SK and Kv4 Channels Limit Spike Timing Perturbations in Pacemaking Dopamine Neurons

Measuring the PRC

The primary effect of a brief synaptic input to a pacemaking neuron is a change in length of the ISI during which that input arrives (Fig. 2A), which shifts the phase of the following spike train. The PRC measures the sensitivity of the phase shift of the neuron to current arriving at each phase of the ISI. The sensitivity is defined as the relative ISI change per unit of charge injected, in cycles/pA-s or cycles/pC. These units are equivalent to those for the slope of the firing frequency–current curve, and thus represent sensitivity in the same sense but on a finer timescale. The PRC provides a first-order description of the neuron’s input–output relationship (Stiefel and Ermentrout, 2016).

Although the PRC is traditionally defined by the effects of single, brief inputs, noise stimuli (Fig. 2B) provide a much more efficient method for PRC estimation. Using noise stimuli, hundreds of thousands of stimulus pulses comprising the noise can be delivered during a stable recording, providing more precise PRC estimates despite the natural variation of ISI length caused by intrinsic ion channel noise. Assuming the neuron’s average phase trajectory during the noise-perturbed ISIs is approximately linear, we can estimate phase as the time fraction of the present ISI traversed and determine the PRC by multiple linear regression, where the independent variables represent charge injected in subdivisions of each ISI (i.e., phase bins; Fig. 2C). and the dependent variable is ISI length (see Materials and Methods). The charge injection data for an example analysis are illustrated by the density plot in Figure 2D. In the example, it is apparent that shorter ISIs were associated with greater positive charge injected, and this association varied according to the phase of the ISI at which the charge was delivered. The PRC obtained from this cell is shown in Figure 2E (left panel). The example PRC has a distinctive shape, with a broad body rising to a peak at a phase of ∼0.2 and then declining, followed by a sharp peak at late phase. This shape indicates that the neuron was relatively sensitive to input arriving at early phases, then lost sensitivity, and then gained sensitivity near the end of the ISI. The variation of sensitivity across the firing cycle represented by the PRC determines the spike-time responses of the neuron to time-varying inputs.

In addition to altering the present ISI, a brief input might also change subsequent ISIs by producing changes in ion channel gating that are not reset by the next action potential or by altering the carryover of currents activated by previous spikes. The effects on the lengths of the following two ISIs can be characterized by second-order and third-order PRCs analogous to the first-order PRC described above (Canavier and Achuthan, 2010). We measured the first-, second-, and third-order PRCs by extending the analysis to include, as independent variables, the charge injected in a set of bins spanning three ISIs (Fig. 2E). In the example cell, we see that the second-order PRC had a distinct shape that was neither a replica nor a mirror image of the first-order PRC, suggesting that it did not arise entirely from the primary effect on ISI length. Instead, the second-order PRC was largely negative (showing an increase in the second ISI by depolarizing input, consistent with typical spike frequency adaptation) but had a sharp positive peak at the end. Thus, depolarizing input arriving only at the latest phases shortened two successive ISIs. The mechanism of this effect is not known, but it is reminiscent of the observations of Kang and Futami (1999), who showed that suprathreshold activation of subthalamic excitatory inputs to SNc DA neurons could generate a short-latency action potential without producing a large change in the next spike time, thereby shortening two ISIs. The third-order PRC was much smaller in amplitude, approaching the noise level of the measurement, but appeared to resemble the second-order PRC. Thus, the major effects of brief current input appeared to be restricted to two ISIs.

DA and GABA neuron PRCs

Using the methods described above, PRCs were obtained from 43 SNc DA neurons and 6 GABA neurons encountered in the same area (in SNc or at the SNc border with pars reticulata). The two cell types were segregated by their spontaneous firing rates and spike widths (Fig. 3A–C). PRCs of individual DA neurons were heterogeneous in amplitude and shape, illustrated by the six examples shown in Figure 3D (top row). As simple measures of PRC properties, we determined the mean amplitude of the primary PRC across the 40 phase bins, and the center-of-mass phase. For the center-of-mass measurement, we excluded the first two bins (representing phases of 0–0.05) and the last two bins (phases of 0.95-1) to remove the final peak, the size of which appeared to be largely independent from the main body of the PRC. A center of mass of <0.5 indicates a left-weighted PRC (higher sensitivity at early vs late phases), and a center of mass >0.5 indicates a right-weighted PRC. The heterogeneity of DA neuron PRCs indicates that these cells would spike at different times even if they were to receive identical synaptic input, and even if their natural pacemaking rates were the same.

