared to the ANF input  and also showed a systematic decrease with increasing modulation frequency . 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 CorrIn 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  and incrsucc and EPSPfail.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 (fail shows a considerable increase in the responses to the second of the first two peaks).The neural response to gamma-tones of both the ANF input and SBC output were temporally structured  separately for the positive (red) and negative (blue) regions in the STRF . Summing all the positive regions revealed a significant reduction from ANFs to SBCs .The endbulb synapse depresses considerably during high-frequency firing  despite SBCs receive inhibitory inputs both on their somata and dendrites . Both glFor a broad range of acoustic stimuli, we observed a consistently sparser and more reproducible response in the SBC output compared to the ANF input. Can a simple inhibition achieve these changes in signal representation? Glycinergic inhibition has previously been demonstrated to be neither purely subtractive nor purely divisive  and act In SBCs, this would translate to multiple, closely timed spikes for an individual input or across multiple excitatory inputs. The partially subtractive glycinergic inhibition would be strongly triggered by large instantaneous firing rates, and weaken subsequent inputs which do not occur closely to other inputs. On the PSTH level, a subtractive reduction thus almost inevitably increases sparsity see . ReproduDue to the inhibitory/excitatory co-modulation with level, the enhancement in sparsity and reproducibility can extend over a wide range of levels. These considerations do not rule out additional enhancements of temporal precision, via additional excitatory inputs, however, these would be influenced similarly by the inhibition. The considerations above are challenging to study since precise temporal control over multiple inputs would be required.spikes selected for transmission.Generally, if the information in the response is largely maintained, an increase in temporal sparsity can be advantageous for mainly three reasons. First, the information per spike is increased . This is– when just considering signal processing – can be regarded as the correlation between the activity of independent neurons from both sides  in the cat reported increased reproducibility in neurons of the AVCN . Here, wPresently, we show that this increase is directly achieved at individual ANF-SBC junctions through the postsynaptic interaction of acoustically evoked excitation and inhibition. Also, during both narrowband and broadband stimulation, the block of inhibition resulted in a considerable decrease in reproducibility, sparsity, and temporal precision compared to the control condition . Hence, The output of SBCs is also relevant for general processing of sounds, i.e. their representation for later analysis, e.g. auditory recognition, a process distinct from sound localization . The incNatural stimuli tend to depart from the classical laboratory stimuli, in being spectrotemporally broad and diverse. We approximated this condition using the RGS stimulus in combination with distinct STRF estimation of both the pre- and the postsynaptic activity. STRFs were first introduced to study midbrain neurons in the grass frog , 1981, aThe RGS stimulus allows a robust estimation of STRFs while keeping the spectrum sparse , the overall, average sound level was kept constant. We predict that SBC STRFs will exhibit a greater robustness to changes in level compared to ANF STRFs, based on the gain-modulating inhibitory input .Several nuclei have been suggested to provide the inhibitory inputs to SBCs. The present data suggest the inhibitory source to feature a broad, symmetrically shaped tuning, consistent with an integration over a wide set of primary tuned inhibitory cells. The integration would have to be weighted by the distance to the postsynaptic CF, in order to achieve the symmetry in inhibitory modulation. In a recent study, In summary, while acoustically evoked inhibition on SBC renders the ANF-SBC junction less reliable with respect to signal transmission, it enables sparser and more reproducible SBC sound encoding, which might be of relevance for subsequent localization of sound sources.Meriones unguiculatus) of either sex aged 4 to 12 weeks (P43 ± 13).All experiments were performed at the Neurobiology Laboratories of the Faculty of Bioscience, Pharmacy and Psychology of the University of Leipzig (Germany), approved by the Saxonian District Government, Leipzig (TVV 06/09), and conducted according to the European Communities Council Directive (86/609/EEC). Animals were housed in the animal facility of the Institute of Biology with a 12 hr light/dark cycle and with access to food and water ad libitum. Data were collected from 42 Mongolian gerbils  to corroborate the stereotaxic coordinates of the rostral, low-frequency pole of the AVCN.Before the experiment, animals were anesthetized by an intraperitoneal injection of a mixture of ketamine hydrochloride  and xylazine hydrochloride . The surgical procedure was performed as described previously . For mulThe activity of SBCs was then acquired by loose-patch recordings . For thaTo study the impact of inhibition on signal processing at the ANF-SBC synapse, loose-patch recordings were combined with iontophoretic drug application of the glycine receptor agonist glycine  and the glycine receptor antagonist strychnine . Three-barreled piggy-back electrodes  were gluAll recordings were performed in a sound-attenuating and electrically isolated chamber  on a vibration-cushioned table. Acoustic stimuli were generated by custom-written Matlab software  and digitized at a rate of 97.7 kHz. Signals were presented via a custom-made earphone  and delivered through a metal funnel ending just in front of the ear canal. The loudspeakers were calibrated using a condenser microphone (Bruel and Kjaer 4133) and custom-written Matlab software. Total harmonic distortions (ratio of the root-mean-squared (RMS) amplitude of higher harmonic frequencies to the RMS amplitude of the fundamental) were below 0.02% across all frequencies tested (0.1–40 kHz). All acoustic stimuli were corrected for the loudspeaker’s impulse response prior to presentation.2 ramps, 300 ms interstimulus interval) derived from a predefined matrix consisting of 20 different frequencies equally spaced on a log scale and 10 different sound pressure levels (SPL) equally spaced on a linear scale. Each of these 200 frequency/intensity pairs was presented 5–10 times while continuously recording the unit’s discharge activity. The FRAs were used to detail each unit’s CF, (the frequency at which the neuron is most sensitive), response thresholds, and – if present – the frequency-intensity domain of an inhibitory sideband.Frequency response areas (FRA) were obtained by pseudorandom presentation of pure tones .Tones were frequency-modulated in the range of one octave below to two octaves above the unit’s CF at modulation frequencies 20 Hz, 50 Hz, 100 Hz, 200 Hz, and 400 Hz .Spectrotemporal receptive fields, sparsity, and reproducibility were estimated in response to a spectrotemporally broad and varied stimulus, a variant of the dynamic random chord stimuli (DRC) as described in The rostral pole of the AVCN was targeted considering its tonotopic organization described previously . SphericThe neurons’ voltage signals were pre-amplified and impedance-converted (Neuroprobe 1600), A-M Systems, Sequim, USA), noise-eliminated , further amplified , and digitized at a sampling rate of 97.7 