Development of on-off spiking in superior paraolivary nucleus neurons of the mouse

Abstract

The superior paraolivary nucleus (SPON) is a prominent cell group in the auditory brain stem that has been increasingly implicated in representing temporal sound structure. Although SPON neurons selectively respond to acoustic signals important for sound periodicity, the underlying physiological specializations enabling these responses are poorly understood. We used in vitro and in vivo recordings to investigate how SPON neurons develop intrinsic cellular properties that make them well suited for encoding temporal sound features. In addition to their hallmark rebound spiking at the stimulus offset, SPON neurons were characterized by spiking patterns termed onset, adapting, and burst in response to depolarizing stimuli in vitro. Cells with burst spiking had some morphological differences compared with other SPON neurons and were localized to the dorsolateral region of the nucleus. Both membrane and spiking properties underwent strong developmental regulation, becoming more temporally precise with age for both onset and offset spiking. Single-unit recordings obtained in young mice demonstrated that SPON neurons respond with temporally precise onset spiking upon tone stimulation in vivo, in addition to the typical offset spiking. Taken together, the results of the present study demonstrate that SPON neurons develop sharp on-off spiking, which may confer sensitivity to sound amplitude modulations or abrupt sound transients. These findings are consistent with the proposed involvement of the SPON in the processing of temporal sound structure, relevant for encoding communication cues.

the temporal structure of acoustic signals is a fundamental cue for perceiving communication vocalizations, including human speech (Shannon et al. 1995; Smith et al. 2002; Holy and Guo 2005; Grimsley et al. 2011). Temporal structure is characterized in part by low-rate periodic modulations of the sound envelope, abrupt stimulus starts and stops, and short gaps between sounds (Frisina 2001; Joris et al. 2004). Although the accurate representation of temporal sound features is critically important for normal behavior in mammals, the neural mechanisms governing these processes are not fully understood. Growing evidence suggests that the superior paraolivary nucleus (SPON), a prominent cell group in the auditory brain stem (Kulesza et al. 2002; Bazwinsky et al. 2003; Kulesza 2008), may be specialized for encoding temporal information (Behrend et al. 2002; Kulesza et al. 2003; Kadner and Berrebi 2008; Felix et al. 2011; Kopp-Scheinpflug et al. 2011).

SPON neurons typically respond to pure tones with precisely timed transient spikes triggered by the stimulus offset (Dehmel et al. 2002; Kulesza et al. 2003; Felix et al. 2012). Transient spiking to the stimulus onset has also been reported (Behrend et al. 2002; Dehmel et al. 2002; Kulesza et al. 2003); however, the prevalence of this response component as well as its role in SPON function remain unclear. In addition to pure tones, SPON neurons also respond to temporally complex stimuli, such as low rates of envelope amplitude modulation, with highly entrained spiking to individual modulation cycles (Behrend et al. 2002; Kadner and Berrebi 2008). SPON neurons are also capable of signaling the presence of short gaps within ongoing sound stimuli (Kadner and Berrebi 2008). Thus, SPON neurons are well suited to faithfully transmit temporal information via their GABAergic projections to the inferior colliculus (IC), a major site of sensory integration located in the midbrain (Schofield 1991; Kulesza and Berrebi 2000; Saldaña and Berrebi 2000; Saldaña et al. 2009).

The information available to date suggests that the SPON likely plays an important role in processing temporal information, but very little is known about the cellular characteristics of its neurons and how they develop intrinsic membrane properties that enable them to respond to stimuli with such high temporal fidelity. The present study examined the firing characteristics and development of membrane properties of SPON neurons. To investigate the link between the morphology of recorded cells and their firing properties, anatomic reconstructions of physiologically classified neurons were performed. In addition, properties of depolarization-driven onset spiking were characterized in vitro and compared with hyperpolarization-evoked rebound spiking at the stimulus offset to measure the temporal precision of each response component. Furthermore, the temporal precision of onset and offset spiking responses were compared in vivo using single-unit recordings in young mice. The results demonstrate that both onset and offset spiking components are robust and well timed and that this precision becomes sharper during development.

MATERIALS AND METHODS

In vitro recordings were conducted on SPON neurons in mice from the C57BL/6 strain [age: postnatal day (P)5–P20], and in vivo recordings were made in the CBA/CaJ strain (age: P20–P22). To rule out possible differences in cellular properties of SPON neurons in the C57/BL6 and CBA/CaJ mice used in the in vitro and in vivo experiments, respectively, brain slices were also prepared from CBA/CaJ mice in a subset of experiments. No differences in intrinsic properties or spiking patterns were found in the depolarizing range between the mouse strains, which is in line with a previous study comparing the same properties in the hyperpolarizing range (Felix et al. 2011). Experimental procedures were in accordance with the EC Council Directive (86/89/ECC) and the National Institutes of Health Guide for the Care and Use of Laboratory Animals and were approved by local Animal Care and Use Committees in Sweden (permits N13/10 and N71/10) and West Virginia University.

Slice Preparation

Mice were decapitated under pentobarbital sodium anesthesia, and the brain stem was carefully removed and placed in ice-cold low-sodium, high-sucrose artificial cerebrospinal fluid (aCSF; see below). Transverse brain stem slices containing the superior olivary complex were obtained at a thickness of 150–200 μm using a vibratome (VT1200, Leica, Wetzlar, Germany) and incubated at 32°C in normal aCSF (see below) for 20–30 min, after which they were allowed to cool to room temperature. Current-clamp recordings were obtained within 4–5 h of brain slicing.

Solutions and Drugs

The low-sodium, high-sucrose aCSF contained (in mM) 85 NaCl, 2.5 KCl, 1.25 NaH₂PO₄, 25 NaHCO₃, 75 sucrose, 25 glucose, 0.5 CaCl₂, and 4 MgCl₂, whereas normal aCSF contained (in mM) 125 NaCl, 2.5 KCl, 1.25 NaH₂PO₄, 26 NaHCO₃, 25 glucose, 2 CaCl₂, and 1 MgCl₂. Solutions were continuously bubbled with carbogen gas (95% O₂-5% CO₂), with the pH set to 7.4. The internal pipette solution for recordings contained (in mM) 130 K-gluconate, 5 KCl, 10 HEPES, 1 EGTA, 2 Na₂-ATP, 2 Mg-ATP, 0.3 Na₃-GTP, and 10 Na₂-phosphocreatinine, adjusted to pH 7.3 with KOH.

