 |
||||
|
||||
|
||||
|
||||
Previous Article | Next Article
The Journal of Neuroscience, March 15, 2002, 22(6):2083-2095
Intrinsic Firing Dynamics of Vestibular Nucleus Neurons
Chris Sekirnjak and Sascha du Lac Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, California 92037
ABSTRACT
TOP
ABSTRACT
INTRODUCTION
Individual brainstem neurons involved in vestibular reflexes respond to identical head movements with a wide range of firing responses. This diversity of firing dynamics has been commonly assumed to arise from differences in the types of vestibular nerve inputs to vestibular nucleus neurons. In this study we show that, independent of the nature of inputs, the intrinsic membrane properties of neurons in the medial vestibular nucleus substantially influence firing response dynamics. Hyperpolarizing and depolarizing inputs evoked a markedly heterogenous range of firing responses. Strong postinhibitory rebound firing (PRF) was associated with strong firing rate adaptation (FRA) and occurred preferentially in large multipolar neurons. In response to sinusoidally modulated input current, these neurons showed a pronounced phase lead with respect to neurons lacking strong PRF and FRA. A combination of the hyperpolarization-activated H current and slow potassium currents contributed to PRF, whereas FRA was predominantly mediated by slow potassium currents. An integrate-and-fire-type model, which simulated FRA and PRF, reproduced the phase lead observed in large neurons and showed that adaptation currents were primarily responsible for variations in response phase. We conclude that the heterogeneity of firing dynamics observed in response to head movements in intact animals reflects intrinsic as well as circuit properties.
Key words: vestibular nucleus neuron; spike frequency adaptation; postinhibitory rebound; IH; potassium current; phase lead
INTRODUCTION
To produce accurate behavioral responses, neural circuits must provide output signals with appropriate temporal properties. The vestibulo-ocular reflex (VOR) is well suited for the study of temporal signal dynamics because the underlying neurons and the control signals they carry have been studied extensively. In response to head movement, the VOR produces eye movements that rapidly and accurately stabilize images on the retina. To ensure faithful image stability over the wide range of behaviorally relevant head movement frequencies, the neural circuitry for the VOR must transform head movement inputs into the precise command signals to ocular motoneurons that are required to move the eye accurately while compensating for phase lags imposed by orbital mechanics (Skavenski and Robinson, 1973
; Highstein, 1988
The temporal processing requirements of the vestibular system are reflected in the diversity of neuronal firing responses to head movements. Neurons in the vestibular nuclei respond to identical sinusoidally modulated head movements with a wide range of response phases that vary over many tens of degrees across neurons (Shinoda and Yoshida, 1974
Although membrane properties of vestibular nucleus neurons have been studied in a number of species, most investigations have focused on action potential shapes, spontaneous firing, and subthreshold membrane currents (Serafin et al., 1991a
To determine how intrinsic firing properties could contribute to the range of response phases observed in vivo, we analyzed the firing response dynamics of MVN neurons recorded with whole-cell patch electrodes in brain slices. Using synaptic stimulation and intracellular current injection, we found that the intrinsic firing responses to hyperpolarizing, depolarizing, and sinusoidally modulated stimuli vary widely across MVN neurons. Pharmacological and computational analyses revealed that variations in response dynamics are conferred by at least two types of ionic currents that depend on previous membrane potential and firing. Our results indicate that ionic currents shape the dynamics of MVN neuronal responses to sensory stimuli.
MATERIALS AND METHODS
Slice preparation. Slices were prepared from 14- to 19-d-old mice of mixed C57BL/6 and BALB/c background (mean age, 17 d). The animals were deeply anesthetized with sodium pentobarbital and decapitated. The hindbrain was rapidly removed from the skull and placed in ice-cold artificial CSF (ACSF) aerated with 95% O2 and 5% CO2. A tissue block containing the brainstem with attached cerebellum was dissected away and glued to a Teflon chuck with cyanoacrylate. Coronal slices of 300 µm thickness were prepared on a vibratome (Campden Instruments, Lafayette, IN) in ice-cold aerated ACSF. All slices were cut at a level rostral to the dorsal cochlear nucleus and caudal to the root of the facial nerve. The slices were transferred to a holding chamber and incubated for at least 1 hr at room temperature in aerated ACSF.
Electrophysiology. The bath solution (ACSF) contained (in mM): 124 NaCl, 5 KCl, 1.3 MgSO4, 26 NaHCO3, 2.5 CaCl2, 1 NaH2PO4, and 11 dextrose. The glutamate receptor blocker kynurenic acid (2 mM) was added to the bath solution in all experiments. Aerated ACSF had a final pH of 7.4 and an osmolarity of 300 mOsm. For ACSF containing zero Ca2+, CaCl2 was replaced with equimolar MgCl2; for low-K+ bath solutions, KCl was reduced from 5 to 1 mM.