kHz . Signals were band-pass filtered between 5 Hz and 7.5 kHz using a zero-phase forward and reverse digital IIR filter and stored for offline analysis using custom-written Matlab software.fail while EPSPs that successfully trigger an AP were termed EPSPsucc. The separation between EPSPsucc and EPSPfail was based on the maximum falling slope following the detection time point. The APs following EPSPsucc exhibited a considerably faster-falling flank than EPSPfail enabling a clear separation of both signal types , and the fraction of EPSPsucc calculated for each bin. Then, a Boltzmann function of the form d indicates the threshold EPSP, i.e. the EPSP slope necessary to yield a > 50% probability of triggering a postsynaptic AP . Earlier studies showed the influence of preceding neural activity on EPSC/EPSP and AP amplitude  or three components  were calculated for both. AI was defined as Norm) as the normalized cross-correlation between the stimulus modulation and the respective response PSTH, adjusted for the latency of the neural response  are only obtained when the response shape follows the stimulus envelope. The reproducibility of the neuronal responses was estimated by measuring the central peak of the shuffled autocorrelation across identical stimulus presentations  the first 20 ms of every repetition were discarded from analysis to reduce the influence of onset effects, and analysis was constrained to complete periods to avoid unequal sampling. The temporal precision of spikes throughout the stimulus period was assessed by calculating the vector strength . The sigonse see  for a dentations . The modfail + EPSPsucc), as well as the SBC output (EPSPsucc). Both input and output were aligned to their maximum EPSP slope, to enable a subtraction of the two STRFs using congruent reference points. STRFs were individually normalized to their standard deviation , in order to allow an evaluation of tuning shape, removing the gain from overall firing rate. Without normalization, the result remains qualitatively the same. However, the inhibitory effect is then dominated by the difference at the STRF peak, which stems from the overall firing rate difference between ANF and SBC. The resulting difference-STRF indicates the translation in spectrotemporal sensitivity from ANF input to the SBC output  represent the neural tuning in the dimensions of the spectrogram (time and frequency) and help to identify stimulus properties that control spiking at high temporal resolution. Specifically, we used STRFs to study the time course of inhibition during ongoing, spectrally dispersed stimulation. Estimation of STRFs was performed using generalized reverse correlation, as described elsewhere . STRFs wtput see . We inteWe evaluated multiple measures of synaptic responsiveness to quantify the input-to-output signal processing at these second order neurons of the ascending central auditory pathway. All measures were computed separately for the ANF input and SBC output and then compared.The temporal sparsity of the neural response was calculated with three different methods, two classical and a third simple and intuitive one. First, we calculated the variance-based method introduced by where Third, we calculated a simple and intuitive measure, which we term ‘Close-to-Silence-Index’ (CSI). The CSI is defined as the fraction of PSTH bins less than a certain firing rate Across multiple repetitions of a stimulus, the neural response can repeat reliably or exhibit a high trial-to-trial variability. In the present experiment, variability on the stimulation side is marginal. Thus, the observed variability is solely due to the neural processing. We calculated a measure of reliability by computing the cross-correlation across different trials  in response to the same stimulus, similar to the correlation index . The heiLastly, the temporal precision of SBC AP generation was quantified by the half-maximum width of the central peak of the cross-correlation function across identical stimulus presentations. The width is termed dispersion, measured in milliseconds  ANOVA after testing for sphericity using the Mauchly test  and no further experiments were deemed necessary. While some of the results in the early part of the paper have been reported by others (including their own group), the latter part of the paper, looking at the coding of more natural stimuli i.e. SAM, SFM and RGS, contains new and interesting data.There are quite a few comments, but the reviewers feel these can all be satisfactorily addressed by the authors. In particular, the reviewers would appreciate a clearer explanation of the STRF stimulus and its analysis e.g. concepts such as sparsity and reproduction need to be made more accessible. In addition, the authors should address the issues of data analysis and presentation outlined in the comments below.Essential comments1) It is important to set out more clearly what this study is addressing. To say simply that "the functional role [of inhibition] is not entirely understood" (Introduction section) is inadequate. In the Abstract and Introduction you suggest that one possibility is to compensate for stimulus level. The passage at the beginning of subsection “Inhibition at the endbulb in the context of sound localization” seems important to this argument, but it is difficult to understand how sparsity enhances coincidence detection in MSO. If two inputs simply fire less, then there are fewer opportunities for coincidence, so it seems coincidence detection could get worse if the spikes are deleted at random. The authors posit that reproducibility also occurs across cells, not just within. Do the present data support that? How does level tolerance arise out of this?2) The concepts of sparsity and reproduction need to be more clearly explained. This is especially true in subsection “Glycinergic inhibition renders SBC responses sparser, more reliable and temporally more precise”, but even in the Abstract, these terms should be clearly defined.a) Why is kurtosis a measure of sparsity? Kurtosis seems to capture both tails of a distribution, but the term "sparsity" implies decreased firing generally, which would include a shift in the mean firing rate, or a skew towards longer intervals. Kurtosis, as the 4th moment, seems to be chosen to avoid both those features. Why is that? It should also be clearly explained why sparsity might be important. The argument from saving energy is not very compelling, if there is not also a decrease in firing rate.b) The term reproduction is not clear. Its definition is hidden in the Methods and while the values are compared in 3) Do all primary-like units show failures? Did you just select cells that showed failures? Please comment on cell type. How is it determined that these are spherical bushy cells? Are recordings targeted towards cells with particularly large synaptic inputs? How many synaptic inputs does each cell receive? Are the electrical signals assignable to activity of one input? Does the analysis of 4) Spike depression. Lorteije et al., 2009 suggested that "spike depression" (presumably the refractory period) contributed to EPSP failures at the calyx of Held. The refractory period would likely appear as an increase in threshold during periods of high activity, similar to 5) Second paragraph subsection “Synaptic depression alone fails to fully account for increased failure rates”: Mathematically, what you have done here is correct, but it will likely be confusing to many readers. It is unclear why an "error function" (Boltzmann?) is used to fit this data, as the points clearly cluster into two clouds, and the "threshold" point is not near