Recording Procedures

Slices were transferred to a recording chamber perfused (∼3 ml/min) with oxygenated aCSF at room temperature (22 ± 2°C). Putative SPON principal cells were viewed with an upright microscope (Zeiss Axioscope, Oberkochen, Germany) equipped with a digital charge-coupled device camera (Orca 2, Hamamatsu, Tokyo, Japan) using a ×40 water-immersion objective (Achroplan, Zeiss) and infrared differential interference optics. SPON cells were visually identified by their large somata in a clearly delineated area medial to the lateral superior olive. Neuron size was estimated from the capacitance compensation measurement under voltage clamp. Only large neurons with a capacitance of >20 pF were included in the analysis. Whole cell current-clamp recordings were performed throughout the SPON with a Multiclamp 700B amplifier (Axon Instruments, Foster City, CA) using borosilicate glass microelectrodes with a final tip resistance of 5–10 MΩ. The bridge balance was applied for current-clamp recordings. The majority of recordings was performed at 22 ± 2°C. To estimate temperature dependency, temperature coefficient (Q₁₀) values were calculated from recordings made at 36°C using the following formula: Q₁₀ = (R₁/R₂)e^{(10/T₂ − T₁)}, where R₁ and R₂ are the measured action potential rise and decay rates at the control (T₁) and test temperatures (T₂), respectively.

Data Acquisition and Analysis

Recorded signals were filtered with a low-pass four-pole Bessel filter at 10 kHz, sampled at 20 kHz, and digitized using a Digidata 1422A interface (Axon Instruments). Stimulus generation, data acquisition, and offline analysis of data were performed using pClamp software (version 10.2, Axon Instruments) or IgorPro (version 6.12A, WaveMetrics, Lake Oswego, OR).

Action potential threshold was defined as the membrane potential at which the first derivative crossed 10 mV/ms. The inflection rate was estimated by fitting a linear regression to the three data points closest to the spike threshold. Spike latency, amplitude, and afterhyperpolarization were estimated with reference to the defined spike threshold voltage. In cases where no local minimum was detected in the repolarization phase, the afterhyperpolarization was not characterized.

Input-output curves were computed as steady-state firing rates as a function of input current. For each current step, spikes were counted in a 450-ms time window starting 50 ms after the current onset, thereby yielding zero firing rates in onset spiking neurons.

Immunocytochemistry and Cell Reconstructions

SPON neurons were filled with 0.1% neurobiotin contained in the patch pipette and visualized with Texas red-streptavidin (20 μg/ml, Molecular Probes, Eugene, OR). After the recordings had been completed, brain slices were placed in 4% paraformaldehyde overnight, and immunohistochemistry was applied to free-floating brain slices (Felix et al. 2011). Sections were incubated with rabbit anti-vesicular GABA transporter (VGAT) primary antibody (1:400, Vector Labs, Burlingame, CA). VGAT immunoreactivity was visualized with a secondary antibody raised in the donkey conjugated to Alexa 488 (1:200, Molecular Probes). Sections were viewed with a fluorescence microscope (Observer Z1, Zeiss) equipped with a digital camera (AxioCam, MRm). Immunolabeling was identified with red and green filters that permitted visualization of Texas red and VGAT, respectively. Images from red and green channels were acquired digitally and overlaid (AxioVision version 4.8, Zeiss) to view filled neurons against background VGAT staining of the superior olivary complex. Brain slices that contained successfully filled neurons were imaged on a laser scanning confocal microscope (LSM 510, Zeiss) using a ×25 oil-immersion lens (numerical aperture: 0.8, Zeiss). Cell reconstructions were performed with Imaris (version 7.2.3, Bitplane, Zurich, Switzerland). A combination of automatic and manual tracing functions was used to map the cell bodies and processes of fluorescent cells. The morphological parameters examined included soma volume and sphericity as well as the number of dendritic branches and end points, as shown in Table 1. In addition, convex hull volume and sphericity were examined to characterize the overall shapes of neurons, including dendritic fields (Malmierca et al. 1995). For each filled neuron, the convex hull was calculated using a function in Imaris by creating points at the tips of each distal dendrite. These points were then connected, creating a three-dimensional convex polygon (i.e., envelope) that encompassed the neuron, thereby providing a measure of the degree to which each cell's dendrites extended within the tissue.

Table 1.

Morphological properties of SPON neurons that exhibited distinct spiking patterns

Property	Onset	Adapting	Burst	Description
Soma volume, μm3	6,302.0 ± 492.6	4,123.5 ± 1,001.9	6,609.4 ± 1,003.9	Volume of the soma
Soma sphericity	0.72 ± 0.04	0.75 ± 0.03*	0.60 ± 0.03*	Ratio of the surface area of a sphere (with the same volume as the soma) to the surface area of the soma
Total dendrite length, μm	678.9 ± 109.3*	766.0 ± 110.5	1303.6 ± 203.3*	Sum of lengths of all dendrite segments, measured along tracing (not straight line distance)
Dendritic node total	5.7 ± 2.0*	12.3 ± 4.0	20.1 ± 4.1*	Total number of dendritic nodes (branching points)
Dendritic end points	8.2 ± 2.2	15.7 ± 4.7	23.4 ± 4.4	Total number of dendritic terminal points (end points)
Convex hull volume, μm3	5.86 × 105 ± 1.31 × 104	7.27 × 105 ± 1.84 × 104	9.31 × 105 ± 1.84 × 104	Volume of the three-dimensional convex polygon created by connecting the distal dendrite segment end points
Convex hull sphericity	0.73 ± 0.02	0.72 ± 0.03	0.73 ± 0.02	Ratio of the surface area of a sphere (with the volume of the dendritic convex hull) to the surface area of the convex hull

Values represent means ± SE; n = 8 neurons with an onset response type, 6 neurons with an adapting response type, and 7 neurons with a burst response type.

^*Statistical significance between groups according to the Kruskal-Wallis test and Kramer-Tukey correction (P < 0.05).

In Vivo Recordings

Surgical procedures.

Before surgery, mice were deeply anesthetized with a mixture of ketamine (100 mg/kg) and xylazine (5 mg/kg) and placed in a stereotaxic frame. Once the animal ceased to respond to nociceptive stimuli, the scalp was incised and reflected laterally. A lightweight headpost was then attached to the skull using dental cement (Charisma, Heraeus Kulzer, Germany), and a small craniotomy (∼1.5-mm diameter) was performed to gain access to the brain stem. After surgery, animals were moved to a sound-attenuated chamber, where the headpost was secured to a stereotaxic device that kept the head in a fixed position during recordings. The core body temperature was maintained at 37°C using a thermostatically controlled heating pad (FHC, Bowdoin, ME). Local anesthetic (5% lidocaine gel) was applied to the wound margins every 2 h, and the depth of anesthesia was monitored with a foot pinch each time the electrode was moved.

