Articles, Behavioral/Systems/Cognitive

Adaptation of Firing Rate and Spike-Timing Precision in the Avian Cochlear Nucleus

Marina S. Kuznetsova, Matthew H. Higgs and William J. Spain
Journal of Neuroscience 12 November 2008, 28 (46) 11906-11915; https://doi.org/10.1523/JNEUROSCI.3827-08.2008

Abstract

Adaptation is commonly defined as a decrease in response to a constant stimulus. In the auditory system such adaptation is seen at multiple levels. However, the first-order central neurons of the interaural time difference detection circuit encode information in the timing of spikes rather than the overall firing rate. We investigated adaptation during in vitro whole-cell recordings from chick nucleus magnocellularis neurons. Injection of noisy, depolarizing current caused an increase in firing rate and a decrease in spike time precision that developed over ∼20 s. This adaptation depends on sustained depolarization, is independent of firing, and is eliminated by α-dendrotoxin (0.1 μm), implicating slow inactivation of low-threshold voltage-activated K+ channels as its mechanism. This process may alter both firing rate and spike-timing precision of phase-locked inputs to coincidence detector neurons in nucleus laminaris and thereby adjust the precision of sound localization.

Introduction

Sensory systems use adaptation to adjust to changes in environmental conditions (Laughlin, 1989; Fettiplace and Ricci, 2003). Adaptation is commonly seen as a decrease in response to a constant stimulus and is thought to accentuate time-varying input while attenuating static background values. Adaptation is ubiquitous in the auditory system and specifically in the sound localization pathway. In psychophysics, adaptation manifests as a shift in perceived stimulus position (Wright, 1960; Grantham, 1992; Kashino and Nishida, 1998; Carlile et al., 2001; Getzmann, 2004; Phillips and Hall, 2005). In vivo recordings have shown adaptation as a decrease in firing rate over time in the eighth nerve (Javel, 1996; Avissar et al., 2007), and in interaural time difference (ITD)-sensitive neurons in the inferior colliculus (Ingham and McAlpine, 2004), and auditory cortex (Malone et al., 2002).

Although firing rate adaptation has been observed in these neurons, information carried by the monaural ITD pathway in the avian brainstem, before binaural convergence, is thought to be encoded primarily in the timing of action potentials rather than the mean rate. Thus, we hypothesized that the most effective means of adaptation in the monaural pathway might be through a mechanism that reduces spike-timing precision. The present study identifies such a mechanism.

ITDs arise from the time taken by sound to travel between the two ears and are used for localization of low-frequency sounds in the horizontal plane. The avian cochlear nucleus, the nucleus magnocellularis (NM), contains the first-order central neurons for ITD processing. NM neurons receive monaural excitatory inputs phase-locked to the auditory tone from the eighth nerve (Parks and Rubel, 1978). NM neurons are highly specialized for rapid and precise firing (Hackett et al., 1982; Raman and Trussell, 1992; Reyes et al., 1994; Koyano et al., 1996). A major current that defines the firing behavior of NM neurons is low-threshold voltage-gated potassium current (IKlt) (Reyes et al., 1994; Fukui and Ohmori, 2004). The output of NM transmits stimulus phase information, encoded in the timing of spikes, bilaterally to nucleus laminaris (NL) (Young and Rubel, 1983; Sullivan and Konishi, 1984; Carr and Konishi, 1990). Coincidence detector neurons in NL convert this information into a place code of horizontal sound location. The monaural ITD pathway is designed to faithfully preserve spike timing relative to the stimulus phase, because NL neurons function with a time resolution of microseconds (Carr and Konishi, 1990).

We investigated adaptation during in vitro whole-cell recording in NM. Repetitive firing was elicited by injected Gaussian noise with a DC offset. NM neurons showed multiple processes of adaptation occurring on different timescales. The present study investigated the process that develops between 1 and 20 s of constant stimulation, which is manifested as an increase in firing rate and a decrease in spike-timing precision. We determined that slow inactivation of IKlt underlies this unique form of adaptation. Finally, we used the dynamic current clamp to demonstrate that the adaptation occurs when physiological inputs are simulated.

Materials and Methods

Brain slice preparation.

Chick brainstem slices were prepared as described previously (Slee et al., 2005). Chicks [embryonic stage (embryonic day 21) or posthatch stage (postnatal day 1)] were decapitated rapidly, and a 1 cm section of the skull containing the brainstem was removed with a razor blade and immersed in ice-cold artificial CSF (ACSF) composed of the following (in mm): 130 NaCl, 3 KCl, 2 CaCl2, 1.25 NaH2PO4, 26 NaHCO3, 2 MgCl2, and 10 dextrose, kept at pH 7.3–7.4 by bubbling with carbogen (5% CO2, 95% O2). A 10 mm transverse section of the brainstem containing NM and NL was dissected and attached to a vibratome. Coronal sections (200 μm thick) were cut, transferred to a holding chamber filled with ACSF, and kept at 35°C for 30 min and room temperature thereafter. All experiments were performed at 33–35°C in ACSF supplemented with 100 μm picrotoxin to block GABAA receptors and either 20 μm DNQX or 10 μm CNQX to block AMPA/kainite receptors.