Micropipettes for whole-cell recordings were fabricated from borosilicate glass (outer diameter 1.5 mm; Garner Glass, Claremont, CA) with a Sutter Instruments (Novato, CA) P-97 puller and had resistances of 5-15 M
. The internal recording solution contained (in mM): 140 K-gluconate, 8 NaCl, 10 HEPES, 0.1 EGTA, 2 Mg-ATP, and 0.3 Na-GTP. The pH was adjusted to 7.2-7.5 and the osmolarity to 280-285 mOsm.
Whole-cell patch recordings were performed in a submersion-type chamber with continuous perfusion of aerated ACSF. Neurons were visualized at 40-80× with an infrared differential interference contrast (DIC) microscope (Olympus, Tokyo, Japan) at depths of 30-90 µm below the slice surface. Recordings were made with a AxoClamp 2B amplifier (Axon Instruments, Foster City, CA) in current-clamp mode. Access resistance was checked and compensated regularly throughout each experiment. Input resistance was measured by injecting small hyperpolarizing current pulses from a membrane potential of approximately
80 mV. Signals were filtered at 10 kHz, digitized at 2-20 kHz, and recorded using Macintosh G3 and G4 computers. Intracellular current injection, as well as triggering of the extracellular stimulating electrode, were controlled by the computer. All recordings were made at a temperature of 31-33°C. A calculated liquid junction potential of 14 mV was subtracted from all membrane potentials.
Monopolar tungsten electrodes (FHC Inc, Bowdoinham, ME) were used for extracellular stimulation of axons. The electrode was placed at a distance of 200-800 µm from the recorded neuron, usually near the dorsolateral rim of the MVN. This region contains the axons of cerebellar Purkinje cells projecting to the vestibular nuclei (Brueckner et al., 2001
Kynurenic acid, cesium chloride, tetrodotoxin, and cadmium chloride were obtained from Sigma (St. Louis, MO), and ZD7288 was obtained from Tocris Cookson (Ballwin, MO). For dye filling of cells, tetramethylrhodamine dextran (Molecular Probes, Eugene, OR) at 0.1 mg/ml was included in the internal recording solution. The slices were fixed overnight in 4% paraformaldehyde, frozen, and resectioned at 100 µm. Images were acquired on a Olympus fluorescence microscope using a Hamamatsu (Tokyo, Japan) digital camera. To capture all neuronal processes, several focal planes were imaged and superimposed in Adobe Photoshop (Adobe Systems, San Jose, CA).
Data analysis. The recorded signals were analyzed offline using IgorPro software (Wavemetrics, Lake Oswego, OR). Instantaneous firing rate was calculated as the reciprocal of the interval between successive spikes and was assigned to the time of the second spike. The width of the action potential was measured at spike threshold, and the afterhyperpolarization (AHP) amplitude was calculated as the membrane potential difference between spike threshold and the absolute membrane potential minimum after the falling phase of the spike. Both values were calculated from averages of 10-20 action potentials, aligned at the peak. For the calculation of spike threshold, see Murphy and du Lac (2001)
Firing rate adaptation (FRA) was quantified as either the difference (absolute adaptation) or the ratio of 100 msec averages of firing rate at the beginning and the end of each current step. The decay in firing rate could be fit well by a single exponential beginning 50 msec after step onset (thereby excluding the first few interspike intervals, which varied markedly across step amplitudes and neurons). Therefore, we excluded the first 50 msec from analyses of adaptation. To standardize the analyses of adaptation across neurons, analyses were restricted to firing responses of <40 spikes/sec at the end of the step.
Sinusoidal functions f(t) = A + B sin(2
ft + C) were fitted to the firing rate responses to sinusoidally oscillating current injections, with the parameters A, B, and C chosen to minimize the mean squared error. The frequency f was set equal to the input frequency. Phase shifts were given by the best-fit values of parameter C. A stretch of at least four or five cycles of each frequency was used to obtain fits. For firing rate responses that displayed cutoff during a half-cycle, data point sequences between silent periods were fitted individually, and the resulting fit parameters were averaged. To estimate the goodness of sinusoidal fits, firing rate values predicted by the best sine fit were regressed against actual firing rate values and the linear correlation coefficients determined. Slopes for these regression lines were always close to 1, with intercepts near 0. Values for R2 ranged from 0.95 to 0.99.
To estimate cell size, DIC images of neurons acquired during whole-cell recordings were analyzed. Each cell shape was approximated by an ellipse, and the product of the long and the short axis was used as the approximate neuronal somatic area. The number of primary processes was counted in the living slices immediately after dye filling, or in paraformaldehyde fixed slices after resectioning.
ANOVA (StatView software) was used to analyze group differences in mean input resistance, spike width, AHP, gain, and phase shifts for neurons with weak, medium, and strong rebound. Post hoc comparisons were made using tests that do not assume equal variances of the groups (Scheffé). A p value <0.05 was considered significant. All error values given are SEMs, unless otherwise indicated. All values of n stated are numbers of neurons.