Acoustic stimulation.

Stimulus generation and data acquisition were controlled by custom-written software (Batlab, Donald Gans, Northeast Ohio Medical University). Stimulus waveforms were output through a 16-bit digital-to-analog converter (400,000 samples/s, model DAP52la, Microstar Laboratories, Bellevue, WA), and analog signals were sent to an antialiasing filter [FT6–2, Tucker-Davis Technologies (TDT), Alachua, FL] and fed to a programmable attenuator (PA-5, TDT). Signals were then routed to speaker drivers (ED1, TDT) and presented to the animal through free-field speakers (ES1, TDT) placed 5 mm from the opening of each ear. The speaker output was calibrated offline over a range of 0.5–60 kHz using a measuring amplifier (model 2610, Brüel and Kjær, Norcross, GA) connected to a condenser microphone (model 4939, Brüel and Kjær) positioned at the location occupied by the animal's head during the recording.

Recording procedures.

Single-unit extracellular recordings were conducted with glass pipettes filled with 0.5 M NaCl recording solution, yielding a resistance of 10–20 MΩ. Neuronal activity was amplified (model 2400, Dagan, Minneapolis, MN), bandpass filtered (200–3000 Hz, model 3364, Krohn-Hite, Brockton, MA), and digitized by a 16-bit analog-to-digital converter (42 kHz, DAP52l6a, Microstar). Individual raw waveforms were recorded in Batlab and stored for offline analysis.

Recordings were guided by stereotaxic coordinates to locate the SPON (Paxinos and Franklin 2005). Once a single unit was isolated, its characteristic frequency (CF), defined as the frequency requiring the lowest intensity to elicit stimulus-evoked spikes to at least 50% of the presentations, and its minimum threshold, defined as the lowest intensity that elicited a consistent spiking response, were determined. Recordings were made from neurons covering the most sensitive range of hearing in the mouse (CFs of 10–41 kHz) (Ehret 1975). Peristimulus time histograms were constructed by presenting CF pure tones at 20 dB above threshold (100 repetitions), and mean first-spike latencies and jitter values were extracted using these measurements.

Localization of recording sites.

The neural tracer biocytin (Sigma) was deposited with current (+1 μA, 5 min, 50% duty cycle) during one electrode penetration in each animal. At the conclusion of each recording session, mice were perfused with 4% paraformaldehyde. Brain stems were removed from the calvaria, cryoprotected in 30% sucrose, and sectioned transversely at 40 μm. Sections were stained for Nissl substance to reveal the boundaries of superior olivary nuclei and processed to reveal biocytin deposits (Kulesza et al. 2003).

Statistics

Data analysis was accomplished using a variety of nonparametric tests in MatLab (Statistics Toolbox, MathWorks). The Kruskal-Wallis test was used to compare the variance across groups. We used the Tukey-Kramer method for multiple comparisons between groups. Dependence of two variables was assessed by Pearson's correlation coefficient (r) and stated as the coefficient of determination (r²). Significance of r is based on a transformation of the data to t-statistics. Differences between two samples were tested by the Mann-Whitney U-test or Wilcoxon paired test. For single tests, we report exact P values up to P = 0.001, but round them for smaller values. For multiple comparisons, we used a significance criterion of P < 0.05. In whisker plots, the boxes represent medians and 25 and 75 percentiles, whereas the dot and two whiskers represent the mean and SDs, respectively.

RESULTS

Data were obtained from 84 current-clamped and 14 single-unit neurons recorded from the SPON in vitro and in vivo, respectively. Mice used for in vitro experiments had an age range spanning P5–P20, covering a large part of the pre- and posthearing onset development of auditory brain stem neurons (Kandler and Friauf 1995; Magnusson et al. 2005; Scott et al. 2005; Chirila et al. 2007). To facilitate comparison between firing patterns elicited by intracellular current injections in vitro with sound-evoked SPON activity in vivo, single-unit recordings were performed in P20–P22 anesthetized mice.

SPON Neurons Exhibit Heterogenous Firing Patterns In Vitro

Typical firing patterns of single SPON neurons were recorded in brain slices by injecting depolarizing currents of increasing strength until the firing rates saturated. The magnitude of current injection needed to reach spike threshold was defined as the rheobase. Based on the qualitative appearance of the firing pattern, three groups were distinguished (Fig. 1A). One or a few spikes triggered by depolarization was the most commonly observed firing pattern (29 of 61 neurons, 48%). This type was termed “onset,” as it only responded with a sharp transient action potential marking the positive edge of the current injection. The “adapting” response type, which may or may not have an onset response at spike threshold but always fired more action potentials with increasing depolarization, was the second most commonly observed firing pattern (24 of 61 neurons, 39%). “Burst” neurons formed the third and least common firing pattern (8 of 61 neurons, 13%) and consisted of an onset action potential followed by a burst of spikelets forming a prolonged depolarizing positive edge. These neurons continued to fire regularly after the burst with increasing numbers of spikes accompanying each depolarizing step. To further distinguish the firing patterns of SPON neurons, their spike rates were plotted as a function of current strength, using 100-pA increments (Fig. 1B). Quantification of the slopes of the respective input-output functions confirmed the three neuronal categories and demonstrated that the response magnitude of adapting neurons was most dependent on stimulus strength (Fig. 1C). To examine if the three SPON firing patterns persist during auditory development (P5–P20), spike rate slopes were plotted against the postnatal day of investigation. For onset and burst neurons, the slopes of the spike rate versus stimulus strength relationships did not vary with age (Fig. 1D). However, the adapting neurons displayed significantly steeper spike rate slopes with age (r² = 0.28, P = 0.01; Fig. 1D), which indicates that these neurons might signal stimulus amplitude with their firing rate.

Fig. 1.

Superior paraolivary nucleus (SPON) neurons exhibit distinct firing patterns in response to depolarizing current injection. A: the “onset” response type (left) was the most commonly observed. These responses were marked by transient spiking triggered by the positive edge of the stimulus. The “adapting” response (middle) was characterized by an increase in spiking with increasing stimulus strength. The “burst” response type (right) consisted of an onset spike followed by a burst of spiklets and also produced higher spiking rates to increasing stimulus magnitude. B: spiking rates for individual SPON neurons were plotted as a function of stimulus strength above rheobase with mean response values shown as dashed lines for each group (n = 58). C: the slopes of the curves plotted in B revealed three distinct firing patterns, with adapting responses being most sensitive to stimulus strength (onset: n = 29, burst: n = 7, and adapting: n = 22). D: changes in spiking rates to increasing stimulus strength did not differ during the period surrounding hearing onset [P = 0.12 by Kruskal-Wallis test, n = 17, 25, and 16, for age groups of postnatal day (P)5–P8, P9–P11, and P12–P20, respectively]. *Statistical significance of pairwise comparisons (P < 0.05 by Tukey-Kramer test).