In vitro electrophysiology.

Standard techniques were used to obtain whole-cell recordings (Reyes et al., 1994; Slee et al., 2005) under visual control using infrared differential interference contrast optics. Patch pipettes (2–4 MΩ) were pulled from borosilicate glass and coated with Sylgard. Patch pipette solution used for the majority of the recordings was composed of the following (in mm): 105 K-gluconate, 2.5 MgCl2, 31.15 KCl, 0.05 CaCl2, 10 HEPES, 2 Na2ATP, 0.5 Na-GTP, 0.1 EGTA, and 0.5–1% biocytin, pH adjusted to 7.2–7.3 with KOH, 280 mOsm. The responses of NM neurons did not show significant differences between slightly different pipette solutions and were pooled for analysis. The data were corrected for the measured liquid junction potential of 6 mV. Current-clamp recording was performed using an Axoclamp 2B or Multiclamp 800A amplifier in bridge mode. Voltage-clamp and hybrid-clamp recordings were done with the Multiclamp 800A. Membrane potential and injected current were low-pass filtered at 5–30 kHz (eight-pole Bessel filter) and sampled at 10–100 kHz using an InstruTECH ITC-16 data acquisition board connected to a Macintosh computer. Current and voltage commands and data acquisition were controlled by custom macros written in Igor Pro. Noisy current stimuli were generated by custom macros written in Igor Pro and were generated by convolving Gaussian white noise with a decaying exponential (time constant, 0.1 ms).

Dynamic current clamp.

We previously developed methods for intracellular stimulation of neurons with an input waveform that simulates the summation of excitatory synaptic conductances (EPSGs) from a variable number of presynaptic neurons (Reyes et al., 1996). Custom macros written in Igor Pro were used to simulate the firing patterns of the eighth nerve fibers during an acoustic stimulus with six independent parameters: (1) sound frequency (F); (2) number of presynaptic cells (N); (3) EPSG time course (τ); (4) peak size (Gmax) of each unitary synaptic conductance, where G(t) = Gmax(t/τ)e(1 − t/τ); (5) average firing rate of each presynaptic cell (which determines the probability that a given presynaptic cell will fire on a cycle of the sound wave); and (6) input jitter, the SD of a Gaussian random variable that determines when a presynaptic cell fires during each cycle of the sound wave. In some experiments, synaptic depression was added to the simulated conductance train. Synaptic depression was calculated by the following formula: Embedded Image where A is the peak conductance of the synaptic event, D is the fast fraction of the recovery process, Δt is the interval between the previous and current synaptic events, τf is the fast time constant of recovery, A0 is the peak conductance of a previous synaptic event, S is the fraction of the available transmitter released, and τs is the slow time constant of recovery (Tsodyks and Markram, 1997; Cook et al., 2003). Parameter values were chosen to match published data (Brenowitz and Trussell, 2001) (D = 0.6; S = 0.45; τf = 20 ms; τs = 350 ms). The reversal potential of the synaptic current was set by a custom-made analog conductance clamp circuit, which converted the conductance command into a current command [Icommand = Gcommand(Erev − Vm)]. The resulting current was then injected into the cell by the Axoclamp 2B amplifier operating in bridge mode.

Hybrid clamp.

The hybrid voltage-clamp/current-clamp technique allows continuous recording from a neuron while the recording mode is switched between current and voltage clamp (see Fig. 6). This technique takes advantage of the dual-circuit headstage of the Multiclamp 800A. Briefly, a whole-cell recording was established as described above. Current and voltage command waveforms were generated using custom macros written in Igor Pro and added together. A mode control waveform, also consisting of a continuous voltage signal, was also generated. The stimulus and mode commands were sent to the amplifier, which applied the stimulus (voltage clamp/current clamp) waveform to the cell and recorded the current or voltage as selected by the mode command. The switch of the headstage circuit between the recording modes was not instantaneous, taking ∼50 ms. Artifacts generated by the amplifier during the mode switch did not appear to alter the stability of recording and were ignored. The data, comprised of alternating sections of current and voltage data, were filtered and digitized as described above and analyzed using custom macros in Igor Pro.

Results

Whole-cell patch-clamp recordings were obtained from cells located in the caudal two-thirds of NM; this portion of the nucleus contains neurons with low to middle characteristic frequencies (∼500–2000 Hz) (Rubel and Parks, 1975). Cells were accepted for analysis if the resting membrane potential was more negative than −55 mV.