Neuronal model. A computer model of firing rate dynamics was implemented in IgorPro. It consisted of an leaky integrate-and-fire algorithm (Knight, 1972
t, the subthreshold membrane potential change was calculated as:
where I(t) is the total current, C the input capacitance (C = 0.1),
To describe firing response variations in MVN neurons, two conductances were used: a rebound conductance gPRF and an adaptation conductance gFRA. The PRF conductance activated exponentially with membrane hyperpolarizations below an activation potential Vact:
with Vact =
= 70 mV. Using this implementation, 10% activation was achieved at approximately
6 for neuron A and 3 * 10
PRF = 300 and 1000 msec for rebound in neuron A and B, respectively, and
Spike threshold varied in the model as observed in experimental data traces; this was implemented by increasing threshold by a fixed amount
The input current Iinput was either a rectangular pulse of 1 sec duration or several seconds of a sinusoidally modulated waveform. Evoked firing was simulated by an additional small constant input current.
The values of
RESULTS
To investigate intrinsic firing dynamics, we made whole-cell patch recordings from MVN neurons in slices of the mouse brainstem and analyzed firing rate responses to hyperpolarizing, depolarizing, and sinusoidally modulated stimuli presented with intracellular injection of current. MVN neurons varied markedly in their intrinsic firing properties, in particular, in the extent of PRF and FRA. Both PRF and FRA depend on the immediate history of firing and therefore could, in principle, influence the phase of neuronal responses to sinusoidal inputs. We describe each of these properties in turn and then examine the underlying ionic mechanisms.
Postinhibitory rebound firing
PRF was observed in a subset of MVN neurons after membrane hyperpolarization evoked by either synaptic or intracellular stimuli. Stimulation of inhibitory axons lateral to the MVN with pulse trains produced a pause in firing that lasted for the duration of the stimulus and was followed by a transient increase in firing rate (Fig. 1A). This increase usually lasted for 1-2 sec, during which the firing rate returned to its baseline value along an exponential time course. The effect of trains of IPSPs could be mimicked by applying hyperpolarizing current pulses through the recording electrode (Fig. 1B). Examples of a neuron with PRF and one that lacked PRF are shown in Figure 1, B and C, respectively.
View larger version (26K):[in this window]
[in a new window]
Figure 1. Postinhibitory rebound firing in MVN neurons. Traces show membrane potential as a function of time in response to synaptic stimulation (A) or current injection (B, C) in three different neurons. The corresponding instantaneous firing rates are plotted below. A, Synaptically evoked rebound firing, induced by a 60 Hz stimulus train. Inset shows individual IPSPs during stimulation on an expanded scale. Calibration: 2 mV, 20 msec. Dotted line in bottom trace indicates spontaneous firing rate (5 spikes/sec). B, Rebound firing evoked by intracellular injection of a
To compare PRF across MVN neurons, each neuron was examined at a firing rate of ~10 spikes/sec, corresponding to the typical resting discharge in vitro; silent neurons were slightly depolarized by DC current injection to fire in this range. A standardized value for PRF was taken as the peak PRF after a 1 sec hyperpolarizing current pulse which lowered the average membrane potential by ~30 mV. The peak changes in firing rate observed ranged from
Properties of neurons with postinhibitory rebound firing
Figure 2A plots the input resistance of 132 MVN neurons as a function of their peak PRF. This distribution formed a continuum, but it was clear that neurons with low and high PRF differed substantially. To facilitate the analysis we grouped the neurons with lowest PRF and the highest PRF. Three populations were thus designated: neurons with weak (10 spikes/sec), medium (11-30 spikes/sec), and strong (>30 spikes/sec) PRF. A comparison of several physiological and morphological parameters across these groups is presented in Table 1. Neurons with medium or strong rebound firing had significantly lower input resistances than those that displayed weak PRF (p < 0.001). Because input resistance tends to decrease with cell surface area, this difference likely distinguished smaller from larger neurons in our sample. Indeed, when a fluorescent dye was included in the recording solution, neurons with strong PRF were found to have the largest somata and possess a multipolar morphology (Table 1). Neurons with weak PRF, on the other hand, tended to be of small size and displayed fewer proximal processes. Of 18 filled neurons, two are shown in Figure 2B. Approximate cell size was also estimated from 96 DIC images acquired during whole-cell recordings (Table 1). Neurons with strong PRF had significantly larger areas than those with weak or medium PRF (p < 0.001). Taken together, these results demonstrate that larger neurons displayed the strongest rebound firing.
View larger version (55K):[in this window]
[in a new window]
Figure 2. Properties of neurons with postinhibitory rebound firing. A, Input resistance depends on peak PRF. Dashed lines separate 132 neurons into groups with weak PRF (filled circles), medium PRF (open circles), and strong PRF (triangles). Neurons with high input resistance displayed less rebound firing. B, Example of a neuron with strong PRF (left) and one with weak rebound firing (right). Neurons were filled with tetramethylrhodamine dextran. Scale bars, 25 µm.