Distinct Firing Patterns Are Associated With Different Cellular Morphologies

A total of 21 (age range: P7–P14) physiologically characterized SPON neurons were filled with neurobiotin tracer to examine whether differences in spiking patterns were associated with distinct cell morphologies. To better visualize auditory brain stem nuclei, tissue was labeled with an antibody to VGAT, which marked the boundaries of the SPON (Fig. 2A). Eight of the twenty-one labeled neurons exhibited onset spiking patterns in response to depolarizing current injections and were recorded throughout the SPON (Fig. 2B). Likewise, six neurons with adapting spiking patterns had no preferred location within the nucleus (Fig. 2D). However, seven burst neurons were localized to the dorsolateral region of the SPON (Fig. 2F). A similar location dependency for neurons that exhibited postinhibitory rebound bursting to hyperpolarizing current injection has been previously reported in the SPON (Felix et al. 2011).

Fig. 2.

Localization and anatomic reconstructions of neurons recorded from the SPON in vitro. A: overview of the superior olivary complex showing the boundaries of the SPON (dashed line) and a neurobiotin-labeled neuron (black box, enlarged in the inset). Tissue was stained with an antibody to the vesicular GABA transporter to better visualize auditory brain stem nuclei. Neurons with onset firing patterns were recorded throughout the SPON (B), and three-dimensional reconstructions of labeled neurons revealed relatively simple dendritic arbors (C). Adapting neurons also showed no location dependency within the SPON (D) and had dendrites with values of dendritic length and branching that were intermediate compared with onset and burst cells (E; see Table 1). In contrast to the other cell types, burst neurons were restricted to the dorsolateral region of the SPON (F) and had complex dendritic arbors (G). The horizontal bars in A and G represent 200 and 50 μm, respectively. LSO, lateral superior olive; MNTB, medial nucleus of the trapezoid body; D, dorsal, M, medial.

To further characterize the relationship between firing patterns and cell morphology, three-dimensional reconstructions were created using the confocal image stacks of labeled SPON neurons (Fig. 2, C, E, and G). Based on these reconstructions, several morphological properties were measured, and the resulting values are shown in Table 1. Soma volume and sphericity were measured to quantify the size and shape of neuronal cell bodies. Although no differences were found between spiking types with respect to soma volume (P = 0.144 by Kruskal-Wallis test), soma sphericity differed between response types (P = 0.021). A closer examination revealed that burst cells were elongated compared with adapting cells (P < 0.05 by Tukey-Kramer test). In addition to somatic properties, the size and shape of dendritic arbors were measured, resulting in significant differences in the total length of dendrites (P = 0.037 by Kruskal-Wallis test) and number of branching points (P = 0.045 by Kruskal-Wallis test). Specifically, burst neurons had longer dendrites with a higher degree of branching compared with onset cells. There were no statistical differences in the number of dendritic end points across the groups (P = 0.069 by Kruskal-Wallis test), although burst cells had more end points on average compared with the other cell types, as shown in Table 1. The convex hull was used as a measure of how much each cell's dendrites extended within the tissue (as described in materials and methods). Convex hulls were not statistically different between groups (P = 0.222 by Kruskal-Wallis test), although burst neurons had a larger mean convex hull volume. Furthermore, no differences were found with respect to the shape (sphericity) of the convex hull (P = 0.968 by Kruskal-Wallis test).

Development of Fast Membrane Properties After Hearing Onset

Membrane properties were measured for SPON neurons to further examine the distinct response types (Fig. 3A). The resting membrane potential displayed significant variability between onset, burst, and adapting SPON neurons (P = 0.044 by Kruskal-Wallis test; Fig. 3B). However, neither input resistance (Fig. 3C) nor membrane time constant (Fig. 3D) varied between the different neuronal types in the depolarizing and hyperpolarizing ranges. When these parameters were compared in the two voltage ranges within the same neuronal category, input resistance in the depolarizing range was significantly lower than in the hyperpolarizing range in onset and adapting cells (P ≤ 0.01 by Wilcoxon paired test). The same trend was observed in burst cells, but the difference did not reach statistical significance (P = 0.46). In addition, membrane time constants were significantly shorter in the depolarizing range than in the hyperpolarizing range for onset and adapting cells (P ≤ 0.001 by Wilcoxon paired test), whereas burst neurons showed the opposite trend.

Fig. 3.

SPON neurons develop fast membrane properties after the onset of hearing. A: example voltage response of a SPON neuron to a depolarizing and hyperpolarizing current step (+100 and −100 pA, respectively) obtained in a P11 animal. The average membrane potential was computed over a 50-ms time window (thick black lines) before the current step and at the end of each current step to derive the input resistance. The membrane time constant (τ) was measured after the offset of the current step (inset) by fitting a single-exponential function to the voltage trace (black curves, inset). Kruskal-Wallis tests were performed to compare membrane properties across groups of response types (B–D) and groups of age (E–G). B: a significant difference was found among the resting membrane potentials (RMPs) of onset, adapting, and burst neurons (P = 0.044). However, both the input resistance (R_in; C) and τ (D) were equivalent across the groups (P ≥ 0.13). For both R_in and τ measurements, there was a difference between values tested in the depolarizing and hyperpolarizing ranges for onset and adapting neurons but not burst neurons. E: data from neurons exhibiting the three response types shown in B–D were pooled, and RMP was found to be constant during the periods of development corresponding to prehearing onset, hearing onset, and posthearing onset (P = 0.66). F: R_in decreased significantly with age, particularly for the depolarizing voltage range (P < 0.01 for both depolarizing and hyperpolarizing voltage ranges). G: a similar pattern was seen for τ, with values decreasing during development for the depolarizing range and, to a lesser extent, for the hyperpolarizing range (P < 0.01 for both depolarizing and hyperpolarizing voltage ranges). n values are indicated above each whisker bar. Black and gray whisker plots represent responses to depolarizing and hyperpolarizing current injection, respectively. Significant differences between depolarizing and hyperpolarizing voltage ranges are shown (*P < 0.05 and **P < 0.01 by Wilcoxon paired test).