NM neurons increase firing rate in response to noisy depolarizing current

To investigate the possibility of spike frequency adaptation in NM neurons, voltage responses to depolarizing Gaussian current injections were recorded in whole-cell current-clamp mode (Fig. 1). Noise stimuli were chosen over other stimulus types such as steps and pulses because long steps elicit only a single onset spike in these neurons, whereas the effect of adaptation during trains of pulses may be discontinuous and depend critically on the pulse amplitude. In contrast, noise stimuli provide a simple means to elicit probabilistic firing, the rate of which is sensitive to most adaptation mechanisms. The mean stimulus current was set to 50–250 pA above the rheobase (defined as the minimum current step that elicited an onset spike in the absence of noise), allowing noise-induced membrane potential fluctuations to frequently reach spike threshold. The SD of the noise was set at 0.5 times the stimulus mean. NM neurons responded to this type of stimulus with a high initial firing rate that rapidly decreased (τ = 27 ± 5 ms, based on fitting a single exponential over the first 600 ms of the response; n = 12) (Fig. 1C, inset). This onset response was followed by a slower increase in firing rate that reached a maximum between 10 and 20 s after stimulus onset (Fig. 1C). In many neurons, this was followed by an even slower decrease in firing rate.

Figure 1.
Figure 1.

NM neurons increase firing rate in response to depolarizing, fluctuating inputs. A, Schematic diagram showing the circuitry of the first-order (NM) and second-order (NL) CNS neurons in the chick auditory system and the in vitro recording location. B, Voltage response of an NM neuron to a depolarizing, noisy current injection. C, PSTH (black trace; left axis) and average membrane voltage (gray trace; right axis) of a response to a 20-s-long, depolarizing noisy current injection averaged over 250 ms bins. The inset shows the PSTH of the initial 600 ms of the response averaged over 50 ms bins. The solid black lines are exponential fits to the rapid decrease (inset plot) and slow increase in firing rate.

We observed that the mean membrane potential rose by an average of 4.9 mV (±1.5 mV; n = 16) along with the firing rate between 0 and 20 s after the stimulus onset (Fig. 1C). In most cells that showed the late, slow decrease in firing rate, the membrane potential continued to rise thereafter, suggesting that the rate decrease may have resulted from sodium channel inactivation under these experimental conditions.

The time courses of the rate increase and the rate decrease were calculated by fitting the initial 200 s of the firing rate histogram with a sum of rising and falling exponentials, obtaining time constants of 11 ± 3 s (N = 8) and 105 ± 20 s (N = 6; in the other two cells, the rate decrease was not observed). In subsequent experiments, we used 5-s-long noise repeats, which were sufficient to initiate the rate increase but not the slower rate decrease. The rate increase was observed in 30 of 30 neurons tested with such stimuli.

The firing rate increase depends on depolarization and does not require spiking

To determine how adaptation depends on the properties of the stimulus, we injected NM neurons with noisy current with different mean levels and SDs. We found that the rate increase occurred when the stimulus had a positive mean (Fig. 2A) and did not occur during a noisy current injection with a zero mean (Fig. 2B). Increasing the SD of the zero-mean stimulus increased the initial firing rate but did not elicit a rate increase over time (n = 11) (data not shown). The rate increase was independent of the initial firing rate, which ranged from 9 to 130 Hz for positive-mean stimuli, and from 12 to 183 Hz for zero-mean stimuli. These results suggested that the rate increase was independent of spiking.

Figure 2.
Figure 2.

The rate increase requires sustained depolarization and is independent of firing. A, PSTH (top) and voltage response (middle) of an NM neuron to a 5-s-long current step containing noise plus a depolarizing DC component to give a positive mean (bottom). B, PSTH and voltage response of the same neuron to a 5-s-long noisy input with zero mean. C, PSTH and voltage response of an NM neuron to a 5-s-long depolarizing current injection with noise during the first and last seconds only; note the absence of firing in the absence of noise. D, Change in mean firing rate between 0 and 1, and 4 and 5 s after the stimulus onset for a positive-mean stimulus (n = 30), zero-mean stimulus (n = 16), and a positive-mean “no noise” stimulus (n = 13). Error bars represent SE (z test, *p < 0.01).

To test this hypothesis more rigorously, we took advantage of the DC filtering properties of NM neurons, using a stimulus consisting of a 5-s-long depolarizing step with noise only during the first and last second. The amount of adaptation was measured as the difference in firing rate between the final and initial noisy periods; firing was absent during the noiseless depolarization. We found that depolarizing current without noise reliably induced an increase in excitability in the absence of firing (Fig. 2C).

IKlt underlies the slow adaptation

The properties of the rate increase, especially the requirement for depolarization and the independence from firing, suggested that a subthreshold voltage-gated current may cause this adaptation. NM neurons have a very prominent low-threshold K+ current (IKlt), which shows slow inactivation during depolarizing voltage steps (Reyes et al., 1994; Rothman and Manis, 2003a). The time course of the slow inactivation is similar to that of the rate increase.