Spike shape also differed between neurons with weak or strong rebound firing (Table 1). Neurons with little PRF had significantly wider action potentials than did those in the medium and strong PRF group (p < 0.001). Weak PRF neurons also had significantly larger AHPs than both other groups (p < 0.001). Both action potential width and AHP magnitude varied continuously across neurons in our sample, and histograms of these two parameters were well described by a unimodal Gaussian distribution. On the basis of these parameters, neurons in our sample with weak PRF would be classified as type A, whereas those with medium to strong PRF would be classified as type B (Serafin et al., 1991a
View this table: [in this window]
[in a new window]
Table 1. Physiological and morphological characterization of MVN neurons Postinhibitory rebound firing depends on inhibition magnitude and duration
To determine the extent to which PRF can be elicited by synaptic inputs, we examined responses to trains of IPSPs evoked by stimulating inhibitory axons at frequencies between 10 and 70 Hz (Fig. 1A, 3A). Although the stimulating electrode was in the region of Purkinje cell axons, the identity of the inhibitory inputs stimulated could not be unambiguously determined. Membrane hyperpolarization increased with stimulation frequency such that hyperpolarizations of >10 mV could be produced with 60-80 Hz trains of IPSPs. Peak PRF increased linearly with the stimulation frequency (Fig. 3A, bottom), as observed in each of six neurons tested [average R2 = 0.91 ± 0.03 with slopes of 0.10 ± 0.02 (spikes/sec)/Hz]. Therefore, the faster inhibitory synaptic inputs fire, the stronger the evoked rebound firing in MVN neurons.
To extend the range of membrane hyperpolarizations beyond that evokable by electrical stimulation of synaptic inputs, responses to steps of intracellularly injected current were analyzed. PRF depended strongly on membrane hyperpolarization. The top panel in Figure 3B shows firing responses of a single MVN neuron to two different amplitude currents steps. As evidenced by the bottom panel of Figure 3B, PRF increased with membrane hyperpolarization. Similar results were obtained in each of 16 neurons tested. The duration of membrane hyperpolarization also influenced PRF. As shown in the example neuron at the top of Figure 3C, current steps lasting 25 msec evoked little PRF, whereas steps of 2 sec evoked strong PRF. The summary plot in Figure 3C (bottom) shows averages across all six neurons tested in this manner, normalized to the maximum value of PRF. The relationship between PRF and pulse duration was well described as a monoexponential process with a time constant of ~600 msec.
View larger version (25K):[in this window]
[in a new window]
Figure 3. PRF magnitude depends on amplitude and duration of inhibition. A, Effects of inhibitory synaptic stimulus frequency on PRF. Top traces, Examples of membrane potential responses to inhibitory synaptic stimulation at 20 and 70 Hz in a single neuron (resulting average hyperpolarizations were
Firing rate adaptation
MVN neurons responded to depolarizing current injection with an increase in firing rate, which gradually declined (adapted) during the duration of the step. Figure 4A shows examples of firing rate responses to families of current steps in two example neurons. The neuron in the right panel adapted significantly more than did the neuron in the left panel. To quantify these results, two measures of FRA were calculated (see Materials and Methods): the change in firing rate during the current step (absolute adaptation), and the ratio of the firing rate at the end and the rate near the beginning of the step (adaptation ratio). The neurons in Figure 4A had absolute adaptation values of 0.5 and 24 spikes/sec, and their adaptation ratios were 0.98 and 0.54, respectively.
View larger version (21K):[in this window]
[in a new window]
Figure 4. Firing rate adaptation varies across MVN neurons. A, Time course of firing rate changes in response to 1 sec depolarizing current steps in a neuron with PRF of 11 spikes/sec (left) and one with 88 spikes/sec (right). Responses to three repetitions at each current amplitude are shown. Current traces are plotted above. B, Summary plot of one measure of FRA (adaptation ratio, see Materials and Methods) as a function of PRF in 66 neurons. Stronger adaptation was seen in neurons with strong PRF. The linear correlation coefficient (R2; dashed line) was 0.70. C, Dependence of FRA on firing rate in neurons similar to the examples shown in A. Neurons with PRF of 11-19 spikes/sec (low PRF) showed an increase of adaptation with firing rate (circles; R2 = 0.48), but no change in adaptation ratio (crosses; R2 = 0.0005; n = 16). In 10 neurons with high PRF (>50 spikes/sec) both measures of FRA increased with firing rate (R2 = 0.37 for absolute adaptation; R2 = 0.30 for adaptation ratio).
The adaptation ratio ranged from 0.36 to 1.06 across our sample of MVN neurons (n = 58) and was correlated with the magnitude of postinhibitory rebound firing. Figure 4B plots the adaptation ratio as a function of peak PRF in 66 neurons; the correlation coefficient (R2) was 0.70, indicating that pronounced rebound firing was typically accompanied by strong adaptation.