any of the data points. Why were the data not binned along the ordinate of rising slope (in 0.5 or 1 V/s bins), and the probability of success or failure computed in each bin, based on the total number of succ+fail. This would give the type of representation that would then make sense in terms of fitting to a Boltzmann to obtain a "threshold". Maybe do this as an inset?6) The summary plots can be very difficult to read. Each plot shows median, quartiles and outliers, as well as averages. This gets very busy, and winds up obscuring or minimizing the important differences. For example, in Figure 3Ciii the averages are squashed down in the lower part of the panel, and most of the panel is taken up with outliers. It would be better to maximize the plot area to make the data as visible as possible. Averages and standard errors are sufficient when testing with a t-test, as are box-and-whisker plots when a non-parametric test. This applies to summary plots throughout, with 7) Many plots compare paired values, and report the original values (+/- SE), but not their differences. For example, 3Ci reports average CFs of ANF and SBC. The average values of these are not very meaningful. What is important is the difference between CFs for input vs. output, which is not reported. This applies in many other places: in 8) ANOVAs in 9) Flatness of the RLF . Using s10) Effects of inhibition. The analysis of the effects of inhibition is very thorough, and beautifully shown, but does not adequately acknowledge similar results from earlier work. This is particularly noticeable in the final paragraph of subsection “Acoustically evoked inhibition elevates threshold for AP generation”, which does not acknowledge that Kopp-Scheinpflug, 2002 and Kuenzel, 2011 also showed similar tuning of inhibition. 11) FM data period histograms. The FM stimuli appear to yield 2 or 3 peaks in the representative example of 12) STRF analysis: This analysis is very nice, but some clarification would be helpful.a) Why is the inhibitory STRF calculated by subtracting the ANF and SBC plots? Please explain how and why firing rates are normalized by the standard deviation. Is it normalized by subtraction or division? What if this normalization is not done? Please add a scale bar to these plots. Can this analysis be done instead by using only the EPSP(fail) for the kernel estimation? How would this approach differ from the subtraction approach?b) The size of the excitatory region is described as sharper in the SBC compared to ANF. This is not obvious in the example. Can the spectral width be indicated on the plot in 13)  […] Essential comments1) It is important to set out more clearly what this study is addressing. To say simply that "the functional role [of inhibition] is not entirely understood" (Introduction section) is inadequate. In the Abstract and Introduction you suggest that one possibility is to compensate for stimulus level. The passage at the beginning of subsection “Inhibition at the endbulb in the context of sound localization” seems important to this argument, but it is difficult to understand how sparsity enhances coincidence detection in MSO. If two inputs simply fire less, then there are fewer opportunities for coincidence, so it seems coincidence detection could get worse if the spikes are deleted at random. The authors posit that reproducibility also occurs across cells, not just within. Do the present data support that? How does level tolerance arise out of this?We reckon that the aims and insights of the study could have been more clearly described early on. The review process has motivated us to investigate the mechanisms deeper than before (see below), which leads to a clearer and more insightful bottom-line:" These improvements are a consequence of the combined subtractive/divisive action of glycine : The subtractive component enhances the temporal sparsity by raising the threshold for spiking. Therefore, mostly high instantaneous firing-rate events pass the junction, which often correspond to salient events in the cell's frequency range. 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 wider 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."We have added this passage in the Introduction section, in addition to a number of other clarifications.Regarding the other questions raised in this point:relatively loudest peaks are kept, which is what we refer to as gain control.The reviewers correctly point out that the step from improved sparsity and reproducibility to improved coincidence detection is not a trivial one, and should be addressed more directly. As the reviewers indicate, a random deletion of spikes would not lead to improved coding. Since a random deletion would correspond to a divisive scaling of the PSTH, it would also not lead to an increase in sparsity . The effect of inhibition in this case is at least partially subtractive , which means that the spikes/parts of the PSTH that remain are those occurring during phases of high firing rate or high firing probabilities. These are the phases, when the stimulus drives a lot of spikes in the current frequency band/neural tuning (directly shown e.g. by the STRF tuning). For a given excitatory frequency these phases will correspond to high stimulus level (given the monotonic rate-level functions of ANFs), which are likely to be present in both the left and the right ear. We propose, that in this way, a given frequency channel transmits the most salient events in its frequency channel, which will also have the best timing . Such an increase in temporal precision was also demonstrated before,  and also our Next, the outputs from both ears are then combined in the MSO (and LSO) for sound localization: In order to precisely estimate sound source locations, MSO neurons have to detect “correct” coincidences, i.e. coincident spikes from both ears while neglecting coincident spikes from one ear or coincidences that are purely random. MSO neurons possess a number of anatomical  and biophysical specializations  to distinguish between monaural and binaural coincidences. The increase of ANF firing rates with sound pressure level would – at high sound intensities – likely result in a large number of coinciding inputs at the MSO level and create MSO activity at unfavorable ITDs, i.e. coincident spikes just by chance alone. The above described focus on the relatively highest-level events may allow the MSO to focus on binaural spikes with high temporal fidelity, and thus avoid inaccurate contributions to the ITD estimation. The increase of inhibition with level maintains a more limited range of firing rates across level. Given the presumably strong biophysical basis of coincidence detection in the MSO, it seems plausible that precise ITD processing can only function over a limited range of input firing frequencies. However, as intuitive as this reasoning may seem, it remains partly speculative at this point. We have added considerations along the lines above in the Discussion, adding to Discussion sections that have been added in response to other points raised by the reviewers Next, the reviewers inquired whether reproducibility increases across cells as well. Before answering this point, we would like to make our claim more precise: If reproducibility is measured with cross-correlation at time-shift 0, an increase in reproducibility in a single cell, does not necessarily lead to an increase in reproducibility across multiple cells. Since different cells will have different tuning properties, their cross-correlation may well reduce (at time-shift 0), even if both cells individually increase their cross-correlation at time-shift 0. If