Apart from membrane potential, basic membrane properties were indistinguishable between onset, adapting, and burst neurons when they were compared across groups (Fig. 3, C and D). Thus, data from all three response types, i.e., onset, adapting, and burst types, were pooled and further divided into the following three age groups to examine the developmental profile of SPON neurons: P5–P8, corresponding to the prehearing onset period; P9–P11, corresponding to the onset of hearing (Mikaelian and Ruben 1965); and P12-P20, corresponding to the posthearing onset condition. After this categorization, i.e., regrouping the data according to age, resting membrane potential remained stable during the developmental period surrounding hearing onset (Fig. 3E). In contrast, input resistance (Fig. 3F) and the membrane time constant (Fig. 3G), which both reflect the leakiness of the neurons, significantly decreased with age, with time constants reaching well below 5 ms (P < 0.001 by Kruskal-Wallis; Fig. 3G). In fact, the speed of the membrane time constants increased fourfold during the developmental period between pre- and posthearing conditions (P5–P9: 13.6 ms, n = 18; P9–P11: 6.3 ms, n = 29; and P12–P20: 3.1 ms, n = 15). Input resistance and membrane time constants estimated in the hyperpolarizing range also decreased significantly with age, reaching values around 5 ms (P < 0.001 by Kruskal-Wallis test; Fig. 3, F and G). Such membrane properties indicate that SPON neurons have the capacity to integrate their inputs on a fast timescale, comparable with other temporally precise neurons in the superior olivary complex (Sanes 1993; Kandler and Friauf 1995; Magnusson et al. 2005; Scott et al. 2005; Chirila et al. 2007).

Action Potential Speed and Shape Are Developmentally Regulated

Temporally precise spiking is a hallmark of SPON neurons (Kadner and Berrebi 2008; Felix et al. 2011); thus, the precision of the action potential is an important factor. From a qualitative point of view, it was apparent that the action potentials of SPON neurons sped up considerably during auditory development (Fig. 4A). A closer analysis of the kinetics of the action potentials revealed that their latencies at rheobase significantly decreased with age (r² = 0.08, P = 0.023; Fig. 4B). Furthermore, the action potentials became both faster, as evident from the increased maximal speed of the upstroke (r² = 0.28, P < 0.001; Fig. 4C), and briefer in their durations, measured as decreased half-width (r² = 0.46, P < 0.001; Fig. 4D), during development. Within 1 wk after hearing onset, the width of the action potentials reached values of ≤1 ms (Fig. 4, A and D). Spike threshold, defined as the voltage at which the first derivative of the membrane potential reached a value of 10 mV/ms, remained unchanged before and after hearing onset (r² = 0.003, P = 0.97; Fig. 4E). Likewise, the spike height remained constant during the observed developmental period (r² = 0.01, P = 0.44; Fig. 4F). The afterhyperpolarization, measured by comparing the deepest voltage of the repolarizing phase with respect to the spike threshold voltage, clearly became deeper with age (r² = 0.49, P < 0.001; Fig. 4G). A tendency for an afterdepolarization was sometimes noted in the youngest animals (P5), which we speculate might be related to an immature K⁺ channel profile. Because recordings were performed at room temperature (22°C) and as the kinetics of the action potentials might be temperature sensitive, a subset of recordings was performed at 36°C (P8–P14, n = 23). When Q₁₀ was estimated from age-matched controls, it generated a value of 1.2 for both the rise times and decay times of the action potentials, indicating that these responses were only marginally faster at physiological temperatures. Another observation was that all three response types were corroborated at the higher temperature and at a similar distribution to the neurons recorded at room temperature (data not shown).

Fig. 4.

Action potential speed increases during development for SPON neurons. A: a qualitative view of superimposed action potentials at P15, P10, and P7 revealed a sharpening of spike timing with age. The first spike latencies (B) decreased with age. Action potentials became faster during development, as evidenced by an increase in the maximum speed of the spike upstroke (C) and a reduced spike half-width (D). The spike threshold (E) and spike height (F) did not change with age. The magnitude of the afterhyperpolarization increased significantly during development (G). The index of determination (r²) and significance of the correlation (P value) are indicated on the top of B–G. n = 61.

We next investigated whether the shape of the action potentials was developmentally regulated. Phase-plane plots using the first derivative of the membrane voltage as a function of membrane potential allowed a closer analysis of the spike shape and its initiation. Representative examples of phase-plane plots before (Fig. 5A) and after (Fig. 5B) hearing onset confirmed that the action potential indeed gets faster and narrower with age. The inflection rate, estimated from the membrane potential slope around spike threshold, was much slower for the P18 SPON neuron than for a neuron 10 days younger (Figs. 5, C and D). Population analysis revealed a fourfold decrease of the inflection rate during this developmental period (P5–P8: 11.6 ± 3.3 mV/ms, n = 16; P9–P11: 8.3 ± 5.2 mV/ms, n = 22; and P12–P20: 3.7 ± 3.6 mV/ms, n = 13; P < 0.001 by Kruskal-Wallis test; Fig. 5E). Interestingly, the inflection rate was positively correlated with the input resistance of the neuron (Fig. 5F).

Fig. 5.

Action potential shape is developmentally regulated in the SPON. Phase-plane plots showing the first derivative of the somatic membrane voltage (dV/dt) versus membrane voltage demonstrated that action potentials before the hearing onset are not as fast or narrow (A) compared with those after the hearing onset (B; arrows indicate spike threshold). The inflection rate at spike initiation before the hearing onset (C) was higher than after the hearing onset (P = 0.0001 by Kruskal-Wallis test, n = 16, 23, and 13, respectively, in age groups; D and E). Inflection rate was estimated using the three points nearest spike threshold (gray dots; see materials and methods). F: overall, spike inflection rate was positively correlated with R_in. *Significant difference from multiple comparisons (P < 0.05 by Tukey-Kramer test).