The dependence of FRA on firing rate differed across neurons with strong and weak adaptation. In the examples shown in Figure 4A, the weakly adapting neuron exhibited substantial adaptation only at high firing rates (responses to large current steps), whereas the strongly adapting neuron showed considerable FRA at all firing rates. Figure 4C shows population data divided into two groups of neurons with low and high PRF (11-19 spikes/sec and > 50 spikes/sec, respectively). Neurons in the low PRF (and low FRA) group showed an increase in absolute adaptation (filled circles) with firing rate, whereas the adaptation ratio (crosses) did not depend on firing rate. This signifies that no matter how fast these neurons fired, the response at the end of the step was always proportional to the initial response. Neurons with high PRF (and high FRA), on the other hand, tended to adapt disproportionally less when active at high rates (Fig. 4C, right).
Dynamics of neuronal responses to temporally modulated inputs
The previous experiments established that in a subset of MVN neurons, sustained membrane hyperpolarization is followed by rebound firing and that those neurons respond to depolarization with strong adaptation of their firing rate. However, input signals to MVN neurons in the behaving animal vary with time, such that hyperpolarizing and depolarizing drive changes significantly during head movements. To better relate intrinsic firing dynamics with response properties measured in behaving animals, we analyzed firing rate responses to sinusoidally modulated input currents.
As shown in Figure 5A, sinusoidally alternating depolarization and hyperpolarization (solid line) evoked alternating increases and decreases in firing rate (data points). We analyzed responses to sinusoidal stimuli presented at frequencies between 0.125 and 4 Hz. Neurons often fell silent during the hyperpolarizing phase of large-amplitude stimuli (Fig. 5B), as can occur during head movements in intact animals (Fuchs and Kimm, 1975
View larger version (27K):[in this window]
[in a new window]
Figure 5. Neurons with strong PRF and FRA experience a phase-lead during sinusoidal current input. A, Comparison of a neuron with strong rebound firing (filled symbols) and one with weak rebound firing (open symbols) as they modulate their firing rate in response to sinusoidal input current at 0.125 Hz. Top trace, Input current versus time; bottom trace, firing rate versus time. The phase of the neuron with strong PRF was +16°, and the phase of the neuron with weak PRF was +4°, relative to the input sine wave. The difference between the two neurons can be clearly seen as a relative offset along the time axis. Dashed line indicates the baseline firing rate before current injection (30 spikes/sec). B, Response of the same neurons shown in A to a 0.25 Hz stimulus. Baseline firing rate of 12 spikes/sec is indicated by the dashed line. The phases were +11° and +2° for the strong and the weak PRF neuron, respectively. C, The phase of best-fitting sine waves is plotted against input frequency. Shown are averages for 12 neurons with strong PRF (>30 spikes/sec, mean 60 ± 7 spikes/sec; filled symbols), and eight neurons with weak PRF (
Neurons with strong PRF respond to sinusoidal inputs with a phase lead relative to those with weak PRF. Figure 5, A and B, shows examples from two neurons with strong and weak PRF (52 and 2 spikes/sec, respectively). To facilitate the comparisons, the firing rates of the neurons were adjusted to comparable levels with DC current. Responses to sinusoidal stimuli presented at 0.125 and 0.25 Hz are shown in Figure 5, A and B, respectively. In both cases, the response of the neuron with strong PRF (filled symbols) precedes that of the low-rebound neuron (open symbols). This positive shift along the time axis is referred to as phase lead and was found at all frequencies tested.
To quantify these phase shifts, sinusoidal best-fit curves to the firing rate response were calculated at each frequency and the phase relationship to the input stimulus determined. Sine waves generally fit the data well, with correlation coefficients ranging from 0.95 to 0.99. For comparison, the R2 values for the two neurons shown in Figure 5, A and B, were from 0.97 to 0.98. Figure 5C summarizes the frequency dependence of the phase of groups of neurons with weak and strong PRF, respectively. Firing rate was modulated by approximately ±20-30 spikes/sec around a baseline of 30 spikes/sec. The first group had an average PRF of 5 ± 1 spikes/sec (n = 8) and is plotted as open circles in Figure 5C. PRF in the second group of neurons averaged 60 ± 7 spikes/sec (n = 12), shown as filled squares. The firing rate phase advance of the neurons with strong PRF relative to the weak PRF neurons ranged from 10 to 15° over the range of stimulus frequencies tested. This phase difference between the two groups was significant at all frequencies (p < 0.01).
The amount of phase shift in each neuron was also correlated with the amount of firing rate adaptation. Figure 5D exemplifies this for 25 neurons tested at an input frequency of 0.25 Hz. Phase is plotted as a function of adaptation ratio. Larger phase leads were clearly associated with stronger FRA (R2 = 0.70). These data indicate that in MVN neurons, the processing of temporally modulated input signals differs considerably between neurons with strong and weak FRA and PRF.
Ionic mechanisms of rebound firing and adaptation
What ionic currents confer variations in response phase onto MVN neurons? We first examined the mechanisms of rebound firing and adaptation using pharmacological manipulations and then incorporated the results into a simple model to gain insights into mechanisms that underlie the differences in response phase described above.