cells with matched tuning properties are compared, however, an increase in reproducibility in each cell, should translate to an increase across cells. We have tested this directly for a subset of cells, identified by clearly peaked ANF cross-correlations . We seleOn the other hand, if reproducibility were measured with a more general measure, e.g. the entropy of the response given a stimulus (i.e. H(R|S)), or – more typically – mutual information  = H(R)-H(R|S)), then we would predict that an increase in reproducibility on single cells would always lead to an increase in reproducibility on the population level. While the current number of repetitions would make entropy estimation inaccurate , the general argument for an overall increase is as follows: considering the extreme case, where every cell produces an individual, but unique spike-train for a given stimulus. Then the neural population would also emit a unique population spike-train, thus be highly reproducible. Similarly, if a single cell becomes more reproducible, this reduces the set of response emitted by the entire population. We have added the following abbreviated version of this argument to the Discussion:“Here, we only directly studied the increase across trials within a cell, but not across cells. We conjecture that reproducibility across cells always increases, if both cells increase their reproducibility individually. However, this can only be assessed with the peak of the cross-correlation, if the cells are matched in their tuning, a condition which is typically assumed for bilateral inputs into the MSO. For non-matched cells, reproducibility should increase, but would have to be assessed with a different measure, that measures the set of responses to a given stimulus .”2) The concepts of sparsity and reproduction need to be more clearly explained. This is especially true in subsection “Glycinergic inhibition renders SBC responses sparser, more reliable and temporally more precise”, but even in the Abstract, these terms should be clearly defined.a) Why is kurtosis a measure of sparsity? Kurtosis seems to capture both tails of a distribution, but the term "sparsity" implies decreased firing generally, which would include a shift in the mean firing rate, or a skew towards longer intervals. Kurtosis, as the 4th moment, seems to be chosen to avoid both those features. Why is that? It should also be clearly explained why sparsity might be important. The argument from saving energy is not very compelling, if there is not also a decrease in firing rate.We added additional information on sparsity and reproducibility to the Introduction and Results section at their first appearance. The word limit in the Abstract prevented us from explaining the concepts in more detail there.We used the kurtosis of the firing rate distribution to measure the sparsity of the neural response, since it had been classically suggested as a measure of sparseness  with higher values of kurtosis related to higher sparseness  and later taken up by Willmore and Tolhurst (2001), as well as several subsequent studies in neuroscience .This measure is defined as: On the other hand, S is close 0, if the average rate is high, and the variations in rate comparatively low.Note, that from the definition of the As an alternative 'safe & clean' measure, we introduce another measure of sparseness, here, termed 'Close-to-Silence-Index' (CSI). CSI is defined as the fraction of PSTH bins, which are below a certain threshold  and the Discussion (at greater length), but not in the Results, since this remains an interpretation. Concretely, we focus on the information per spike and the energy efficiency: One main advantage of sparsity can be the increased information per spike, e.g. to temporal information, or also the detection of changes in firing rate/stimulus properties, if sparsity is achieved through gain normalization. Since the firing rate does indeed decrease in all cells , we also keep the argument of more energy-efficient coding, but provide a more detailed argument now.b) The term reproduction is not clear. Its definition is hidden in the Methods and while the values are compared in We agree that “reproducibility” is more in line with our definition  than “reproduction”, hence, we replaced it throughout the revised manuscript. To introduce the term an explanation has been added to the Results section and a subplot has been added to its first mention in Further, we also agree that increased reproducibility of the neural response does not necessarily imply a more efficient or useful coding of acoustic information . However, reproducibility across trials, which we measure here, provides a basis for the consistency in extracting information  to repeated presentations of the same stimulus. This is the minimal requirement one should have for a consistent processing of stimuli.According to Joris, 2003, comparing each pair of spike trains from recordings of a single cell can be considered as providing the input to a simple binaural coincidence detector. While we think the quantification on the basis of the cross-correlation is generally sensible, it is particularly fitting in the context of subsequent computations for extracting interaural time-delays in subsequent processing stations, which likely relies on operations similar to cross-correlation . For example, MSO neurons are thought to perform an approximate cross-correlation between their bilateral inputs, hence, an increase in cross-correlation translates into a more precise firing of MSO neurons, i.e. more precise in interaural time.The last point raised by the reviewers, whether a more reproducible response is more correct, is hard to answer, without relating it to the subsequent computation. For example, if only interaural-time difference were computed, then the combination of increased reproducibility and temporal precision can be taken as a signature of a 'more correct' response for this purpose. However, this could also render the response less correct for other purposes. We have added these important considerations to the Discussion. They are embedded into the section dealing with functional origin of the increase in sparsity and reproduction (see below):"These properties may be sufficient for increasing sparsity and reproducibility under the assumption that large deviations of firing rate are the consequence of stimulus-elicited, high firing probabilities rather than noise see . The lat3) Do all primary-like units show failures? Did you just select cells that showed failures? Please comment on cell type. How is it determined that these are spherical bushy cells? Are recordings targeted towards cells with particularly large synaptic inputs? How many synaptic inputs does each cell receive? Are the electrical signals assignable to activity of one input? Does the analysis of From our recordings at the rostral pole of the AVCN, all cells showed failures but to very different degrees , and the majority of rostral SBCs seem to receive a single endbulb in cats , whereas GBCs are reported to receive between 10-70 endbulb inputs . Our dataset and previous studies recording from low-CF SBCs in the Mongolian Gerbil suggest that the majority of cells receive a single functional endbulb input . This conclusion is supported by the following findings:(i) Multiple endbulb inputs to SBCs would be indicated by a larger variation of EPSP slopes reflecting differences in the convergence of different inputs which – during in vivo recording of spontaneous activity – will not be completely synchronized. The consequently expected variations in the shapes (and amplitudes) of EPSPs were not observed in our recordings.