To directly test whether IKlt causes the rate increase, we blocked the ion channels responsible for this current by applying 0.1 μm α-dendrotoxin (α-DTX), which is a selective blocker of K+ channels comprised of Kv1.1, Kv1.2, and Kv1.6 subunits (Brew and Forsythe, 1995; Harvey and Robertson, 2004). Adaptation was induced by voltage steps in voltage-clamp mode, bracketed by noisy, depolarizing test stimuli in current clamp (Fig. 3). In the absence of α-DTX, NM neurons displayed a large, slowly inactivating outward current during the depolarizing voltage step and showed a consistent relationship between the conditioning voltage and the increase in firing rate in 14 of 14 cells (Fig. 3A,C). Application of α-DTX greatly reduced the outward current and blocked the rate increase in 9 of 9 cells tested (Fig. 3B,C). The initial firing rates ranged from 10 to 130 Hz in the control condition and 4 to 195 Hz in the presence of α-DTX. Addition of α-DTX increased neuronal input resistance by eliminating IKlt. Because the rate increase depends on membrane voltage rather than firing rate, we compensated for the changes in input resistance (72 ± 10 MΩ in α-DTX and 17 ± 1 MΩ in control ACSF; n = 8 and 14, respectively) by reducing the strength of the current-clamp stimuli to match the average depolarization produced in the control condition. Input resistance was defined as the slope of the voltage–current relationship obtained by measuring membrane voltage during the last 10 ms of 100 ms depolarizing current injections from 0 to 200 pA. Cells that fired action potentials throughout the depolarizing steps in the presence of α-DTX were not used for measuring input resistance. We observed that depolarizing NM neurons above −40 mV in the presence of α-DTX caused a gradual decrease in firing rate, which may represent an unmasking of the very slow rate decrease observed in some NM neurons under control conditions. This rate decrease might be caused by inactivation of sodium channels. These findings provide direct evidence in support of the hypothesis that IKlt is responsible for the rate increase.

Figure 3.
Figure 3.

α-DTX blocks IKlt and the slow rate increase. A, PSTH (top), voltage response or command (middle), and current injection or response (bottom) of an NM neuron in hybrid clamp in normal ACSF. The dotted line represents the recording mode versus time (VC, voltage clamp; IC, current clamp). The arrow indicates the large, slowly inactivating outward current during the depolarizing voltage-clamp step. B, PSTH, voltage, and current (as in A) of the same neuron in the presence of 100 nm α-DTX. The arrow shows the reduced outward current during the depolarizing voltage-clamp step. C, Mean change in firing rate versus conditioning voltage for NM neurons in control ACSF (n = 14; open triangles) and 100 nm α-DTX (n = 7; filled diamonds; *p < 0.01, Bonferroni's corrected t test). Error bars represent SE.

Recovery from IKlt inactivation and firing rate adaptation

To further investigate the involvement of IKlt in the rate increase, we compared the time courses of recovery from IKlt inactivation and recovery from the firing rate increase. The time course and voltage dependence of recovery from IKlt inactivation were measured by recording from NM neurons in voltage-clamp mode in the presence of 0.5 μm tetrodotoxin to block voltage-gated Na+ channels (Fig. 4A). IKlt inactivation was reliably induced by 5-s-long steps from −80 to −40 mV. At the end of the conditioning step, the cell was stepped to a voltage between −80 and −50 mV for 10–9000 ms, followed by a 200 ms test step to −40 mV. Recovery was calculated by the following formula: Recovery = (Itest − Ifinal)/(Iinitial − Ifinal), where Iinitial is the peak current during the conditioning step, Ifinal is the current at the end of the conditioning step, and Itest is the peak current during the test step. The results of this experiment are summarized in Figure 4A, bottom. Recovery from inactivation was strongly voltage dependent, with less recovery at more depolarized potentials.

Figure 4.
Figure 4.

Recovery from IKlt inactivation and recovery from firing rate adaptation follow similar time courses. A, Recovery from IKlt inactivation. The top two traces show the current generated in response to voltage-clamp steps. Two sweeps with different recovery intervals are superimposed in black and gray. The bottom panel is a plot of the amount of recovery from IKlt inactivation versus recovery interval at −50 mV (n = 10), −60 mV (n = 8), −70 mV (n = 7), and −80 mV (n = 6). The dashed lines are single-exponential fits to the data. Error bars equal SE. B, Same layout as in A but for recovery from firing rate adaptation in hybrid-clamp mode. The dotted black and gray lines indicate times spent in voltage clamp (VC) and current clamp (IC) for the like-shaded traces. The bottom panel is a plot of the recovery of firing rate from adaptation versus the recovery interval at −60 or −70 mV (n = 8). The dashed lines are exponential fits to the data. C, Plot of the mean recovery time constants (from the exponential fits) versus the recovery voltage for IKlt inactivation (open triangles) and firing rate adaptation (filled squares) (*p < 0.01, Bonferroni's corrected t test). Error bars represent SE.