Development of Onset and Rebound Spiking

SPON neurons are equipped with a rebound spiking mechanism that causes action potential production upon a preceding hyperpolarization regardless of the spiking response type and age (P5–P20) of the animal (Felix et al. 2011; Kopp-Scheinpflug et al. 2011). To compare the precision of onset versus offset spiking, temporal characteristics of the respective spiking response components were compared at rheobase during the development of hearing (Fig. 6A). The first spike latencies of onset and rebound spikes became significantly shorter with age (P = 0.011 and P < 0.001, respectively, by Kruskal-Wallis test). It is noteworthy that the onset spikes had consistently shorter latencies compared with rebound spiking (P < 0.001, P < 0.001, and P = 0.017 for age groups P5–P8, P9–P11, and P12–P20, respectively, by Mann-Whitney U-test; Fig. 6B). Otherwise, the speed (Fig. 6C) and brevity (Fig. 6D) of the onset versus rebound action potentials developed in parallel, making both types of spikes equally sharp after hearing onset (P = 0.6 and P = 0.79, respectively, by Mann-Whitney U-test). In addition, voltage threshold did not change with auditory development for either onset or rebound spikes (P = 0.37 and P = 0.22, respectively, by Kruskal-Wallis test; Fig. 6E). However, rebound spikes had a tendency to have lower thresholds than onset spikes (Fig. 6E), particularly at values of ±200 pA from the rheobase value (pooled data across ages: at rheobase, onset −35.8 ± 5.9 vs. offset −39.3 ± 6.9, P = 0.013, n = 61; at rheobase ± 200 pA, onset −38.0 ± 6.2 vs. offset −45.0 ± 6.5, P < 0.001, n = 38; by Mann-Whitney U-test). Moreover, spike height remained equally large for both onset and rebound spiking at all ages studied (P = 0.32 and P = 0.30, respectively, by Mann-Whitney U-test; Fig. 6F). Finally, the afterhyperpolarization, which may contribute to the duration and interspike interval of the corresponding spiking response in vivo, became deeper for both onset and rebound spiking with age (P < 0.001 by Kruskal-Wallis test; Fig. 6G), displaying the largest effect after hearing onset.

Fig. 6.

Properties of onset and offset spiking responses in the SPON in vitro. A: voltage response to hyperpolarizing (gray) and depolarizing (black) current steps (−100 and 200 pA, respectively) for one SPON neuron obtained in a P12 animal. Insets show enlarged versions of the onset and offset spikes with the analyzed parameters indicated. B–G: whisker plots showing onset (black) and offset (gray) spike parameters as a function of age groups for spike latency (B), maximal speed of the membrane voltage change during the spike (C), spike half-width (D), spike threshold (E), spike height (F), and membrane afterpotential (G). Significant differences across age groups according to the Kruskal-Wallis test were detected for spike latency (P = 0.011 for onset spikes and P < 0.001 for offset spikes; B), for the maximal speed of the voltage change, spike half-width, and spike afterpotential (P < 0.001 for both onset and offset spikes; C, D, and G). Neither spike threshold nor spike height changed across development (P > 0.2 for both onset and offset spikes; E and F). n values indicated on the top of the whisker bars in D apply to B–F. Significant differences between onset and offset spike parameters are shown (*P < 0.05 and **P < 0.01 by Mann Whitney U-test).

SPON Neurons Respond to Sound Transients With High Precision In Vivo

The most prominent and consistent response pattern of SPON neurons in vitro is sharp onset spiking upon depolarization and sharp rebound spiking upon hyperpolarization. To investigate how the spiking properties of SPON neurons in vitro are reflected in the response properties in vivo, we performed single-unit recordings in anesthetized mice. All 14 neurons, anatomically verified to locations in the SPON from mice aged P20–P22 (Fig. 7A), responded with onset spiking to a pure tone stimulus, in addition to the typical offset response at the termination of the sound stimulation (Fig. 7B). To shed light on the possible origin and role of the onset response, this component was characterized and compared with the offset response. Measurements were compared between response components within the same cells. The CF of the onset response (19.9 ± 2.7 kHz) was, on average, lower than the CF for the offset response (25.5 ± 3.1 kHz, P = 0.013 by Wilcoxon paired test; Fig. 7C). In addition, the average threshold was higher for the onset response (53.8 ± 3.3 dB sound pressure level) compared with the offset response (47.2 ± 2.8 dB sound pressure level, P = 0.003; Fig. 7C). For measures of temporal precision, the onset response measured from the start of the stimulus had a shorter first spike latency (4.4 ± 0.3 ms) and lower jitter (1.0 ± 0.1 ms) compared with the offset response measured from the termination of the stimulus (6.2 ± 0.3 and 1.2 ± 0.1 ms, respectively, P = 0.002 and 0.048; Fig. 7D).

Fig. 7.

Properties of onset and offset spiking responses in the SPON in vivo. A: biocytin was deposited in the SPON to mark the recording site. B: SPON responses exhibited prominent spiking triggered by the onset of tone stimuli, in addition to the typical offset response. C: onset spiking responses had lower characteristic frequencies and higher thresholds compared with offset responses measured for the same cells. D: the onset spiking component had lower first-spike latencies and jitter compared with offset responses. L, lateral. The length of the arrows in A represent 100 μm, and the border of the SPON is marked with a dashed line. The black bar in B represents the tone stimulus within the recording window. Significant differences in onset and offset spike parameters are shown (*P < 0.05 and **P < 0.01 by Mann Whitney U-test).

DISCUSSION

The present study provides the first characterization of the development of intrinsic electrical properties of SPON neurons. The results also suggest distinct types of neurons based on spiking patterns, morphological features, and the location of cells within the nucleus. Despite these differences, on-off spiking was commonly observed for all cell types after either electrical stimulation in vitro or tone stimulation in vivo. In addition, on-off spiking mechanisms driven by depolarization and hyperpolarization, respectively, develop equally fast response kinetics after the onset of hearing. These results support the notion that the SPON may be involved in detecting sharp transitions of acoustic energy that can be cues for communication perception.

Are There Different Subpopulations of SPON Neurons?