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Figure 3.

DA and GABA neuron PRCs. A, Typical examples of spontaneous firing in DA and GABA neurons. B, Average action potential waveforms in the example cells. C, Scatter plot of mean spontaneous firing rate versus spike width at half-height for DA neurons and GABA neurons. Gray lines indicate a spike width of 0.6 ms and a firing rate of 10 Hz. The DA and GABA neurons in this sample could be separated by either criterion. D, Example primary PRCs from individual DA neurons (top row) and GABA neurons (bottom row), each sorted by their center of mass (left to right). The gray lines superimposed on each PRC indicate the center of mass (vertical line) and the mean value (horizontal line). E, Average first-order, second-order, and third-order PRCs of DA and GABA neurons. Error bars indicate the SEM. F, Scatter plot of the mean PRC value versus the center-of-mass phase for the first-order PRCs of DA neurons (blue symbols) and GABA neurons (red symbols).

PRCs of GABA neurons were measured in the same manner, but a weaker noise stimulus (pulse length, 0.5 ms; SD, 40 pA) was used to avoid excessive perturbation of their ISI lengths, which might compromise the assumption that phase can be estimated as the time fraction of the ISI traversed. The GABA neuron PRCs are plotted in Figure 3D (bottom row), sorted by the center of mass. It is apparent that the GABA neurons had higher overall sensitivity (indicated by the mean PRC amplitude) than the DA neurons (p = 0.00008, Mann–Whitney U test), and their PRCs were weighted toward later phases (p = 0.00002, t test), showing increasing sensitivity across most of the ISI. Two of the GABA neuron PRCs had a small negative lobe, indicating that depolarizing input at early phases lengthened the ISIs, and so would be classified as type 2 (Hansel et al., 1995). PRCs with similar shapes, but generally without a negative lobe, were reported previously in studies of substantia nigra pars reticulata neurons (Simmons et al., 2018, 2020).

The average first-order, second-order, and third-order PRCs of DA and GABA neurons are shown in Figure 3E. Although few individual DA neurons had flat PRCs, the average DA neuron PRC was nearly flat except for the first point (representing phases of 0–0.025) and the last two points (phases of 0.95–1). In contrast, the average GABA neuron PRC sloped upward across most of the firing cycle before falling toward zero in the last two points. This final falloff is expected because neurons generally lose sensitivity as the action potential begins to initiate, but it is likely not resolved for the DA neurons because each of the 40 bins into which the ISI was divided represented a much longer time for these slower-firing cells. The second-order PRCs of GABA neurons also differed from those of DA neurons, being entirely negative in sign, indicating that depolarizing input always lengthened the following ISI. The third-order PRCs nearly vanished for both cell types, indicating that the major effects of brief current inputs were limited to two ISIs.

The measures of DA and GABA neuron first-order PRCs are compared in Figure 3F, showing the lower overall sensitivity and more symmetrical PRC shapes of DA neurons. This comparison indicates that the different pacemaking mechanisms present in the two cell types produce striking differences in input sensitivity and PRC shapes, in addition to the well established difference in autonomous firing rates.

Membrane potential responses underlying the PRC

In theory, a pacemaking neuron repeatedly traverses a closed trajectory, or limit cycle, in a state space defined by the V_m and numerous other state variables (i.e., ion channel gating variables) that are not experimentally observable. We may, however, gain insight about the mechanisms that shape the PRC by observing how a perturbation at a given phase changes the V_m trajectory leading up to the next action potential. For example, voltage perturbations in pacemaking subthalamic neurons do not relax as they would in a resting neuron. Rather, a depolarizing input essentially advances the cell to a later point in the natural trajectory (Farries et al., 2010).