The time course and voltage dependence of recovery from spike frequency adaptation were determined by recording from NM neurons using the hybrid current-clamp/voltage-clamp technique described in Materials and Methods (Fig. 4B). After holding the cell at −60 mV in voltage clamp, adaptation was induced by a 5-s-long, noisy, depolarizing stimulus in current clamp, with an identical noise segment during the initial period (0–0.5 s) and the final period (4.5–5 s). The cell was then immediately switched to voltage clamp and held at either −60 or −70 mV for 250–9000 ms. At the end of the voltage-clamp recovery period, a test stimulus consisting of a half-second-long injection of noisy depolarizing current identical with that at the beginning of the conditioning step was presented in current clamp. Recovery from adaptation was calculated using the response to the identical noise segments as follows: Recovery = (Ffinal − Ftest)/(Ffinal − Finitial), where Finitial is the mean firing rate from 0.1 to 0.5 s, Ffinal is the mean firing rate from 4.6 to 5 s during the conditioning stimulus, and Ftest is the mean rate from 0.1 to 0.5 s during the test stimulus (Fig. 4B, bottom).

The amounts of recovery from IKlt inactivation and recovery from firing rate adaptation were voltage dependent. Recovery from IKlt inactivation was greatest at −80 mV, reaching 90% after 9 s. Higher voltages caused the current to recover progressively less. At −50 mV, the total recovery was 11%. The amount of recovery from adaptation also increased with hyperpolarization. The total recovery of firing rate was 80% at −70 mV and 35% at −60 mV. This study may have underestimated the total recovery, because IKlt showed gradual rundown during whole-cell recording.

The time courses of recovery from IKlt inactivation and spike frequency adaptation were fitted with a single exponential function. The time constants of the fit for current and firing rate are plotted in Figure 4C. The time course of recovery from IKlt inactivation did not show a monotonic relationship with recovery voltage. Recovery was fastest at −80 mV, slowest at −60 mV, and slightly faster at −50 mV. Recovery from spike frequency adaptation showed a time course similar to that of recovery from IKlt inactivation.

Because the recovery from IKlt inactivation is slow, and is less complete at more depolarized voltages, firing rate adaptation is likely to persist during and after periods of high synaptic activity. Therefore, once adaptation has developed, it can affect the processing of subsequent auditory stimuli that contain overlapping frequency components.

Changes in spike-timing precision during adaptation

Previous studies implicated IKlt as the major current responsible for the selectivity of NM neurons for rapidly fluctuating inputs, and for the exceptional spike-timing precision of these cells (Reyes et al., 1994; Rathouz and Trussell, 1998; Rothman and Manis, 2003b; Svirskis et al., 2003; Fukui and Ohmori, 2004; Slee et al., 2005; McGinley and Oertel, 2006; Day et al., 2008). We hypothesized that inactivation of IKlt should result in spike generation in response to more slowly rising inputs and an overall increase in spike jitter. We investigated the effects of adaptation on spike-timing precision by stimulating NM neurons with 5 s frozen noise steps in current clamp, with an identical noise segment during the initial period (0–1 s) and the final period (4–5 s) (Fig. 5A). Spike jitter was calculated as the SD of the averaged spike time histograms during the early response (200–700 ms after stimulus onset) and during the late response (4200–4700 ms). We found that spike jitter during the late response was increased by an average of 15% (Fig. 5C, black diamonds) (N = 13; p < 0.05). Spike jitter was increased in 11 of 13 neurons tested.

Figure 5.
Figure 5.

Adaptation decreases spike-timing precision in NM. A, Example of a “frozen noise” stimulus used to measure changes in spike jitter; a and b indicate identical noise segments. B, Response of an NM neuron to three repeats of the stimulus shown in A. The numbered rasters show the time of spikes during noise segment a (top raster; black) and during noise segment b (bottom raster; blue, “old” spikes; red, “new” spikes). C, Change in spike jitter for early (200–700 ms after stimulus onset) and late response (4200–4700 ms) (gray crosses, individual cells; black diamonds, group average; n = 13; p = 0.4; error bars represent SE). D, Mean spike jitter versus spike type (see text) (n = 8; *p < 0.05, paired t test). Error bars represent SE.

The responses to early and late segments of the frozen noise were superimposed to evaluate the reproducibility of spiking to identical inputs before and after adaptation. The spikes occurring during the early part of the response were termed “early” (Fig. 5B, top rasters, black). The late response could be divided into two sets of spikes. The set of spikes termed “old” occurred at times matching those of early spikes within 1 ms (Fig. 5B, bottom rasters, blue). The other late spikes were termed “new” (Fig. 5B, bottom rasters, red). It was rare to observe an early spike that did not have an old spike partner in the late segment. We analyzed spike-timing precision by calculating spike jitter as described above. The jitter of old spikes was reduced by 18% compared with the early spikes, but the jitter of the new spikes was 47% greater (n = 13; p < 0.001) (Fig. 5D). Addition of the new spikes increased the overall mean spike jitter after adaptation by ∼15%.

