Whole cell patch-clamp recordings were made from visually-identified NA cells using IR/DIC (infrared/differential interference contrast) video microscopy (Stuart et al. 1993). Initial micropipette resistances were 3–7 MΩ with an internal solution of (mM): 120 potassium gluconate, 20 KCl, 0.1 EGTA, 2 MgCl₂, 2 Na₂ATP, 10 Hepes, and 0.1% biocytin. Electrical recordings were made using an AxoPatch 200B (Axon Instruments, Foster City, CA) in voltage-clamp mode with the series resistance corrected by 60–90%. Experiments were carried out in 20 μM bicuculline and 1–3 μM strychnine to eliminate measurement contamination by GABA/glycine inhibitory currents. Fast AMPA-receptor mediated excitatory synaptic currents were isolated by the application of 25–75 μM APV. Stimulation and recordings were controlled by a PC running custom software written in the programming software IGOR Pro programming environment (Wavemetrics, Lake Oswego, OR) with a National Instruments A/D board (National Instruments, Austin, TX). Stimuli biphasic waveforms passed through an analog, constant-current stimulus isolation unit (World Precision Instruments, Sarasota, FL) using metal bipolar electrodes.

Synaptic stimulation was performed as previously described (MacLeod and Carr 2005). Extracellular stimulation of the 8^th (auditory) nerve fibers tracts was accomplished with a tungsten monopolar or bipolar electrode. As has been shown in a previous paper, each NA neuron receives synaptic input from multiple auditory nerve fibers (MacLeod and Carr 2005). Stimulation amplitude was carefully controlled by measuring a series of stimulation levels to find the response threshold and plateau region to isolate a response that evoked an EPSC typically between 50–300 pA in peak amplitude. The stimulation level was then set to a value in the center of the plateau region to avoid contamination of train responses by additional fiber recruitment. Experiments in which recruitment was suspected were discarded from our analysis. We evoked trains of 8–10 pulses with a constant interpulse interval (4, 5, 7, 10, 30, and 100 ms), which we report as stimulus “rate” (250, 200, 143, 100, 33, 10 Hz, respectively). An additional recovery test pulse was delivered 2 s after the last pulse in the train. For each cell, 4–6 different train rates were tested in interleaved trials. Intertrial intervals were 10–15 s for trains, and 5–10 s for the paired pulse experiments.

Peak EPSC amplitudes were measured relative to a baseline calculated from the previous EPSC. Descriptions of the short-term plasticity changes are given as ratios relative to the initial EPSC amplitude (ratios >1 were enhanced; ratios <1 were depressed) or alternatively as an equivalent percentage (above 100% means synapses were enhanced; less than 100% were depressed). “Paired pulse ratio” was the ratio of the 2^nd EPSC to the 1^st EPSC, for both the trains and paired stimuli. “Steady state ratio” (trains only) was the ratio of the average of the last 3 EPSCs to the 1^st EPSC. While preliminary work that used longer trains (10–15) had suggested that the EPSCs reached steady state by the 7^th or 8^th response, analysis of the neurons here showed that this was not always the case, and therefore ‘steady state’ should be taken as “near steady state”. However, analysis of 10-pulse trains showed that there was generally <6% variation between pulses 8–10 than during pulses 6–8 (often due to variability; and only one case showed that the synapses had clearly not reached a steady state). No qualitative differences in the steady-state versus input rate functions were observed. Re-analysis using pulses 6–8 (ignoring 8–10) had no impact on the results.

In contrast to the profound short-term depression uniformly expressed at synapses in the brain stem pathways that encode timing information, we found that the short-term synaptic plasticity recorded at the auditory nerve to NA neuron synapses was typically composed of a mixture of facilitation and depression (Fig. 1). In this paper, we use the term “enhancement” to describe the measured net increase in the EPSC amplitude and the term “facilitation” to refer to the putative underlying synaptic molecular mechanism, presumed to be a presynaptic increase in release probability. Enhancement of the EPSC amplitude (above a criterion of 10% greater than initial EPSC amplitude) was observed in a subset of NA recordings for at least one stimulus rate (n = 12/34; Fig. 1). For example, enhancement of the EPSCs recorded from one NA neuron during stimulus input trains of 100, 143, and 200 Hz is shown in Fig. 2. A maximal EPSC amplitude occurred on the 2^nd or 3^rd stimulus and some enhancement was sustained throughout the rest of the train (Fig. 2 A and B). For the 250 Hz input train, the EPSC amplitude declined after an initial enhancement back to the amplitude of the initial EPSC. In contrast, the EPSCs recorded during 10 Hz and 33 Hz input trains did not show any transient enhancement (Fig. 2A), but instead depressed in amplitude to a steady state ratio of 0.6 and 0.8, respectively (Fig. 2B, markers). In other recordings, the enhancement of EPSC responses was moderate, limited to less than a two-fold increase over the initial EPSC amplitude. The enhancement was often followed by a depression in the recovery EPSC in response to a stimulation delivered after a delay period (2 s after the end of the train; “R”, in Fig. 2A). This “anomalous” recovery response suggested that the EPSCs were the result of two competing mechanisms, one which facilitated the synaptic response and the other which depressed the synaptic response. In the case of enhancement during the train, the facilitating factor dominated. At the time of the recovery response, the facilitating factor had declined, and the depressing factor dominated. These effects could be reproduced with a quantitative model of synaptic plasticity, described below.