A major obstacle to measuring small perturbations of the V_m trajectory is that interspike trajectories vary because of intrinsic ion channel noise and spontaneous synaptic input. To overcome this, we measured the average effects of thousands of perturbations produced by the noise stimuli used to determine the PRC. To minimize voltage artifacts caused by imperfect bridge balance and capacitance compensation, all the data used for this analysis were subjected to post hoc bridge balance recorrection and capacitance artifact removal. For each perturbation phase examined, we determined whether the noise pulse being delivered at that fraction of the ISI was positive or negative. The V_m trajectories for all ISIs with a positive noise pulse at the specified phase (approximately half of the ISIs) were sampled at 1000 equally spaced time points spanning the ISI, and the times relative to the first spike and the V_m values were averaged. This analysis was performed for a set of perturbation phases (0.1–0.9 at increments of 0.1) across the firing cycle. To reduce noise further, grand average perturbed trajectories were obtained from the entire sample of neurons for every perturbation phase, and these were compared with the grand average of all trajectories (Fig. 4A, top).

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Figure 4.

Membrane potential trajectory responses to current pulses within the noise stimulus. The interspike V_m trajectories shown are grand averages for the entire sample of DA or GABA neurons. A, Top, Perturbed trajectories of DA neurons, each selected for the presence of a positive current pulse at a specified phase (0.1–0.9, color coded blue to red), compared with the average of all trajectories during noise stimulation (black). Bottom, Difference trajectories obtained by subtracting the overall average trajectory from each perturbed trajectory. The strong relaxation of the initial V_m perturbations led to very small changes in ISI length despite initially substantial depolarizations. B, Perturbed trajectories and difference trajectories of GABA neurons. Compared with the DA neuron data, these responses showed less relaxation, particularly in the second half of the ISI, which was associated with larger relative changes in ISI length for a given initial V_m change. The magnitude of V_m change is not directly comparable to the DA neuron data because of the smaller, briefer noise pulses applied. C, Estimation of the moving equilibrium potential (V_eq) from the time course of V_m relaxation after positive perturbations in DA neurons (same data as in A). V_eq was estimated as the value of V_m at which dV_m/dt reached zero before the trajectory resumed its upward course (green symbol). These data suggest that V_eq differed by only a fraction of a millivolt from the natural trajectory across most of the firing cycle. D, Example of difference in V_m relaxation between pacemaking (no holding current) and resting (−100 pA holding current). The top left traces show the average V_m responses after current pulses within the noise stimulus delivered during pacemaking, excluding pulses in the first 10 ms and the last 50 ms of each ISI; the red trace is the average for positive pulses, and the gray trace is the average for all pulses. The bottom left traces show the average V_m responses after current pulses within the noise stimulus delivered while the cell was held at rest; the blue trace is the average for positive pulses, and the black trace is the average for all pulses. The right graph shows the difference trajectories during pacemaking (red) and at rest (blue). The slower relaxation of V_m perturbations seen at rest indicates that the fast relaxations shown in A and C are not artifactual, but instead depend on membrane conductances activated during pacemaking, which cause the total membrane conductance to exceed the resting level.

To examine the trajectory changes, the overall grand average trajectory was subtracted from each perturbed trajectory to produce difference trajectories (Fig. 4A, bottom). At most phases, the initial V_m change caused by the pulse relaxed almost but not quite completely to the natural trajectory, leaving a small offset that was evidently responsible for the small change in spike time produced. In contrast to the DA neurons, GABA neurons showed less relaxation of the V_m changes, particularly at late phases (Fig. 4B). These data suggest that the higher overall sensitivity and right-weighted PRCs of GABA neurons arise from interspike V_m trajectories that retain more of the initial deflection from the natural trajectory, particularly in the later portion of the ISI.

These data indicate that although DA neurons are integrator like in the sense of having, on average, relatively constant sensitivity across the ISI, they integrate only a small fraction of their current input, discarding the rest to return near their natural trajectory. On a short timescale, they respond to V_m perturbations as though they were at a stable equilibrium potential although they are actively pacemaking. After depolarizing current pulses, the equilibrium potential can be estimated as the value of V_m where the slope of the trajectory (dV_m/dt) falls to zero before resuming the slow depolarization toward the next action potential (Fig. 4C). This estimate is not exact because dV_m/dt also depends on axial current flow among the compartments of the neuron. However, qualitatively, the observation of fast V_m relaxations to an apparent equilibrium potential near the natural trajectory suggests that the phase of a perturbed DA neuron (as defined by the time to its next spike in the absence of further input) does not depend primarily on the present value of V_m. Instead, the phase must depend on slowly varying ion channel gating variables that set the equilibrium potential at each phase and are altered only slightly by brief changes in V_m. Some of these variables are considered later in the Results.