The hyperpolarization-activated current IH contributes to rebound firing
The conductances underlying PRF must fulfill the following criteria, derived from the results presented above: (1) they must be activated by membrane hyperpolarizations in a membrane potential-dependent manner, (2) they must provide a net inward current after the offset of hyperpolarization, and (3) their effects on membrane potential should decay exponentially with a time constant of hundreds of milliseconds. The conductance that mediates the hyperpolarization-activated H current (IH) meets each of these criteria. Subthreshold activation of IH produces a transient depolarization after offset of membrane hyperpolarization that can be blocked by low concentrations of Cs+ (Pape, 1996
The effect of blocking IH on PRF is summarized in Figure 6C, which plots PRF in the presence of IH blockers against PRF in control bath medium for 26 neurons. To obtain independent verification of the results with Cs+, the selective IH blocker ZD7288 was substituted for Cs+ in seven neurons (filled circles); no differences were seen between the two pharmacological agents. The dashed line in Figure 6C denotes the case of IH having no effect on PRF. The data lie below this line in all but one neuron tested, indicating that IH contributes to PRF, although this contribution varies substantially across neurons. Note that there is an apparent plateau of PRF in IH blockers, indicating that a residual, IH-independent component contributes a maximum of ~30 spikes/sec to PRF. The contribution of IH to PRF depended on baseline firing rate. In five neurons firing at 10 ± 1 spikes/sec, blocking IH reduced PRF by 50 ± 7% (data not shown). However, when the same neurons were depolarized to fire at 30 ± 0.5 spikes/sec, IH blockade reduced PRF by only 26 ± 8%, indicating that given the same input current, IH contributes more strongly to PRF when neurons fire slowly and that an IH-independent mechanism dominates PRF at higher baseline firing rates. Blocking IH did not alter firing rate adaptation. Figure 6D shows the firing rate responses to depolarizing current steps in a neuron with strong PRF. Although Cs+ reduced PRF by 34% (data not shown), the magnitude and time course of FRA was unaffected. In 12 neurons tested, the average adaptation ratio before and after application of IH blockers was 0.83 ± 0.03 and 0.83 ± 0.04, respectively. Consistent with these findings, IH blockers did not induce a significant change in firing response gain (gain in Cs+ or ZD7288 was 103 ± 4% of control; n = 12). The phase lead observed in strong PRF neurons (Fig. 5) was not affected by blocking IH. Figure 6E shows average phase in response to sinusoidal current injection before and after IH blocker application. Care was taken to avoid cutoff during the trough of the sine waves to eliminate confounding subthreshold membrane potential changes because of increases in input resistance induced by IH blockers. No change in the phase of the firing response was evident, despite an average of 43% reduction of PRF (n = 3). These data suggest that it is unlikely that variations in PRF contribute significantly to phase differences in MVN neurons. In summary, although rebound firing, adaptation, and response phase are correlated in MVN neurons (Figs. 4B, 5D), these measures of firing response dynamics are mediated by ionic currents that differentially depend on IH. These results raise the possibility that an IH-independent component of PRF may underlie both FRA and phase lead.
View larger version (28K):[in this window]
[in a new window]
Figure 6. The hyperpolarization-activated current IH contributes to PRF, but not to FRA or phase. A, Responses to hyperpolarizing current steps in two MVN neurons. Left column, Example of a neuron with PRF dominated by IH. Membrane potential traces as a function of time for control (top trace) and with bath-applied 3 mM Cs+, a blocker of IH (bottom trace). Note that the membrane sag seen in the control trace (arrow) was abolished in the presence of Cs+. The injected current amplitudes were adjusted so that the average hyperpolarizations produced by both current steps were matched. Right column, Example of a neuron in which IH contributed little to rebound firing. Dashed lines indicate
Rebound can be fully eliminated by blocking IH and INa
What current or currents mediate the residual PRF observed when IH is blocked? In many cell types, Ca2+ currents contribute to low-threshold spikes after the offset of membrane hyperpolarization (Huguenard, 1996
A combination of TTX together with Cs+ or ZD7288 completely eliminated postinhibitory rebound in 12 neurons of 16 tested. The remaining four neurons displayed small residual rebound depolarizations in the presence of both drugs, which in two neurons were eliminated by blocking Ca2+ channels with cadmium (100 µM; data not shown), indicating that a small percentage of MVN neurons have a calcium contribution to rebound, most likely mediated by low-threshold Ca2+ channels (Serafin et al., 1990
View larger version (11K):[in this window]
[in a new window]
Figure 7. Rebound can be completely blocked by a combination of IH and INa antagonists. Membrane potential traces are plotted versus time under three different conditions in a single neuron. After eliminating the IH component with 100 µM ZD7288, PRF in this neuron was reduced from 63 (left trace) to 27 spikes/sec (center trace). Addition of the Na channel blocker TTX eliminated all postinhibitory rebound depolarization (right trace), indicating that either spiking or a TTX-sensitive Na+ current is necessary for the IH-insensitive component of PRF. Dashed line indicates
Firing rate adaptation is mediated by K+ currents, but is Ca2+-independent
In many types of neurons, firing rate adaptation is mediated by potassium currents that accumulate with successive action potentials (Baldissera et al., 1978
Repetitive firing is often followed by a long-lasting hyperpolarization (slow AHP) that is usually attributed to the accumulation of same current that underlies FRA (Azouz et al., 1996
View larger version (19K):[in this window]
[in a new window]
Figure 8. Spike-dependent K+ currents underlie firing rate adaptation. A, FRA depends on K+ currents. The driving force for K+ was increased by lowering extracellular K+ from 5 mM (control) to 1 mM (low K+). As shown in the firing response traces, adaptation was larger in low K+, indicating a K+-dependent contribution to FRA. B, The slow AHP is mediated by K+ currents. Overlap of membrane potential responses to a depolarizing step for control condition (thin line) and in low K+ (thick line). Arrows point to the slow AHP, which is increased in low K+. Dashed line indicates
Firing response dynamics modeled
A lack of specific pharmacological antagonists to K+ currents activated during spiking precluded experimental determination of the relative contributions of the currents underlying PRF and FRA to phase response in MVN neurons. To further explore the mechanisms of response dynamics, we constructed an integrate-and-fire model that captured the dynamics of both PRF and FRA in a quantitative manner. This type of model was chosen because it readily produces realistic spiking behavior without requiring detailed knowledge about the properties of every conductance expressed in MVN neurons. It reproduces firing responses of MVN neurons with a small number of variable parameters.