To test the hypothesis that adaptation allows more slowly rising current fluctuations to elicit spikes, we measured the spike-triggered average current (STA) for the unadapted, old, and new spikes. The STAs were obtained by analyzing NM neuron responses to 5 s noise steps with identical noise segments from 0 to 1 s and 4 to 5 s. The paired segments were unique to each 5 s stimulus. The neurons were allowed to rest for 5 s between the noise sweeps. Data were accepted for analysis if the resting potential and firing rate of the cell remained stable throughout the recording. Responses of an NM neuron to three sweeps of this stimulus are shown in Figure 6A. The STA for the three spike types defined above was calculated by averaging the current waveforms preceding each spike across 24 noise repeats (Fig. 6B). The STA was identical for the early and old spikes. However, the STA for the new spikes showed reduced maximum slope, and reduced amplitude of both negative and positive components (Fig. 6C) (n = 5), indicating that the new spikes were on average caused by smaller, more slowly rising current fluctuations. Thus, adaptation caused the input selectivity of NM cells to broaden to encode slower and smaller input fluctuations.

Figure 6.
Figure 6.

Adaptation allows more slowly rising current fluctuations to elicit spikes. A, Rasters of spike times evoked during the initial and final segment of a noise stimulus as in Figure 5, but with a different noise stimulus during each sweep. B, Example of spike-triggered average current (STA) for early (black line), old (blue line), and new spikes (red line). C, Change in the STA for early versus old spikes (blue) or early versus new spikes (red). Changes for the maximum slope of the STA (peak value of the first derivative), and amplitude of the negative and positive components of the STA for the new or old spikes relative to the early spikes were calculated by the following formulas: [(Xnew − Xearly)/Xearly]*100% or [(Xold − Xearly)/Xearly]*100%, respectively (n = 5; *p < 0.01, paired t test).

NM neurons show a firing rate increase in response to simulated synaptic inputs

NM neurons reliably showed adaptation in response to depolarizing current injection. However, the eighth nerve inputs to NM give rise to patterns of synaptic conductances that have little resemblance to the current steps and Gaussian noise applied in the experiments described above. In addition, because of the tonotopic organization of the chick auditory system (Rubel and Parks, 1975), the higher-order statistics of the eighth nerve inputs change gradually along the tonotopic axis.

To determine whether more physiological stimuli of long duration elicit spike frequency adaptation, we obtained several recordings from NM neurons while stimulating them with simulated eighth nerve inputs using a dynamic-clamp paradigm (see Materials and Methods). We hypothesized that physiological stimuli with a high degree of temporal dispersion, which depolarize the cells in a sustained manner, will cause IKlt inactivation. The amount of temporal dispersion can be influenced by many factors; for example, it becomes greater with increasing characteristic frequency (CF) of the input, increased firing rate or number of eighth nerve fibers, and increased jitter of the eighth nerve action potentials. In this experiment, we tested the effects of the input CF on the rate increase. The other parameters were chosen as conservative estimates of the physiological parameters (producing minimal temporal dispersion) based on published data (Hackett et al., 1982; Warchol and Dallos, 1990; Raman and Trussell, 1992; Salvi et al., 1994; Zhang and Trussell, 1994b; Brenowitz and Trussell, 2001; Saunders et al., 2002).

Simulated EPSGs were each generated as an α function with a time constant of 0.25 ms; the amplitude of the unitary EPSG was set at 25–60 nS; the EPSG train was generated using three eighth nerve fibers firing at 200 Hz, which is in the intermediate range of eighth nerve firing rates; the jitter of the eighth nerve action potentials was set to zero. EPSGs phase-locked to a sine wave representing a pure tone were generated for three different tone frequencies (500, 1000, and 2000 Hz) and injected into NM neurons. A gradual rate increase similar to that caused by noisy, depolarizing current was elicited by the simulated 1000 Hz (n = 14) and 2000 Hz (n = 8) inputs but not by the 500 Hz input (n = 19) (Fig. 7). These data suggest that spike frequency adaptation can occur under physiological conditions if the synaptic inputs occur with sufficient temporal dispersion.

Figure 7.
Figure 7.

Physiological patterns of simulated excitatory synaptic conductance can cause a slow rate increase. A, Example of a conductance stimulus (bottom), the resulting injected current (middle), and the voltage response (top) in dynamic clamp. B, PSTH of responses to simulated synaptic inputs phase-locked to 500 and 1000 Hz. C, Mean change in firing rate versus the phase-locking frequency imposed on the simulated synaptic inputs (n indicated above each bar). Error bars represent SE (z test, *p < 0.01).