To alleviate concern that the fast V_m relaxations are in some way artifactual and might not result from the membrane conductances active during pacemaking, we perturbed three DA neurons with noise stimuli during pacemaking and while the neuron was held at rest with a negative holding current (−100 pA). An example cell is illustrated in Figure 4D. In the case of pacemaking, the V_m responses were averaged for positive pulses and for all pulses at all phases of the ISI excluding the first 10 ms and the last 50 ms. At rest, the responses were averaged for positive pulses and for all pulses across each episode of noise perturbation. This analysis demonstrated that V_m perturbations relaxed much faster during pacemaking than while the neuron was held at rest. Thus, the fast relaxations observed during pacemaking result from increased membrane conductance relative to the resting state, which pulls V_m toward a moving equilibrium potential.

Effects of firing rate on DA neuron PRCs

The effect of a brief input on the phase of a pacemaking neuron may be expected to vary according to the firing rate of the cell. If the change in cycle duration caused by a given charge injection at a particular phase were constant, the PRC, expressed in cycles of phase advance per unit charge injected, would scale in proportion to the firing rate. However, this type of scaling is seldom observed in real neurons or in their biophysical models. Instead, the relationship between firing rate and the PRC depends on the unique, rate-dependent pacemaking dynamics of each neuron, which vary substantially among neurons of different types. PRCs of some neurons are relatively insensitive to rate changes, whereas the PRC sizes and shapes of other cell types are strongly rate dependent (Gutkin et al., 2005; Tsubo et al., 2007; Phoka et al., 2010; Wilson et al., 2014; Couto et al., 2015; Simmons et al., 2018).

To examine the relationship between firing rate and the PRC in our sample of DA neurons, we plotted the mean sensitivity and PRC center of mass against the mean firing rate during noise perturbation (Fig. 5A,B). These data show that the mean sensitivity was strongly correlated with the mean rate (r = 0.56, p = 0.00002^a, Pearson correlation test; Table 1), whereas the center of mass showed no significant relationship with rate (r = −0.16, p = 0.31).

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Figure 5.

Effects of firing rate on DA neuron PRCs. A, Mean PRC value versus mean firing rate during noise perturbation for each cell. The superimposed line is the linear regression. Asterisks indicate p < 0.0001 (Pearson correlation test). B, PRC center of mass versus mean firing rate. C, Average PRCs of five DA neurons tested with alternating episodes of noise stimulation with and without a +50 pA direct current added to the noise. The error bars indicate SEM. D, Mean value of each PRC without and with the direct current (p < 0.05, paired t test). E, Center of mass of each PRC without and with the direct current.

To examine the effect of changing the firing rate on the PRC in individual DA neurons, we alternated episodes of noise stimulation with and without a superimposed +50 pA direct current injection, which increased the mean firing rates from 3.11 spikes/s (SD, 1.07) to 5.56 spikes/s (SD, 1.63; n = 5 cells). The average PRCs with and without the added current are shown in Figure 5C, and the mean PRC values and centroids of each cell without and with the direct current are compared in Figure 5, D and E. Raising the firing rate by direct current injection increased the mean sensitivity measured by the PRC (control: 0.053 cycles/pA/s; SD, 0.019; +50 pA: 0.080 cycles/pA/s; SD, 0.035; p = 0.03^b, paired t test). In contrast to the effects of natural heterogeneity of pacemaking rates, raising the firing rates of individual neurons also changed the PRC center of mass (control: 0.50; SD, 0.06; +50 pA: 0.43; SD, 0.05; p = 0.04^c, paired t test). These results suggest that the PRC shape may be affected differently by the natural mechanisms of pacemaking rate heterogeneity than by a direct current. The mechanisms responsible for the rate dependence of the PRC are not known, but some of the interactions may be explained by the conductances discussed below.