To simulate neuronal responses to hyperpolarizing and depolarizing current pulses, we added two ionic conductances to an integrate-and-fire model: a hyperpolarization-activated conductance and a spike-dependent potassium conductance. The membrane potential dependence of activation and time constants of decay of these conductances approximated those inferred from the results of our pharmacological experiments and from the literature (see Materials and Methods) and were set by matching model responses to depolarizing and hyperpolarizing steps to those of representative MVN neurons. The model also incorporated a variable spike threshold (see Materials and Methods), which did not change the results qualitatively, but was required to reproduce quantitatively the pattern of phase versus frequency, as discussed below. The dependence of PRF magnitude on hyperpolarization amplitude (Fig. 3B) and duration (Fig. 3C) were mimicked by the model (data not shown): PRF increased linearly with current step magnitude and exponentially with step duration. Similarly, adaptation increased linearly with baseline firing rate (as in Fig. 4C).
Figure 9 shows data and model results for two representative neurons with strong FRA and PRF (neuron A) and weak FRA and PRF (neuron B). Figure 9, A1 and B1, shows that the responses to depolarizing steps were reproduced well between 0 and 80 spikes/sec in both neurons. The adaptation conductance was 10-fold stronger in the strongly adapting model neuron than in the weakly adapting model neuron. Figure 9, A2 and B2, shows examples of real and modeled neuronal responses to hyperpolarizing steps at two different current amplitudes. The values for the PRF conductances required to reproduce the data differed by almost an order of magnitude between the two neurons shown (A and B), in accordance with the PRF measured experimentally (70 and 10 spikes/sec). At firing rates of ~30 spikes/sec and higher, an adaptation conductance alone was capable of producing transient increases in firing rate after the offset of a hyperpolarizing stimulus similar to PRF (data not shown). The modeled FRA conductance accumulated to a steady-state level during high-frequency firing, deactivated during the hyperpolarization, and its slow reactivation was seen as a PRF-like increase in firing rate after hyperpolarization. These results show that our model can reproduce the range of PRF and FRA observed in MVN neurons.
View larger version (23K):[in this window]
[in a new window]
Figure 9. Firing dynamics of an integrate-and-fire model with rebound and adaptation currents. A1-A3, Firing responses in an MVN neuron with strong PRF (70 spikes/sec; left) and in a modeled neuron (right). Responses to depolarizing steps (A1), hyperpolarizing steps (A2), and sinusoidal current injection at 0.5 Hz (A3) are shown. The simulated adaptation and rebound conductances were adjusted until the model reproduced the data. The model was then tested with sinusoidal current injection without further adjustments of parameters. The response phase of the model (12.1°) closely reproduced the experimental result (11.7°). B1-B3, As in A1-A3, but for a neuron with weak PRF (10 spikes/sec). Weak FRA (B1) and weak PRF (B2) were modeled by reducing the respective conductances by about an order of magnitude with respect to those in neuron A. The modeled response phase was 0.9°, and the experimental result was 0.4°. C, Comparison of phase versus frequency in real and modeled neurons. D, Comparison of results from model neuron A with all conductances (full model), with the PRF conductance set to 0 (without PRF), and with the FRA conductance set to zero (without FRA). The FRA conductance produces a phase lead, whereas the PRF conductance has little effect on phase.