SPON neurons display distinct firing patterns upon depolarization. Similar variability in spiking responses has been documented in other auditory nuclei [the cochlear nucleus (Oertel 1983; Rothman and Manis 2003a), lateral superior olive (Adam et al. 2001; Barnes-Davies et al. 2004), and IC (Koch and Grothe 2003)]. Modeling studies have suggested that such spiking variability can be explained by a graded amount of the low voltage-activated K⁺ current (I_KLT) in combination with the hyperpolarization current (I_h) that tunes the I_KLT into the optimal voltage range (Manis and Marx 1991; Rothman and Manis 2003a, 2003b). Therefore, subtle variations in the abundance of similar types of currents can generate qualitatively distinct spiking patterns in a population of functionally related neurons, which may be the case with onset and adapting response types. It would be interesting to measure the specific types of K⁺ conductances in SPON neurons and investigate their relationship to the firing properties and cellular morphology. If these neurons form a continuum with respect to their physiological response properties, such distributed spiking patterns might be advantageous for obviating redundancy and thus achieving more efficient information coding in auditory pathways (Typlt et al. 2012). In contrast, burst neurons in the dorsolateral quadrant of the nucleus seem to represent a distinct subclass of SPON neurons. First, they exhibit a unique firing pattern, which seems to correlate with a higher expression of low voltage-activated Ca²⁺ currents (Felix et al. 2011) than in onset and adapting cells. Whether burst neurons represent a transitional developmental phase or if they persist throughout development remains to be confirmed in adult animals. Second, at the ages investigated here, burst neurons possessed longer and more complex dendrites than onset and adapting cells. Presumably, the more complex dendritic tree of SPON burst neurons provides them the means to integrate a wider range of synaptic inputs, which potentially make them suitable to fulfil different functions than other SPON neurons. However, future studies will have to confirm the morphological profile of the physiologically classified SPON neurons in older animals, as it has been demonstrated that alterations of the dendritic tree can take place up to ∼P27 in medial superior olivary (MSO) neurons in gerbils (Rautenberg et al. 2009). We also acknowledge that portions of some distal dendrites may have been cut during slicing and thus not included in our analysis, but we assume that only part of the dendritic arbor would be omitted and that this issue would affect all neuron types. It is currently not known if the various synaptic inputs to the SPON terminate differentially within the nucleus, nor is it known how excitatory and inhibitory synaptic terminals distribute onto individual SPON neurons. In this context, it would be useful to know if SPON neurons follow patterns of synaptic organization seen in other auditory neurons, such as the distribution of excitatory terminals impinging on the distal dendrites and inhibitory terminals impinging on the proximal dendrites and somata demonstrated in MSO neurons (Kapfer et al. 2002).

Maturation of Intrinsic Membrane Properties of SPON Neurons

Although auditory brain stem development has been studied in detail (for reviews, see, e.g., Hoffpauir et al. 2009 and Kandler et al. 2009), the SPON has received relatively little attention. To our knowledge, this is the first study that focuses on the intrinsic properties of SPON neurons. The data show that, similar to other neurons in the auditory brain stem (Sanes 1993; Kandler and Friauf 1995; Adam et al. 2001; Magnusson et al. 2005; Scott et al. 2005; Chirila et al. 2007; Hassfurth et al. 2009), SPON neurons undergo strong developmental regulation of their electrical properties over the first 3 postnatal weeks, becoming more temporally precise.

The input resistance in the depolarizing range underwent significant reduction with age and thereby shortened the membrane time constants by a factor of four during the same developmental time period, reaching values on the order of 3 ms. This indicates that SPON neurons are well suited for encoding temporal features of sound on the order of 10s of milliseconds. Such low-rate temporal information is at least partially extracted in lower areas of the brain (Deutscher et al. 2006) and is essential for auditory scene analysis (Shamma et al. 2011).

Membrane time constants in the hyperpolarizing voltage range also decreased with development, reaching values on the order of 5 ms. This acceleration of the membrane time constant could be related to a developmental upregulation of I_h, similar to what has been demonstrated in the lateral superior olive (Hassfurth et al. 2009). Several studies have indicated that the relative HCN subunit composition changes during development, resulting in higher expression of the faster HCN1 subunit at the expense of the other slower isoforms (Vasilyev and Barish 2002; Surges et al. 2006). Because SPON neurons strongly express the HCN1 subunit in adult animals (Koch et al. 2004; Felix et al. 2011; Kopp-Scheinpflug et al. 2011), it is possible that HCN subunit plasticity takes place in these neurons during early auditory development. Functionally, the fast membrane time constant in the hyperpolarizing range would attenuate temporal summation of the inhibitory inputs from the medial nucleus of the trapezoid body (Moore and Caspary 1983; Sommer at al. 1993; Smith et al. 1998; Behrend et al. 2002), which drive the sharp offset response to tones in the SPON in vivo (Kulesza et al. 2007).

Development of Fast Action Potentials in SPON Neurons

Action potentials triggered in SPON neurons speed up significantly after hearing onset. This property is reflected in the extremely fast rise and fall of the membrane potential, giving rise to very narrow action potentials. The sharp, high-amplitude action potentials recorded in the SPON are in contrast with the broad and shallow action potentials that develop over the same time period in the MSO (Grothe and Sanes 1993; Scott et al. 2005, 2007). This difference may be related to the fact that MSO neurons are specialized coincidence detectors that must preserve precisely timed synaptic inputs (Scott et al. 2007, 2010; Mathews et al. 2010). For instance, MSO neurons display extreme electrical segregation of their somata and dendrites from the initiation axon segments, which supports robust high-frequency axonal signaling (Scott et al. 2007). In contrast, the large, somatic action potentials of SPON neurons suggest a less rigid electrical compartmentalization compared with MSO neurons. If so, the spread of the action potential to the somata and the vast dendritic trees of SPON neurons (present study; Schofield 1991; Saldaña and Berrebi 2000) in combination with somewhat more relaxed membrane time constants than the adjacent binaural nuclei suggest a different function for the SPON than that of an extremely fast neural integrator. We speculate that the intrinsic membrane properties of SPON neurons give them the capacity for fast and robust electrical signaling, albeit tuned to encode slower temporal events than neurons involved in binaural processing. This notion is supported by intrinsic tuning to low-frequency (<50 Hz) modulated sinusoidal currents reported in SPON neurons in vitro (Felix et al. 2011), in addition to an upper limit of responses to modulation rates around 200 Hz for sinusoidally amplitude modulated tones in vivo (Grothe 1994; Kuwada and Batra 1999; Kulesza et al. 2003; Kadner and Berrebi 2008; Felix et al. 2012). Presumably, the large dendritic trees of SPON neurons and the distribution and density of their voltage-operated channels play an important role in determining the intrinsic frequency-following capacity of these neurons.

The development of fast, brief action potentials is paralleled by changes in the shape of the action potentials, such as a more shallow inflection rate and an increase of the afterhyperpolarization after hearing onset. A developmental increase in K⁺ currents could provide an explanation for these changes. For instance, low voltage-activated K_v1.1 current has been demonstrated in the SPON (Kopp-Scheinpflug et al. 2011). This K⁺ current is partially activated at rest in other auditory nuclei (Brew and Forsythe 1995; Bal and Oertel 2001; Rothman and Manis 2003a), in which it promotes fast, strong, or coincident excitation to trigger spiking (Svirskis et al. 2002, 2004; Rothman and Manis 2003b; Scott et al. 2005). A developmental upregulation of K_v1.1 currents might shunt the spike initiation and thus result in shallower inflection rates with age. A correlation between the inflection rate and input resistance of SPON neurons in the depolarizing range further implies that one or more ion currents may have been upregulated. An alternative explanation for the strong reduction of the inflection rates is that the axon initial segment undergoes a dynamic relocation (Grubb and Burrone 2010) during auditory development, possibly guided by auditory experience (Kuba 2012). If so, a more shallow inflection rate might indicate that the action potential is generated farther from the soma (Shu et al. 2007) in older SPON neurons. The pronounced increase in afterhyperpolarization after hearing onset could be related to more rapid Na⁺ channel inactivation or larger K⁺ currents or both (Bean 2007).