Effects of SK channels on DA neuron firing activity and PRCs

Although inward sodium and calcium currents are essential for pacemaking, they are not sufficient to produce the slow firing and clock-like regularity of SNc DA neurons in brain slices. The main slow variables that govern the pacemaking clock are the ones that determine the activation and inactivation of potassium conductances. SK channels produce a major component of the AHP and are essential for the slow rate and regularity of pacemaking in SNc DA neurons (Gu et al., 1992; Wolfart et al., 2001; Ji et al., 2009; Deignan et al., 2012; Iyer et al., 2017). SK channels in DA neurons respond to widespread increases in intracellular calcium rather than being coupled tightly to sites of calcium influx (Wilson and Callaway, 2000), and, thus, the time course of intracellular calcium elevation is one of the critical slow variables that governs pacemaking.

To investigate the effects of SK channels on DA neuron PRCs, we first applied the SK/IK (intermediate-conductance KCa) channel positive modulator NS309 (Ji et al., 2009; Christophersen and Wulff, 2015) at a concentration of 1 μm. Because NS309 was dissolved in DMSO, the same amount (0.1%) was added to the control ACSF. An example of the effect of NS309 on firing at baseline and during noise perturbation is shown in Figure 6A. NS309 caused a deepening of the AHP, demonstrated by the average interspike trajectories of the example cell (Fig. 6B). NS309 lowered the firing rates of all the neurons tested (control: 3.66 spikes/s; SD, 1.32; NS309: 2.32 spikes/s; SD, 0.67; n = 9; p = 0.002^d, paired t test; Fig. 6C) and initially reduced the CV of the baseline ISIs (control: 0.035; SD, 0.023; NS309: 0.020; SD, 0.010; p = 0.02^e, paired t test; Fig. 6D). However, in some cells NS309 eventually disrupted pacemaking, causing missed beats during which spiking was replaced by a subthreshold oscillation (Fig. 6E). Because this phenomenon is not compatible with PRC analysis, which requires rhythmic firing, the data used for PRC measurement were limited to the period of NS309 wash-in before missed beats were observed. The average effect of NS309 on the PRC is shown in Figure 6F. NS309 reduced sensitivity at all phases of the ISI, but the effect was largest at early phases, consistent with an enhanced SK conductance activated during and/or shortly before each action potential and decaying across the ISI. This increased conductance presumably acts to shunt the external current input, reducing its ability to alter the ISI length. On average, NS309 reduced the PRC mean from 0.081 cycles/pA/s (SD, 0.051) to 0.028 cycles/pA/s (SD, 0.015; p = 0.006^f, paired t test; Fig. 6G) and shifted the center of mass from 0.51 (SD, 0.05) to 0.65 (SD, 0.12; p = 0.003^g, paired t test; Fig. 6H).

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Figure 6.

Effects of the SK channel positive modulator NS309 on DA neuron firing activity and PRCs. A, Pacemaking of an example neuron in control ACSF and in the presence of 1 μm NS309. The noise stimulus (top) was identical for the two episodes shown. B, Average baseline interspike V_m trajectories of the example cell in control ACSF and with NS309, showing the enhancement of the AHP. C, Mean baseline firing rates in control ACSF and with NS309 (p < 0.05, paired t test). D, CV of baseline ISIs in control ACSF and with NS309. These data, and the mean firing rate and PRC data, were limited to the periods of NS309 application when pacemaking remained regular. E, Example of a missed beat, or spontaneous transition to subthreshold oscillation, in the example neuron later during the application of NS309. Data showing this phenomenon were not used for PRC analysis. F, Average PRCs in control ACSF and with NS309. Error bars indicate SEM. G, Mean PRC values in control ACSF and with NS309 (p < 0.05, paired t test). H, PRC center of mass in control ACSF and with NS309.

As a second experiment to investigate the effects of the SK channel conductance, we applied the selective blocker apamin. A subsaturating concentration of 1 nm was chosen because it reduced the AHP and increased the firing rate but did not cause burst firing, which is often observed with higher concentrations and would preclude PRC analysis. An example of the effect of apamin on firing at baseline and during noise perturbation is shown in Figure 7A. Apamin increased the baseline firing rates from 3.09 spikes/s (SD, 1.12) to 4.70 spikes/s (SD, 2.21; n = 5; p = 0.04^h, paired t test; Fig. 7B). Apamin also increased the baseline CV_ISI in each cell tested (control: 0.028; SD, 0.014; apamin: 0.138; SD, 0.090), but this effect did not reach significance (p = 0.051, paired t test; Fig. 7C).

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Figure 7.