To determine the roles of adaptation and rebound conductances in the variations in response phase seen in Figure 5, we assessed the responses of the model neurons to sinusoidal input currents. The results are presented in Figure 9, A3 and B3. The model simulated the actual firing rate waveforms well (R2 = 0.98 for neuron A and R2 = 0.99 for neuron B). As shown in Figure 9C, model neuron A exhibited a phase lead with respect to model neuron B of 11-14° across all frequencies, similar to the phase difference of 11-15° seen in the experimental data from the real neurons. In addition, the model reproduced the pattern of phase as a function of frequency observed in the real neurons and in the grouped data shown in Figure 5. Model neurons with fixed spike thresholds showed similar phase differences of 5-21° across frequencies. However, the pattern of phase versus frequency in the fixed-threshold model neurons differed from the real neurons, showing an increasing phase lead versus frequency.
To establish whether conductances responsible for PRF and FRA contribute equally to phase lead, the rebound or the adaptation conductance were each removed from the model in turn. We found that an adaptation conductance alone could produce most of the phase lead and that a model neuron with zero adaptation produced sinusoidal firing responses comparable with neurons with the smallest phase leads. This is shown in Figure 9D, where the model output for neuron A is plotted for three combinations of conductances. Removing the FRA conductance abolished the phase advance (crosses), whereas the output of a model without PRF (open circles) resembled that of the full model, except for an initial deviation in the rising phase of the sine wave. The phase values for the curve without FRA was 1.4°, for curve without PRF was 14.3°, and for the full model was 15.0°. Results from fixed threshold models did not differ qualitatively, producing phases of
When the simulation was run at higher baseline firing rates to avoid cutoff, the PRF conductance contributed very little to the sinusoidal firing response. In fact, at higher firing rates virtually no change was seen when the PRF conductance was left out of the simulation altogether (data not shown). Further analysis of the activation time course of the simulated conductances during the sinusoidal cycle revealed that the adaptation conductance was maximally activated shortly after the peak in firing rate, whereas the PRF conductance activated maximally during the rising phase of the sinusoidal firing rate.
These findings indicate that the observed phase lead in neurons with strong PRF and FRA was primarily caused by mechanisms underlying adaptation. Thus, whereas FRA and PRF were strongly correlated in MVN neurons, adaptation conductances dictated temporal firing dynamics to a larger extent than did rebound conductances. This is consistent with the pharmacological dissociation of PRF and FRA shown in earlier figures: block of IH affected PRF, but not FRA and phase leads. Finally, at higher firing rates response phase appears to be entirely governed by FRA.
DISCUSSION
This study investigated temporal firing dynamics in vestibular nucleus neurons and showed that intrinsic firing responses to hyperpolarizing, depolarizing, and sinusoidally modulated input currents vary continuously across neurons. Large, multipolar neurons tend to have strong postinhibitory rebound firing, pronounced spike frequency adaptation, and in response to sinusoidal inputs, exhibit a phase lead with respect to small, nonadapting neurons that lack rebound firing. Pharmacological and modeling analyses revealed that at least two types of ionic currents confer dynamics onto vestibular nucleus neurons: firing rate adaptation is mediated, at least in part, by a slow potassium current, and the same current, together with IH, appears to mediate rebound firing. Variations in response phase correlate both with rebound firing and with adaptation, but are conferred predominantly by ionic currents that underlie adaptation. These findings imply that the heterogeneity of neuronal firing dynamics observed in intact animals in response to head movement stimuli reflect intrinsic membrane conductances as well as differences in synaptic inputs.
Implications for interpreting response dynamics recorded in vivo
When challenged with sinusoidally modulating head movements, vestibular nucleus neurons recorded in awake, behaving animals respond with sinusoidal modulations in firing rate that vary widely in response phase with respect to head velocity. Vestibular nerve afferents also exhibit a range of phases in response to head movements (Goldberg and Fernandez, 1971
Potential identity of neurons with distinct firing dynamics
Neurons in the vestibular nuclei are heterogenous with respect to their synaptic inputs and their projection patterns, as well as their function in vestibular and oculomotor processing (Highstein, 1988
Of the relatively few studies that describe the morphology of vestibular nucleus neurons that have been functionally identified in vivo, two classes of neurons with large multipolar dendrites have been identified: neurons that project to the oculomotor nucleus via the ascending tract of deiters (ATD) (Reisine et al., 1981
Postinhibitory rebound firing has been observed in the subset of MVN neurons that receive powerful synaptic inhibition from the cerebellar flocculus [flocculus target neurons (FTNs)] (Kawaguchi, 1985
Ionic currents underlying intrinsic firing dynamics
A number of ionic currents have been identified previously in MVN neurons. These include calcium and sodium currents responsible for plateau potentials (Serafin et al., 1991b
Our results indicate that firing response adaptation in MVN neurons is at least partially mediated by the summation of slow K+ currents that are activated during each action potential and progressively hyperpolarize the membrane during repetitive firing. A potassium-dependent AHP followed the decline in firing evoked by sustained depolarization and was well correlated with the extent of adaptation. Two predominant types of slow K+ currents have been shown to be activated during repetitive firing by calcium and sodium influx, respectively. Ca2+-dependent K+ currents are responsible for firing rate adaptation in some neurons (Madison and Nicoll, 1984