(ii) Multiple endbulb inputs onto SBCs would be indicated by violation of the refractory period and temporal overlap of two or more EPSPs. Our data on EPSP slopes show a unimodal distribution and we did not observe an overlap of multiple EPSPs in the recordings.(iii) Multiple endbulb inputs would likely be reflected by ANF input rates above the rates of single auditory nerve fibers, since converging ANF inputs on bushy cells originate from the same SR type .To further deal with this issue we compared the distribution of ANF input rates of our recordings with published data from recordings of single AN fibers from the Mongolian Gerbil . If our recordings would contain EPSPs from multiple independent endbulbs, we would not see such facilitation, since only a fraction of EPSPs should be affected by previous activity. Also, the observation of facilitation rather than depression is unlikely to occur with multiple endbulb inputs.4) Spike depression. Lorteije et al., 2009 suggested that "spike depression" (presumably the refractory period) contributed to EPSP failures at the calyx of Held. The refractory period would likely appear as an increase in threshold during periods of high activity, similar to We agree that spike depression and the influence of refractory period could potentially contribute to failures in AP generation in SBCs. This effect was also addressed in a previous study by Kuenzel and colleagues (2011) who showed that shortly after an SBC spike the failure fraction is increased, similarly to the results reported in the study at hand. Both Lorteije et al., 2009 and Kuenzel et al., 2011 reported short recovery times of 0.8 ms and 2.1 ms, respectively, indicating that the influence of spike depression on signal transmission is limited to periods shortly after the last SBC spike.We attempt to address the influence of spike depression on AP generation in vivo by recording spontaneous activity in the absence of sound stimulation, when the inhibitory influence is likely small  Second paragraph subsection “Synaptic depression alone fails to fully account for increased failure rates”: Mathematically, what you have done here is correct, but it will likely be confusing to many readers. It is unclear why an "error function" (Boltzmann?) is used to fit this data, as the points clearly cluster into two clouds, and the "threshold" point is not near any of the data points. Why were the data not binned along the ordinate of rising slope (in 0.5 or 1 V/s bins), and the probability of success or failure computed in each bin, based on the total number of succ+fail. This would give the type of representation that would then make sense in terms of fitting to a Boltzmann to obtain a "threshold". Maybe do this as an inset?We agree with the reviewer that it is intuitively not comprehensible that the current approach is a valid method for the estimation of threshold EPSP. The criticism of the reviewer is well taken and we not only would like to justify the approach in this letter but also explain the approach in in more detail in the text body of the manuscript. Choosing a Gaussian error function was based on a previous study . Fitting different kinds of sigmoid functions  resulted in different slope parameter, but identical inflection points, thus providing the same estimates for the threshold EPSP. In the original submission, we refrained from binning the rising slopes to avoid potential effects of bin size on threshold EPSP estimation. Also, in the method applied in the original manuscript, the fit is inherently weighted by the number of events, which is not the case when using binned data, as all bins will be equally weighted independent of the number of events in each bin, i.e. a probability calculated from e.g. 3 events contributes equally to the fit as does a probability calculated from 1000 events. This is why we thought the use of raw, unbinned data would provide more reliable data. However, we agree that the approach suggested by the reviewers is a more intuitive method of threshold calculation. We therefore recalculated the threshold EPSPs throughout the manuscript using the suggested method (bin size=0.5 V/s). We added a linear weighting parameter to each bin based on the number of events in each bin. The results are virtually identical to the previous approach  The summary plots can be very difficult to read. Each plot shows median, quartiles and outliers, as well as averages. This gets very busy, and winds up obscuring or minimizing the important differences. For example, in Figure 3Ciii the averages are squashed down in the lower part of the panel, and most of the panel is taken up with outliers. It would be better to maximize the plot area to make the data as visible as possible. Averages and standard errors are sufficient when testing with a t-test, as are box-and-whisker plots when a non-parametric test. This applies to summary plots throughout, with The reviewer is correctly pointing out, that the way the data were presented in the graphs made it difficult to single out the important effects. We choose this approach to show all observed values and scaled the axes to cover all theoretically possible values (as in 7) Many plots compare paired values, and report the original values (+/- SE), but not their differences. For example, 3Ci reports average CFs of ANF and SBC. The average values of these are not very meaningful. What is important is the difference between CFs for input vs. output, which is not reported. This applies in many other places: in We thank the reviewer for pointing out that relevant information was missing the way the data were presented. Our motivation had been to provide a transparent presentation of the distribution of the raw data. We now added mean ± standard deviation for differences in the paired comparison to provide this previously missing information to the reader.8) ANOVAs in We apologize for the misleading term “systematic variation” when describing the results of the VS across stimulation frequencies. Our focus was on the comparison between ANF input and SBC output rather than changes across modulation frequencies. Regarding the VS values, for both ANF input and SBC output the VS decreased with increasing stimulation frequency (VS 50 Hz: ANF=0.26 ± 0.09 vs. SBC=0.31 ± 0.07: 400 Hz: ANF=0.2 ± 0.1 vs. SBC=0.25 ± 0.1). While relatively stable VS values are obtained up to 200 Hz modulation frequency, the VS was significantly reduced at 400 Hz for both ANF input and SBC output. Importantly, the VS of the SBC output was larger than the ANF input across all frequencies tested.The reviewer correctly observed the effect of the modulation frequencies, and here is, how we would like to explain this finding: The non-linearity of the frequency effect could be explained by the time-constant of the impact of inhibition. The FM stimulus spanned 3 octaves around the unit’s CF, thus covering frequency areas with strong inhibition and areas lacking any inhibitory effect. The time constant of the inhibitory effect is around 20-50 ms in slice recordings , but somewhat shorter in vivo . During frequency modulated stimulation, the inhibitory conductance should therefore also be modulated and essentially act as a band-pass filter. The slowest and highest modulation frequencies might result in saturation of inhibition or insufficient activation (if the inhibitory conductance does not summate), thus reducing the inhibitory effect on the ANF-to-SBC synapse. However, the exact inhibitory dynamics in vivo are not well understood, also the combination of cell types exerting this inhibition on SBC is unclear. Hence, the temporal dynamics