Adaptation to simulated synaptic inputs decreases spike-timing precision in NM neurons

To determine whether the rate increase in response to the simulated synaptic inputs was accompanied by a decrease in spike-timing precision, we measured the spike jitter and vector strength of the NM responses to a simulated synaptic input that reliably induced a rate increase. We generated 5-s-long conductance trains phase-locked to 1500 Hz, using three eighth nerve fibers firing at an average rate of 300 Hz, without jitter (Fig. 8A, bottom trace). The conductance trains had identical segments from 0 to 1 s and 4 to 5 s. The paired segments were unique to each 5 s stimulus. Synaptic depression was added to the stimulus to avoid a bimodal distribution of stimulus amplitudes (see Materials and Methods). The mean level of depression reached steady state in <200 ms after stimulus onset. Changes in firing rate, spike jitter, and vector strength were calculated for the early response (200–700 ms after stimulus onset) and the late response (4200–4700 ms). Spike jitter was defined as the SD of a Gaussian fit to the periodic peristimulus time histogram (PSTH) for the early and late responses (Fig. 8B). Vector strength, which is a measure of phase-locking of the response of auditory neurons to periodic stimuli, was calculated by the following formula: Embedded Image (Goldberg and Brown, 1969). NM neurons increased their firing rate by an average of 15% during this stimulus (Fig. 8C, black diamonds) (N = 8; p < 0.05). Spike-timing precision of the late response was reliably reduced, with spike jitter increasing by 12% (Fig. 8D, black diamonds) (N = 8; p < 0.05) and vector strength decreasing by 12% (Fig. 8E, black diamonds) (N = 8; p < 0.001). These findings suggest that NM neurons can exhibit adaptation of spike-timing precision under physiological conditions.

Figure 8.
Figure 8.

Adaptation decreases spike-timing precision in NM in response to simulated physiological inputs. A, Example of a conductance stimulus (bottom), the resulting injected current (middle), and the voltage response (top) in dynamic clamp. B, Cycle PSTH of the early (black) and late (gray) responses. The solid lines are Gaussian fits to the data. C, Firing rate for early and late response (gray crosses, individual cells; black diamonds, group average; n = 13). D, Spike jitter for early and late response. E, Vector strength for early and late response. For C–E, the error bars represent SE (*p < 0.01, paired t test).

Discussion

NM neurons relay monaural information about the phase of auditory inputs, encoded in the timing of action potentials, to specialized coincidence detector neurons in NL. In this in vitro study, we found that NM neurons show non-monotonic spike frequency adaptation on multiple timescales. We investigated the mechanisms of the rate-increasing adaptation that develops between 1 and 20 s and is accompanied by a decrease of spike-timing precision. This adaptation depends on sustained depolarization, is independent of firing, and is eliminated by α-dendrotoxin, strongly suggesting that its mechanism is slow inactivation of IKlt. Consistent with previous studies implicating IKlt as the current responsible for the selectivity for rapidly fluctuating inputs and precise spike-timing characteristic of NM neurons (Reyes et al., 1994; Rathouz and Trussell, 1998; Rothman and Manis, 2003b; Svirskis et al., 2003; Fukui and Ohmori, 2004; Slee et al., 2005; McGinley and Oertel, 2006; Day et al., 2008), we found that spike frequency adaptation in NM neurons resulted in spike generation in response to more slowly rising inputs. We further investigated the spike frequency adaptation elicited by simulated physiological inputs in dynamic current clamp. We found that inputs phase-locked to 500 Hz did not cause a rate increase, whereas inputs at CF of 1000–2000 Hz reliably elicited the rate increase accompanied by a decrease in spike-timing precision.

The rate increases elicited by the sEPSG trains in this experiment were smaller than those caused by noise. However, these studies probably underestimate the strength of adaptation that occurs under real physiological conditions, in which several additional factors may increase IKlt inactivation. These include a slow component of the EPSC, higher firing rates of the eighth nerve inputs, jitter in the eighth nerve firing, and depolarizing GABAergic input. Each of these factors will increase the amount of sustained depolarization and thereby increase IKlt inactivation. In the present study, we modeled the unitary EPSG to match the rise and decay time course of the AMPA receptor-mediated EPSCs described by Zhang and Trussell (1994a,b). Previous work found that a small NMDA receptor-mediated current was also present in NM (Zhang and Trussell, 1994b). The time course of this current is slow compared with the AMPA receptor-mediated EPSC and could provide a small persistent depolarization that may increase IKlt inactivation (Zhang and Trussell, 1994a). The present study also used conservative parameter choices for the firing rate of the eighth nerve fibers, simulating three eighth nerve inputs firing at 200 Hz. Previous in vivo studies showed that auditory nerve fibers in the chick can follow a sound stimulus at rates up to 400 Hz (Salvi et al., 1994; Saunders et al., 2002), thus allowing for an increased frequency of EPSC arrival and the possibility of greater temporal dispersion. In addition, previous studies showed a tonotopic gradient of the number of eighth nerve inputs to NM neurons; lower-frequency units receive a larger number of afferent fibers, whereas mid- and high-frequency cells receive only two to three fibers (Parks and Rubel, 1978; Carr and Boudreau, 1991; Köppl, 1994; Köppl and Carr, 1997; Fukui and Ohmori, 2004). The greater number of converging units in the low-frequency region could also contribute to temporal dispersion, especially if the incoming action potentials are not perfectly phase-locked to the auditory tone.