Effects of the partial block of SK channels. A, Pacemaking of an example neuron in control ACSF and in the presence of 1 nm apamin. The example traces were obtained with the same noise stimulus (top) in both conditions. B, Mean baseline firing rates in control ACSF and with apamin (p < 0.05, paired t test). C, CV of baseline ISIs in control ACSF and with apamin. D, Average interspike V_m trajectories of the example cell in control ACSF (black line) and with apamin (red line). Apamin reduced both fast and medium components of the AHP. E, Average PRCs in control ACSF (black) and with apamin (red). Error bars indicate the SEM. F, Mean PRC values in control ACSF and with apamin. G, PRC center of mass in control ACSF and with apamin.

Examination of the average interspike V_m trajectories (Fig. 7D) suggested that, in addition to its direct effect on the SK channel-mediated medium AHP, which is typically apparent as a hyperpolarization reaching a minimum V_m tens of milliseconds after an action potential, apamin also reduced a faster component of the AHP representing the final phase of action potential repolarization. This effect is likely secondary to increased sodium and potassium channel inactivation produced by the higher overall interspike V_m, which typically reduces both action potential height and fast AHP depth.

Based on the contribution of SK channels to the medium AHP, as well as the effects of NS309 described above, we expected that apamin would affect the PRC most strongly at early phases of the ISI. However, our data showed that apamin greatly increased the PRC across all phases except for the rightmost point where the control PRCs peaked (Fig. 7E). On average, apamin increased the PRC mean from 0.075 cycles/pA/s (SD, 0.047) to 0.222 cycles/pA/s (SD, 0.120; p = 0.03ⁱ, paired t test; Fig. 7F) but did not change the center of mass (control: 0.46; SD, 0.04; apamin: 0.51; SD, 0.07; p = 0.22, paired t test; Fig. 7G). There are two general explanations for the large effects of apamin (and, to a lesser extent, NS309) extending to late phases despite SK channels acting primarily to produce the AHP at early phases. First, SK channels may normally be activated across the entire ISI by the levels of calcium present in some compartments of the neuron during pacemaking (Wilson and Callaway, 2000). Second, the AHP produced by SK channels may indirectly affect the sensitivity at later phases by altering the availability of other conductances that are activated later in the firing cycle.

Effects of an artificial AHP conductance

To investigate whether a defined AHP conductance active early but not late in the ISI could affect the PRC at late phases, we applied an artificial AHP conductance to real DA neurons using the dynamic clamp technique (see Materials and Methods). The artificial AHP was incremented by 4 nS upon the detection of each spike, decayed with a time constant of 50 ms (thereby becoming small within the first half of a typical ISI), and had a reversal potential of −90 mV. Episodes of noise stimulation with and without the artificial AHP were alternated.

The effect of the artificial AHP on firing is illustrated in Figure 8A. The added spike-triggered conductance deepened the medium AHP (Fig. 8B), slowed the firing rate, and reduced the CV of baseline ISIs. The baseline firing rates were lowered from 4.13 spikes/s (SD, 1.51) to 3.04 spikes/s (SD, 1.31; n = 8; p = 0.002^j, paired t test; Fig. 8C), and the CVs decreased from 0.050 (SD, 0.026) to 0.028 (SD, 0.010; p = 0.03^k, paired t test; Fig. 8D). Surprisingly, although the added AHP conductance decayed almost completely within each ISI, it greatly reduced the PRC across early and late phases (Fig. 8E). The mean PRC values decreased from 0.088 (SD, 0.051) to 0.030 (SD, 0.009; p = 0.013^l, paired t test; Fig. 8F), while the PRC center of mass showed no significant change (Fig. 8G). Overall, the effects of the artificial AHP were opposite to those of apamin and resembled those of NS309. The artificial AHP data showed that a change in the medium AHP can have secondary effects that alter sensitivity at later phases.

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Figure 8.

Effects of an artificial AHP conductance. A, Pacemaking of an example neuron in control conditions (left) and with the artificial AHP (right). The top trace is the noise stimulus, and the middle right trace shows the added AHP conductance triggered by each spike. B, Average interspike V_m trajectories of the example cell without and with the artificial AHP. The top panel shows the time course of the artificial AHP conductance. C, Mean baseline firing rate of each cell without and with the artificial AHP (p < 0.05, paired t test). D, CV of baseline ISIs without and with the artificial AHP. E, Average PRCs without and with the artificial AHP, showing a large reduction of sensitivity at early and late phases. F, Mean PRC values without and with the artificial AHP. G, PRC center of mass without and with the artificial AHP.