Onset Versus Offset Spiking

In vitro, SPON neurons develop equally high and narrow action potentials triggered by respective depolarization and hyperpolarization. However, their rebound spikes have a longer latency and lower threshold than their onset spikes. This could be explained by the interplay between I_h and low voltage-activated K⁺ and Ca²⁺ currents in these cells. For example, a hyperpolarization would activate I_h in the SPON (Felix et al. 2011; Kopp-Scheinpflug et al. 2011), which presumably lowers membrane time constants and enables these neurons to integrate their inputs with higher resolution. Another very important function of I_h is presumably to set the membrane voltage in an optimal working range for the low voltage-activated Ca²⁺ current, which also contributes to the rebound spiking mechanism in SPON neurons (Felix et al. 2011; Kopp-Scheinpflug et al. 2011). This Ca²⁺ current is active over several tenths of milliseconds within a limited voltage range, during which the membrane voltage reaches spike threshold (Felix et al. 2011; Kopp-Scheinpflug et al. 2011). It is thus possible that the longer latency of the rebound spike in SPON neurons may be correlated to activation kinetics of low voltage-activated Ca²⁺ channels. A preceding hyperpolarization might also lower the threshold by deinactivating a larger fraction of Na⁺ channels (Svirskis et al. 2004; Platkiewicz and Brette 2010) or by deactivating low voltage-activated K⁺ channels, which have been shown to increase spike threshold in cortical neurons (Bekkers and Delaney 2001).

SPON neurons also respond with robust and precise onset spiking to tones in vivo. Previous studies in the SPON have highlighted the offset spiking in response to tones (Kulesza et al. 2003, 2007; Kadner et al. 2006; Kadner and Berrebi 2008; Felix et al. 2012). The onset spiking response has been commented on previously (see Behrend et al. 2002; Grothe 1994; Kulesza et al. 2003) but has been largely overlooked. This oversight is likely due to the fact that onset responses typically have a different CF and a higher threshold than the offset response in the same cell. If recordings are focused on the CF of the offset response and conducted 10–20 dB above the threshold of the offset response, the magnitude of an onset spiking component would easily be underestimated.

One critical question is where the excitatory drive for the SPON onset response originates. There is evidence for an excitatory input to the SPON from the contralateral cochlear nucleus (Friauf and Ostwald 1988; Kuwabara et al. 1991; Thompson and Thompson 1991; Schofield 1995; Saldaña et al. 2009) and tentative evidence for a specific input from the specialized octopus neurons (Friauf and Ostwald 1988; Schofield 1995; Saldaña et al. 2009). The responses of octopus neurons are triggered by the onsets of sounds (Godfrey et al. 1975; Rhode and Smith 1986) and exhibit extremely precise spike timing. This exquisite precision is conferred by input from a large number of auditory nerve fibers that represent a wide range of frequencies and fire in synchrony (Golding et al. 1995; Oertel et al. 2000). Octopus neurons are capable of encoding transient events of complex periodic stimuli and have been implicated in the processing of vocalization cues important for communication perception (Oertel et al. 2000). In the present study, the fact that SPON onset responses had higher thresholds (Oertel et al. 2000), shorter and more consistent spike latencies (Nayagam et al. 2005), and a different frequency tuning than the offset responses is compatible with a well-timed octopus cell input to the SPON.

Possible Roles for SPON Neurons in Acoustic Processing

This study strongly implies that the SPON has the capacity to signal sharp sound transients or fluctuations in both the positive and negative directions. One lingering question is how this information would affect subsequent levels of central auditory processing. The SPON provides a major source of GABAergic inhibition to the IC, a large midbrain center that integrates multiple inputs from subcollicular centers. Inhibition has been shown to play an important role for the response selectivity of IC neurons to complex and behaviorally salient acoustic stimuli varying in both the spectral and temporal domains (e.g., Suga 1968; Fuzessary and Hall 1996; Koch and Grothe 1998; Klug et al. 2002; Nataraj and Wenstrup 2006; Andoni et al. 2007). Interestingly, it has recently been demonstrated that the IC systematically represents the temporal dimension of sounds with a periodotopic map that is perpendicularly organized to the classical tonotopic map (Baumann et al. 2011). It seems safe to assume that at least some aspects of the temporal sound code related to periodicity are inherited from the auditory brain stem (Palombi and Caspary 1996; Koch and Grothe 1998; Backoff et al. 1999; Zhang and Kelly 2003; Caspary et al. 2002). The SPON is a prime candidate for conveying such temporal information to the IC, as it provides one of the major ascending inputs (Kelly et al. 1998; Saldaña and Berrebi 2000; Saldaña et al. 2009). Inhibition in the IC also contributes to more complex processing of acoustic signals, such as computing the difference in temporal envelopes across frequency (Li et al. 2006), forward masking of sound amplitude modulations (Nelson et al. 2009), and discrimination of species-specific communication calls (Klug et al. 2002), all of which are important mechanisms for behaviorally relevant sounds. Future studies will examine the relative contribution of onset and offset responses in SPON neurons during more complex acoustic stimuli with high behavioral salience for mice and other mammals.

GRANTS

This work was supported by Swedish Research Council Grant 80326601, Jeanssons Stiftelse, Hörselskadades Riksförbund, Tysta Skolan, Karolinska Institutets fonder, the Wenner-Gren Foundation, German Research Foundation Grant VO 1980/1-1, and National Institute on Deafness and Other Communication Disorders Grant RO1 DC-002266 (to A. S. Berrebi).

DISCLOSURES

No conflicts of interest, financial or otherwise, are declared by the author(s).

AUTHOR CONTRIBUTIONS

Author contributions: R.A.F. and A.K.M. performed experiments; R.A.F., K.V., and A.K.M. analyzed data; R.A.F., K.V., and A.K.M. interpreted results of experiments; R.A.F., K.V., and A.K.M. prepared figures; R.A.F., K.V., and A.K.M. drafted manuscript; R.A.F., K.V., A.S.B., and A.K.M. edited and revised manuscript; R.A.F., K.V., A.S.B., and A.K.M. approved final version of manuscript; A.S.B. and A.K.M. conception and design of research.