In addition to the excitatory input, which was the focus of the present study, the depolarizing GABAergic input to NM neurons provided by the superior olivary nucleus (Lachica et al., 1994) may be a major cause of adaptation. Mature NM neurons have a much higher intracellular chloride concentration than most mature neurons in the CNS. Therefore, activation of GABAA receptors in NM produces depolarizing responses, which have been shown to evoke spikes in vitro (Lu and Trussell, 2001; Monsivais and Rubel, 2001; Howard et al., 2007). GABA application in vitro reduces the firing rates of NM neurons in response to pulse trains by producing sustained depolarization and activating IKlt, which shunts excitatory inputs. Thus, prolonged activity of the depolarizing GABAergic inputs is likely to elicit IKlt inactivation and the subsequent spike frequency adaptation.

Although adaptation in NM neurons increases firing rate, its functional outcome may also depend on an associated increase in spike jitter. An acceleration of firing caused by inactivation of outward current has been observed in multiple cell types (Hounsgaard and Midtgaard, 1988; Storm, 1988; Manis, 1990; Spain et al., 1991; Turrigiano et al., 1996; Morisset and Nagy, 1998). In addition, several studies implicated slow inactivation of potassium current in delayed firing in response to depolarizing steps (Storm, 1988; McCormick, 1991; Nisenbaum et al., 1994; Wang and McKinnon, 1995; Turrigiano et al., 1996). Although some of the studies suggested that slow inactivation of the outward current caused long-term changes in neuronal excitability (Marom and Abbott, 1994; Turrigiano et al., 1996), the functional implications of the accelerating firing were not well understood. Recently, a few studies have investigated changes in spike-timing precision caused by different input statistics (Mainen and Sejnowski, 1995; Glantz and Schroeter, 2004; Xu-Friedman and Regehr, 2005; Prescott et al., 2006), inhibitory circuits (Wehr and Zador, 2003; Gabernet et al., 2005; Mittmann et al., 2005; Bacci and Huguenard, 2006), and neuromodulation (Tang et al., 1997; Billimoria et al., 2006). Previous studies that investigated the effect of adaptation on spike-timing precision found that it either had no effect on precision (Tang et al., 1997; Heitwerth et al., 2005; Avissar et al., 2007) or had small effects compared with the temporal structure of the stimulus (Higley and Contreras, 2006). To our knowledge, the present study is the first to report adaptive changes in spike-timing precision on a timescale that may be relevant to stimulus processing.

Although the changes in spike-timing precision reported here are small (∼15 μs or 12%), studies in a variety of species show that changes of this magnitude are comparable with the timescale of perceived changes in the location of a sound. For example, the smallest change in ITD detectable by humans is 10–20 μs (Klumpp and Eady, 1956; Mills, 1958), whereas the ITD discrimination threshold in cat is ∼30 μs (Wakeford and Robinson, 1974), and in rabbit, an animal with poor ability to localize sounds, the ITD threshold is 50–60 μs (Ebert et al., 2008). In chick, the maximum physiologically available ITD measured with a 1000 Hz pure tone is ∼180 μs, and this value decreases with increasing tone frequency (Rosowski and Saunders, 1980; Hyson et al., 1994). This ITD difference represents perceptual difference for a sound directly in front of the head compared with a sound 90° at the side. Presumably, the chick can perceive differences in sound location for smaller gradations within each hemifield. In addition, NL neurons in the barn owl can use information encoded in the timing of spikes generated in NM with a time resolution of microseconds (Carr and Konishi, 1990).

At present, it remains unknown whether the firing rate increase or the loss of temporal precision is more important for the encoding of ITDs in postsynaptic NL cells. The sound localization circuit has been shown to partially compensate for increases in firing rate by using inhibition (Peña et al., 1996; Grande et al., 2004) and synaptic depression (Kuba et al., 2002; Cook et al., 2003). Thus, it is possible that the increase in jitter during adaptation in NM neurons might be the dominant effect. Greater jitter may decrease the sharpness of the sound localization map in NL and thereby reduce ITD sensitivity during sustained auditory stimuli without compromising the localization of newly presented sounds. Future studies are required to investigate this hypothesis.

Footnotes

  • This work was supported by a Veterans Affairs Merit Review (W.J.S.) and by National Research Service Award 5F31DC009176 from the National Institute on Deafness and Other Communication Disorders (M.S.K.). We thank Sean Slee, Adrienne Fairhall, Marc Binder, David Perkel, and Ed Rubel for helpful discussions, Carol Robbins, and Sue Usher for excellent technical assistance.

  • Correspondence should be addressed to William J. Spain, Neurology (127), 1660 South Columbian Way, Seattle, WA 98108. spain{at}u.washington.edu

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Adaptation of Firing Rate and Spike-Timing Precision in the Avian Cochlear Nucleus
Marina S. Kuznetsova, Matthew H. Higgs, William J. Spain
Journal of Neuroscience 12 November 2008, 28 (46) 11906-11915; DOI: 10.1523/JNEUROSCI.3827-08.2008
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