Effects of Kv4 channels

In the later portion of the ISI, the major hyperpolarizing conductance in midbrain DA neurons shifts from SK to the A-type potassium conductance mediated primarily by Kv4 channels (Liss et al., 2001; Khaliq and Bean, 2008; Tarfa et al., 2017; Haddjeri-Hopkins et al., 2021). To determine the effects of the Kv4 conductance on the PRC, we applied the scorpion toxin AmmTx3 at 100–200 nm, a concentration range expected to produce partial block of Kv4 channels (Vacher et al., 2002; Amendola et al., 2012; Maffie et al., 2013; Pathak et al., 2016). The best characterized effect of Kv4 channels in DA neurons is a long delay to the first spike after offset of a hyperpolarizing current. Consistent with previous studies using selective toxins to inhibit Kv4 channels (Liss et al., 2001; Khaliq and Bean, 2008; Haddjeri-Hopkins et al., 2021), AmmTx3 greatly reduced this rebound delay (Fig. 9A,B). On average, the delay was 279 ms (SD, 190) in control ACSF and 49 ms (SD, 49) in the presence of the toxin (n = 6, p = 0.04^m).

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Figure 9.

Effects of partial block of Kv4 channels. A, Effect of AmmTx3 (200 nm) on the rebound delay after a 1 s hyperpolarizing current step. The long delay and ramp-like V_m trajectory seen in control ACSF (left) were eliminated in the presence of AmmTx3 (right). B, Effect on AmmTx3 on the maximum rebound delay of each neuron, measured as the time to the first spike after the end of a −200 pA, 1 s step. For all panels, blue symbols indicate cells tested with 100 nm AmmTx3, and red symbols indicate 200 nm. C, Pacemaking of an example neuron in control ACSF and in the presence of AmmTx3 (100 nm). The control and AmmTx3 data shown were obtained with the same noise waveform. D, Mean baseline firing rates in control ACSF and with AmmTx3 (p < 0.05, paired t test). E, CV of baseline ISIs in control ACSF and with AmmTx3. F, Average PRCs in control ACSF and with AmmTx3. Error bars indicate SEM. G, Mean PRC values in control ACSF and with AmmTx3. H, PRC center of mass in control ACSF and with AmmTx3.

An example of the effects of AmmTx3 on pacemaking at baseline and during noise perturbation is shown in Figure 9C. In addition to its effect on firing rate, AmmTx3 replaced the ramp-like portion of each interspike V_m trajectory with a faster curve upward toward the next action potential, presumably reflecting the reduced rebound delay mechanism. AmmTx3 increased the baseline firing rates from 3.49 spikes/s (SD, 1.12) to 4.67 spikes/s (SD, 0.99; n = 6; p = 0.03ⁿ, paired t test; Fig. 9D). AmmTx3 also increased the baseline CV_ISI in each cell tested (control: 0.033; SD, 0.018; AmmTx3: 0.090; SD, 0.101), but this effect was not significant (p = 0.20, paired t test; Fig. 9E). The major effect of AmmTx3 on the PRC was increased sensitivity at middle to late phases, except for the last point (Fig. 9F). AmmTx3 increased the PRC mean (control: 0.070 cycles/pA/s; SD, 0.029; AmmTx3: 0.119 cycles/pA/s; SD, 0.072; p = 0.046°, paired t test; Fig. 9G) and shifted the center of mass from 0.49 (SD, 0.06) to 0.60 (SD, 0.13; p = 0.03^p, paired t test; Fig. 9H).

Based on a previous study using voltage-clamp ramp protocols to simulate the interspike membrane potential trajectory in VTA DA neurons (Khaliq and Bean, 2008), we would predict that the active Kv4 conductance is largest in the middle to late portion of the ISI. Our data show increased PRC values at corresponding phases, suggesting that the Kv4 conductance acts directly to shunt external current inputs arriving at these phases, limiting the changes in spike time. In this way, the Kv4 conductance appears to fill the gaps between the periods of high SK conductance during the AHPs. On average, this results in an almost constant, low sensitivity to external input across nearly all phases.