To determine whether the synaptic responses observed could be accounted for by a simple model of short-term plasticity, we fit each synapse individually with a dynamic model of synaptic release, similar to a class of models of plasticity published previously by a number of investigators (see METHODS; Abbott et al. 1997; Dittman et al. 2000; MacLeod and Horiuchi 2006; Tsodyks and Markram 1997). EPSCs were presumed to result from the fractional release of vesicles from a release-ready pool at the time of each presynaptic stimulation. The model contained both depression and facilitation. Depression resulted from a decrease in the size of the release-ready pool due to depletion, and recovery from depletion followed a two-step vesicle pool replenishment process. Facilitation was implemented by making the vesicle release fraction activity- and time-dependent. EPSCs were generated by the model for each stimulus in the train, and compared with the data for each individual neuron (Fig. 2E; see also Supplemental Figures S1 and S2¹). Using a randomly seeded gradient descent method to find the parameters, we were able to fit nearly all the NA cells (30 of 34; 4 were excluded due to data irregularity). The time-dependent variables in the model during the input trains are shown for fits to three NA synapses (panels C and D in Figs. 2–4; specific model parameters are given in the legend). In general, the data were well fit with a fast recovery time constant for the readily releasable pool (τ_k1 = 20.1 ± 16.0 ms, n = 30), and a slow recovery time constant for the backup pool (τ_k2 = 8.4 ± 9.3 s, n = 30; excluding one outlier at 80.9 s) (Table 1; Fig. 5). Many NA profiles were best fit with some degree of facilitation (average τ_F = 42.4 ± 30.0 ms, n = 20; excluding one outlier of 542 ms). Other NA profiles were best fit by minimizing the facilitation, including all those that were classified as monotonically depressing (the Xs in Fig. 5B clustered on far left). The initial release fraction (F₀, equivalent to an average probability of release) ranged from 0.07 to 0.92 (0.43 ± 0.21; n = 30). The fit was considered good if the error was low (compared with a zero-plasticity baseline error), and if several other qualitative criteria were met. Besides a good overall fit to the train data, model should fit the recovery response, especially the “anomalous” recovery. For example, the recovery responses in Fig. 2, C and D show that long after the facilitation parameter had relaxed to its initial value (top dashed line), the release-ready vesicle pool had not yet fully recovered (solid line). This resulted in a model recovery EPSC that was smaller than the EPSCs during the train (black dot late in the trace), matching the recovery EPSCs in the data. Finally, the model synapses should reproduce the profile of the steady state function (solid lines in panel B, Figs. 2–4). Reduced models in which a single pool was modeled, the relative pool sizes were held to be equal, or facilitation was eliminated (for those that required it) produced poorer fits, or failed to meet the criteria listed above. Thus we were able to account for the wide range of synaptic plasticity profiles in our data sample with a simple quantitative model of vesicle release, and a restricted parameter range (Fig. 5).

Summary data for all the train data obtained from NA recordings are shown in Fig. 6. Because there were no differences observed related to the cell types within NA (defined by their firing responses to current injection; see supplementary Fig. S3), the data are pooled here. The paired pulse ratios (i.e., the ratio of the 2^nd EPSC in the train to the 1^st EPSC) plotted in Fig. 6A demonstrated that for most NA neurons, there was either enhancement (in 11/34 neurons) or generally weak paired pulse depression, in contrast to the strong paired pulse depression seen in NM and NL neurons (see following text, Fig. 7). The average paired pulse ratio remained about 0.80–0.90 across all rates, with a slight maximum at 33–100 Hz (Fig. 6A). The average steady state ratio profile with input rate was also nonmonotonic, similar to many individual steady state profiles, with a maximum at 33 Hz (0.77 ± 0.23, mean ± SD; Fig. 6B). The average steady state ratio decreased to 0.70 ± 0.18 at 10 Hz, and was minimal at 250 Hz at 0.52 ± 0.26.

The average initial EPSC amplitude recorded in the NA neurons was 232.3 ± 212.4 pA, and ranged from 30.7 pA to 1113.3 pA (n = 34). There was no correlation between the amplitude of the initial EPSC with the paired pulse or steady state ratio at any frequency. Thus smaller EPSC recordings were no more or less likely to demonstrate depression, or facilitation, than those of larger EPSCs. The size of the EPSC reflects of the number of inputs recruited with a given stimulus intensity, and is unrelated to release probability (MacLeod and Carr 2005).