of inhibitory inputs during acoustic stimulation remain elusive. At this point, we can only speculate, why the effect is smaller at the highest and lowest frequencies tested, but it is tempting to argue, that the inhibitory dynamics capture the modulation rates of natural acoustic stimuli. Further studies employing natural sounds would be necessary to evaluate the inhibitory effect on different time scales.9) Flatness of the RLF . Using sThe reviewer is right in pointing out, that the measure of rate-level variability (RLV) as the standard deviation of the rate-level function (RLF) will depend on the intensities that actually were sampled. We calculated the standard deviation across all of the sampled intensities, identically for the ANF input and the SBC output. In most cells, the RLF of the ANF input shows monotonic increase over a relative wide range of firing rates, while the RLFs of the output were more flat or even non-monotonic. A measure of monotonicity was previously suggested by Kuenzel and colleauges, (2015), which relates the maximum firing rate throughout the RLF to the firing rate obtained at highest sound intensities. While this measure is suitable for detecting non-monotonic behavior in the SBC RLF, it does not quantify the gain of neuronal response, i.e. the change in firing rate across the sound intensity. Since non-monotonic behavior of the RLF is only present in about half of the SBC , we opted to use another measure. The original measure was normalized to the spontaneous activity and sampled identically for all cells, and thus quantifies the deviation of firing rates relative to the spontaneous activity. Still, the reviewer is right in pointing out that in the original analysis, the RLV will depend on the threshold of the neuron, i.e. for identical changes in firing rates, different measures of RLV might be obtained. While this should not pose a major problem for the comparison of ANF input and SBC output (which are both driven by the same ANF), we agree that the way the data were analyzed might be somewhat puzzling for the reader. We therefore calculated the RLV as suggested by the reviewer as the difference between minimal and maximal firing rate normalized to the spontaneous activity of ANF input and SBC output, thus providing a measure of the change in firing rates relative to spontaneous activity independent of the sampled sound intensities or the unit’s threshold. Consequently, the RLV was renamed rate-level gain (RLG) throughout the manuscript. The new analysis is shown in the revised 10) Effects of inhibition. The analysis of the effects of inhibition is very thorough, and beautifully shown, but does not adequately acknowledge similar results from earlier work. This is particularly noticeable in the final paragraph of subsection “Acoustically evoked inhibition elevates threshold for AP generation”, which does not acknowledge that Kopp-Scheinpflug, 2002 and Kuenzel, 2011 also showed similar tuning of inhibition. As the reviewer correctly pointed out, the analysis of tuning curves showed similar characteristics of inhibition as reported earlier. While we did not observe two distinct types of inhibition, consistent with Kuenzel et al. (2011), we found inhibition to be broader than excitation, which is consistent with . We now acknowledge and cite these earlier findings now in the revised manuscript.One of the main functions of inhibition seems indeed to be the control of gain, i.e. an overall shift in the firing rates and less dependence of firing rate on sound intensity. However, various studies showed, that the temporal precision also improves from the ANF to SBC on a single-unit level . Recent studies analyzing the EPSP size showed, that large EPSPs are better timed than small EPSPs . Modeling studies showed that an increase in temporal precision can be achieved by a sufficiently large number of converging inputs  and such non-endbulb inputs have been reported in the rat VCN . A recent modeling study showed that inhibition in combination with excitatory inputs at the dendrites could increase the temporal precision, but inhibition alone cannot . Additionally, as documented by Nerlich et al. 2014  the inhibitory inputs are rather slow, thus not suited to act on a spike-to-spike basis.To answer the question of the reviewer, we tested if the increase in vector strength, temporal precision, reproducibility and reproduction can be achieved by different mechanisms of inhibitory action. First, we simulated a pure divisive effect of inhibition, i.e. a random loss in output spikes. For that we used the input spike times of our recordings and pseudorandomly removed spike times to match the failure fractions observed in our recordings. The simulated SBC output then reflects a pure (divisive) scaling of the ANF input with failure rates identical to the experimental data. This simulation showed that a random reduction in postsynaptic firing rates is not sufficient to cause the observed increase in temporal precision and trial-to-trial reproducibility neither for SAM nor SFM stimulation see  and 2. NFinally, we applied the same simulations to the data obtained during RGS stimulation. Again, we find that purely divisive inhibition cannot explain the increase in sparsity, reproducibility and temporal precision, while pure subtractive inhibition matches the data well. The simulation of purely subtractive inhibition shows a larger improvement in sparsity and reproducibility than observed in the data.We conclude that the subtractive shift in SBC output rates compared to the ANF input can explain the increase in most metrics. A subtractive effect on the firing rates has been associated with hyperpolarizing inhibition with slow temporal dynamics , consistent with previous slice recordings in SBCs . However, we also observed a reduced response gain of the SBC output, which is generally attributed to shunting inhibition . Previous studies showed that both types of inhibition might be present at the SBC synapse and our data suggest that these two mechanisms result in a two-fold effect of increased temporal precision and reduced sound level dependence. While during SFM stimulation with constant stimulus level, the simulated subtractive inhibition matches the observed data well, during SAM stimulation, the simulated subtractive inhibition overestimates the effect on the SBC output. This might be caused by the inherent changes in stimulus level and the additional effect of inhibition on response gain in the experimental data.Regarding the temporal dynamics of inhibition: Considering the slow dynamics of inhibitory currents, the temporal relation between inhibition and excitation might play only a minor role. It seems conceivable that the slow inhibitory currents will summate during acoustic stimulation, resulting in a functionally tonic inhibitory conductance . However, it has been shown, that well-timed EPSPs are more likely to generate an SBC output than poorly timed EPSPs. Also, experimental data provided evidence that indeed the EPSP size, and – depending on this – the probability of SBC AP generation, does partially depend on the timing of the event , and it has been suggested that non-endbulb excitatory SBC inputs might facilitate AP generation of well-timed endbulb EPSPs . The degree of non-endbulb excitatory inputs converging on SBCs and their activation pattern during acoustic stimulation remains to be investigated to draw conclusions of their importance of signal processing.11) FM data period histograms. The FM stimuli appear to yield 2 or 3 peaks in the representative example of We agree with the reviewer that using VS for distributions with multiple peaks does not provide a sufficient measure of the temporal precision or reproducibility of the neuronal response. We included the VS for SFM stimuli to use a traditional, standardized measure for comparison with the SAM data and previous publications. We complemented VS with other measures of modulation depth , reproducibility and CorrNorm to provide a more comprehensive description of the temporal response properties. Also, the main focus of this analysis targeted a direct comparison of ANF input and SBC output rather than changes across modulation frequencies.However, to inform the reader about the potential problems in VS interpretation for the observed phase histograms, we added a paragraph at the Results section which reads:“It has to be considered that the interpretation of VS values is difficult when the phase histogram of the neuronal response shows multiple peaks . We, the12) STRF analysis: This analysis is very nice, but some clarification would be helpful.a) Why is the inhibitory STRF calculated by subtracting the ANF and SBC plots? Please explain how and why firing rates are normalized by the standard deviation. Is it normalized by subtraction or division? What if this normalization is not done? Please add a scale bar to these plots. Can this analysis be done instead by using only the EPSP(fail) for the kernel estimation? How would this approach differ from the subtraction approach?b) The size of the excitatory region is described as sharper in the SBC compared to ANF. This is not obvious in the example. Can the spectral width be indicated on the plot in We are happy to provide the information the reviewer asked for. We realize that some necessary information was missing in the original manuscript which happened by trying to keep the text succinct.Strictly speaking, we computed the difference STRF by subtracting the STRFs of ANF from the STRF of the SBC. Basically, this difference STRF could have positive and negative parts, but – in the recorded datasets – was dominated by negative parts, i.e. indicative of a reduction in response, which could be described as 'inhibitory STRF' , although not all missing spikes needed to be an indication for inhibition. We therefore (as far as we know) do not refer to it as "inhibitory STRF", but more generally as difference-STRF.For us, the normalization seems to be an appropriate procedure, since otherwise, the change in gain  dominates the change in shape of the STRF. We originally computed it on non-normalized STRFs, which leads to a similar result after the excitatory peak. However, one obtains a dominating, large peak at BF x Best Latency, which visually masks the changes thereafter. As suggested by the reviewer, we added color scale bars to two of the plots in the revised The difference STRF could also be computed using the EPSPfail, which would mathematically be equivalent (if the appropriate weighting is included) to the difference without normalization. We chose to perform it as the difference since it offers the possibility to normalize  in between the responses, and we can visualize each of the STRFs in comparison.Regarding spectral width: Indeed, the spectral half-width is only slightly reduced, and this is not the case not in all units. We now indicate the spectral half-width in the example cell visually, demonstrating that the unit shown is a representative example .The potential influence of sound level on the shape of the units’ response areas is an interesting suggestion: It would be interesting to test the level dependence of ANF and SBC output separately with the prediction that output shape would be more constant than input shape. Given the inhibitory influence, this could hold up to high sound levels, but may deteriorate at low sound levels.In previous studies in the MNTB , we experimented with different choices of sound level, essentially finding similar results as  in the auditory cortex (testing for sound contrast processing), namely that the shape of the STRF is mostly invariant, but that the scaling (e.g. via an output nonlinearity) is changed. Therefore, it could also be that changes in gain would be hidden in the STRF structure. But, at this point, this is speculation since in our stimuli overall loudness was not modulated. A consideration of possible sound level influence has been added to the Discussion.The computational aspects regarding the benefit of normalization are now explained in more detail in the Methods section, right where the STRF is introduced, and furthermore are also briefly mentioned in the Results section (with reference to the Methods).13) Thank you for encouraging us to address this topic directly: since reviewer point #10 asked a similar question, we partially have dealt with the mechanism above.http://f1000.com/work/citation?ids=1980015. It prevents ANF inputs from evoking SBC output spikes, a mechanism especially affecting poorly timed endbulb inputs . As a consequence, sparsity increases, as in many cases mainly the previously high peaks in firing rate remain, whereas a bulk of PSTH bins is shifted to low or zero firing rate bins. As indicated by the reviewer, this will lead kurtosis to increase, as well as the other two measures of sparsity . This shift is triggered by sound evoked glycinergic input, which covaries with excitation, but arrives slightly delayed More generally, we think that the subtractive part of the glycinergic inhibition, in combination with the well-timed, high firing rate events, can explain the improvement of sparsity, reproducibility and temporal precision. We tested this hypothesis by simulating the effects of simple divisive (remove fixed fraction of spikes – relative to the firing rate – from a time bin in a spiketrain) and subtractive (remove fixed number of spikes from each time bin in a spiketrain) inhibition see . The remFrom the literature it appears that the effect of glycinergic inhibition is not clearly subtractive or divisive . Hence, both a general increase in membrane permeability (leading to divisive firing rate effects) as well as shifts of membrane potential by hyperpolarization  seem to shape the SBC output (see Ayaz and Chance (2009) for more details). Subtractive and divisive inhibition have been shown to differently affect the neuron’s output and tuning properties. While sole subtractive inhibition can sharpen the tuning and shifts the stimulus-response function without altering the gain, divisive inhibition preserves the tuning and influences the response gain .We think that such a – potentially temporally unspecific – effect is nonetheless compatible with an increase in temporal precision, if the high peaks of the ANF firing rate are linked to specific acoustic events. In the cross-correlations underlying the computations of reproducibility and temporal precision, a removal of the bulk of spikes, and a focus on the peak rates, would lead to higher, tighter correlations. These precisely timed spikes are then preserved and transmitted to the MSO (see #1 for more detail). Assuming that the same occurs at the other AVCN, this would hence lead to a sharpening of interaural temporal correlation.These arguments were added to the Discussion of the revised manuscript as a new paragraph “Inhibitory mechanism for improving sparsity and reproducibility of the neural response”