Although the paired pulse ratio was not related to initial EPSC amplitude, the ratio could be altered by changes in the external calcium concentration. We investigated the calcium dependence of the paired pulse ratio with a 10 ms interval. Application of an elevated calcium ACSF (4 mM) shifted the paired pulse ratio toward more depression with a ratio of 0.70, compared with 1.02 in control calcium (2 mM; n = 6; P < 0.01). Elevated calcium also slightly increased the amplitude of the average initial EPSC, from 114.9 pA to 143.2 pA (n = 6; P = 0.113). Under decreased calcium conditions (1 mM), the paired pulse ratio shifted toward greater enhancement, from 1.01 to 1.58 (n = 5; P < 0.01) and decreased the amplitude of the initial EPSC, from 152.9 pA to 58.4 pA (n = 5; P = 0.055). During individual recordings, paired pulse ratios could change from net enhancement to depression and vice versa.

To compare the short-term plasticity found in NA recordings with those in the timing nuclei, we made recordings from neurons in the cochlear nucleus magnocellularis (NM) and nucleus laminaris (NL). Synaptic responses recorded in NM neurons showed profound depression at this age, as has been shown previously (Fig. 7A) (Zhang and Trussell 1994b). The steady state amplitude monotonically decreased with input stimulus rate (Fig. 8A; open squares, n = 3 NM neurons). These experimental results are similar to that predicted by a model of simple depression in NM (dashed line; see METHODS). The synapses onto NM neurons showed significantly more depression on average than those onto NA neurons at input rates 200 Hz and 250 Hz (P < 0.05). NL neurons receive synaptic input from NM, and these synapses also show profound short-term depression (Cook et al. 2003; Kuba et al. 2002). We found that NM to NL synapses expressed significantly more depression than auditory nerve to NA synapses for all rates (Fig. 7B; Fig. 8A, open triangles; n = 5 NL neurons; P < 0.05). Although NA synaptic responses often showed net depression at steady state, they nevertheless showed much less depression on average than the synaptic responses recorded in NM or NL neurons, and had a distinctly different steady state synaptic transfer function.

To understand how the short-term plasticity expressed at the auditory nerve to cochlear nucleus synapse affects the transmission of spike rate information, we analyzed how the total steady state synaptic conductance is related to the input rate (Abbott et al. 1997; Markram et al. 1998b; Tsodyks and Markram 1997). We used as an approximation of total conductance the product of the steady state amplitude with the presynaptic spike rate (Fig. 8B). This calculation provides an estimate of the net impact on the postsynaptic neuron per unit time, assuming linear summation of the input conductance. For depressing synapses such as those in NM, the relationship of the total conductance with input rate is a nonlinear, saturating curve (Fig. 8B, symbols as in Fig. 8A). This is because the decreasing steady state amplitude offsets the increase in input stimulus rate, keeping the total conductance nearly constant. The curve for NA was linear (Fig. 8B; linear fit, slope = 0.50, R² = 0.98), implying that changes in the input stimulus rate would result in a proportional change in total postsynaptic conductance.

Individual NA neurons received synaptic inputs that expressed varying degrees of facilitation and depression, with some expressing primarily depression. This variability suggests the intriguing speculation that differential filtering may occur within NA. We have demonstrated how short-term plasticity may contribute to maintaining intensity information (in the case of the nonmonotonic NA synapse); a purely depressing synapse, on the other hand, could signal changes in intensity, a potentially important cue in many auditory signals, such as speech and birdsong. Computational studies suggest that a synaptic filter can have numerous effects on the signal. Depending on the properties of the synaptic filter, it can implement low- or band-pass filtering (Chance et al. 1998; Fortune and Rose 2002; Fuhrmann et al. 2002), shift the phase of the firing activity (Chance et al., 1998), selectively transmit changes in stimulus magnitude (Abbott et al. 1997; Markram et al. 1998a), and differentiate between bursts of presynaptic behavior or tonic input (Matveev and Wang 2000; Tsodyks and Markram 1997). In the data presented here, no differences were observed between the different cell types, in terms of the shape of the steady-state functions with input rate (i.e., simple depressing versus nonmonotonic) or quantitatively across input rates. The conclusion that plasticity and cell type have no relationship may be confounded by two factors: variations in age and position within NA. A developmental reduction in short-term depression has been well documented at the synapses in NM (Brenowitz and Trussell 2001a). We did not observe any effects by age, but nearly all the recordings were carried out at E17 or E18, and further work in more mature chicks will be needed to determine how development may affect the plasticity expressed in NA. Furthermore, we observed differences across recordings within the same slice, although we cannot completely exclude the possibility that variation in plasticity reflects variation in the rates of maturity at individual synapses. Interestingly, if maturity results in similar changes at NA synapses as those in NM (less depression, perhaps due to improved vesicle cycling or recruitment), many NA information transfer profiles (i.e., plot in Fig. 6B) may more closely approximate linearity, further improving the transmission of intensity information.