Protein binding experiments validate this assertion by demonstrating pseudo-phosphorylated Ser-150 decreases the affinity of isolated TnI for actin, whereas Ser-23/24 pseudo-phosphorylation is not different from unphosphorylated. Thus, our data supports that TnI Ser-23/24 affects TnI-TnC binding, while Ser-150 phosphorylation alters TnI-actin binding. By measuring force development in troponin-exchanged skinned myocytes, we demonstrate that the Ca2+ sensitivity of force is directly related to the amount of phosphate present on TnI. Furthermore, we demonstrate that Ser-150 pseudo-phosphorylation blunts Ser-23/24-mediated decreased Ca2+-sensitive force development whether on the same or different TnI molecule. Therefore, TnI phosphorylations can integrate across troponins along the myofilament. These data demonstrate that TnI Ser-23/24 and Ser-150 phosphorylation regulates muscle contraction in part by modulating different TnI interactions in the thin filament and it is the combination of these differential mechanisms that provides understanding of their functional integration.Troponin I (TnI) is a major regulator of cardiac muscle contraction and relaxation. During physiological and pathological stress, TnI is differentially phosphorylated at multiple residues through different signaling pathways to match cardiac function to demand. The combination of these TnI phosphorylations can exhibit an expected or unexpected functional integration, whereby the function of two phosphorylations are different than that predicted from the combined function of each individual phosphorylation alone. We have shown that TnI Ser-23/24 and Ser-150 phosphorylation exhibit functional integration and are simultaneously increased in response to cardiac stress. In the current study, we investigated the functional integration of TnI Ser-23/24 and Ser-150 to alter cardiac contraction. We hypothesized that Ser-23/24 and Ser-150 phosphorylation each utilize distinct molecular mechanisms to alter the TnI binding affinity within the thin filament. Mathematical modeling predicts that Ser-23/24 and Ser-150 phosphorylation affect different TnI affinities within the thin filament to distinctly alter the Ca Calcium regulation of the myofilament through this binding event is not a simple “on and off” switch  represents a key mechanism in modulating the myofilament response to Ca2+-sensitivity  but accelerating relaxation (accelerated Ca2+ dissociation)  was conducted according to manufacturer's instructions to generate cDNA constructs encoding human TnI pseudo-phosphorylations: Ser-150 to Asp (S150D), Ser-23/24 to Asp (S23/24D), Ser-23/24 to Asp with Ser-150 to Asp (S23/24/150D). All resultant constructs were verified by DNA sequencing.Escherichia coli and purified to homogeneity as previously described  was used to coat a 96-well microtiter plate in 100 μL/well at 4°C overnight. Unbound actin was washed with Buffer T (Buffer A containing 0.1% Tween-20). Following washes, the wells were blocked with Buffer A containing 1% BSA. Following removal of blocking solution, serial dilutions of TnI WT or phosphomimetics  were incubated in 100 μL/well for 2 h at room temperature. The wells were then washed and bound TnI was quantified by ELISA using a mouse anti-cardiac TnI antibody  and appropriate HRP-conjugated secondary antibody. Following the addition of H2O2-ABTS  substrate solution, the absorbance at 405 nm was monitored over the linear course of color development. Protein binding assays were conducted in triplicate wells and repeated. Each assay contained wells that were coated with F-actin but incubated with buffer in the absence of TnI as a negative control.ELISA-based solid-phase protein binding assays were conducted as previously described to determine the effect of phosphorylation to alter TnI binding to actin compared to that of non-phosphorylated TnI  containing 1% peroxide-free Triton X-100  with incubation at room temperature for 10 min rocking. Following skinning, myocytes were centrifuged and immediately resuspended for Tn exchange.All animal protocols and procedures were performed in accordance with National Institutes of Health guidelines and approved by the Institutional Laboratory Animal Care and Use Committee at The Ohio State University. Steady-state Ca2, 1 DTT, 5 EGTA, 20 MOPS, pH 6.5) containing 13 uM column purified Tn overnight at 4°C  , heated for 5 min at 80°C and clarified by centrifugation for 5 min. SDS-PAGE and Western blot were carried similar to that previously described  while maintaining accelerated thin filament Ca2+ dissociation (accelerated relaxation)  recently developed by the Davis laboratory to identify mechanisms involved in TnI phosphorylation integration   , S23/24 pseudo-phosphorylated TnI (Tn S23/24D), S150 pseudo-phosphorylated TnI (Tn S150D) or S23/24/150 pseudo-phosphorylated TnI (Tn S23/24/150D). Following exchange, the percent of exogenous Tn incorporated into all measured myocyte preparations was determined by Western blot, as the exogenous human cardiac TnI migrates faster by SDS-PAGE than endogenous rat cardiac TnI. Exchange quantification demonstrated an average of 57% exogenous Tn incorporation that was not different for any of the different Tn's exchanged    or 50% Tn S150D (Tn WT+S150D). Western blot for cardiac TnI demonstrated the incorporation of 51.7 ± 3.3% WT+S23/24D and 56.5 ± 6.9% WT+S150D exogenous Tn (p > 0.05)    that resulted in an average of 56.8 ± 7.5 percent total exogenous Tn exchange. Thus, the Tn S23/24D+S150D exchange group is expected to result in cells containing ~25% TnI S23/24D, ~25% S150D and ~50% endogenous WT Tn in the myofilament. Calcium sensitivity of cells exchanged with Tn S23/24D+S150D was not different from those exchanged with Tn S23/24/150D, even though the pseudo-phosphorylations occurred on separate Tn molecules  TnI Ser-23/24 or Ser-150 phosphorylation occupancy linearly alters Ca2+-sensitive force development. (3) The mechanisms utilized by Ser-23/24 and Ser-150 phosphorylation integration to affect Ca2+-sensitive force development occur both when on the same and separate Tn molecules along the myofilament.The phosphorylation of different TnI residues is altered by physiological and pathological stress and therefore the combined function of multiple TnI phosphorylations plays a critical role to differentially modulate cardiac function in both the normal and diseases states. Our current study aimed to elucidate underlying mechanisms utilized by Ser-23/24 and Ser-150 phosphorylation. Our findings demonstrate that: (1) The integrated function of TnI Ser-23/24 and Ser-150 phosphorylation is dependent in part upon their regulation of different TnI protein-protein interactions within the thin filament. TnI Ser-23/24 phosphorylation alters the dissociation of the TnI C-terminus from TnC, while TnI Ser-150 phosphorylation alters binding of the TnI C-terminus to actin and slows the dissociation of Ca2+-sensitive force production . Data from our model and binding studies suggest that Ser-23/24 phosphorylation acts, at least in part, by altering the TnI-TnC interaction to increase the dissociation of TnI from TnC-Ca2+ . It is therefore the combination of each TnI phosphorylation's affect on different mechanisms within Tn to alter myofilament activation that imparts functional integration. The integrative role of multiple TnI phosphorylations thus allows for the complex fine-tuning of cardiac function through Tn.In all, our current study demonstrates that the apparent magnitude and rates of myofilament activation and deactivation are dependent on multiple mechanisms functioning within Tn that comprise intricate protein-protein interactions. We demonstrate different TnI phosphorylations can affect varied mechanisms within Tn that differentially alter specific contractile functions , HL114940-S1 (to HS), HL091986 (to JD), HL113084 (to PJ) and American Heart Association grant GRNT27760114 (To MZ).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.