Complex behaviors often depend on temporally precise neuronal firing that coordinates network activity at brain levels ranging from cortical microcircuits to hindbrain pattern generators (Llinás, 2014; Kros et al., 2017; Sober et al., 2018). Mechanisms known to increase precision at single cell and network levels include, for instance, feed-forward inhibition in auditory circuits (Grothe, 2003), recurrent inhibitory input in cerebral cortex (Kapfer et al., 2007), and neuronal synchrony in cortical and sensory neurons (Tiesinga and Sejnowski, 2001; Uhlhaas et al., 2010). Synchronous, concurrent activation of neurons is widely distributed in the brain (Llinás, 2014) and especially important for behaviors requiring both rapid and precise motoneuron activation such as electrogenesis in fishes (Bennett, 1971) and vocalization in fishes (Chagnaud et al., 2012) and tetrapods (Mead et al., 2017; Kwong-Brown et al., 2019).

Several mechanisms by themselves or in combination contribute to neuronal synchrony: coherent excitatory firing, electrotonic coupling, and inhibitory input (Singer, 1999; Uhlhaas and Singer, 2006; Kapfer et al., 2007). While coherent (i.e., phasic) input leads to neuronal coupling mainly by excitation, inhibition might be the predominant way to synchronize activity (Van Vreeswijk et al., 1994). Electrotonic coupling enhances synchrony by spreading voltage changes, for example, during synaptic inputs, that lead to concomitant membrane potential changes within an electrotonic, interconnected population (Bennett and Zukin, 2004; Pereda, 2014).

Unlike motor systems controlling locomotion and respiration (Ramirez and Baertsch, 2018; Grillner and El Manira, 2020), in-depth inquiries of cellular and network properties of brainstem neurons influencing acoustic signaling remain relatively unexplored, despite advances in characterizing the musculoskeletal periphery (Mead et al., 2017; Kwong-Brown et al., 2019; Riede et al., 2019; Bowling et al., 2020). A neurobehavioral challenge often facing soniferous species is fine temporal control of rapid modulations of acoustic waveforms.

The vocal network of toadfishes, a marine order of teleosts that is highly dependent on acoustic signaling for social interactions and successful reproduction, exhibits unusually high levels of synchronous activity, making it ideal for investigating mechanisms underlying precise neuronal firing (reviewed in Pappas and Bennett, 1966; Bass et al., 2015). This includes Gulf toadfish (Opsanus beta), the species studied here, which produce two main types of vocalizations with pulse repetition rates (PRRs) in the range of ~200–250 Hz – broadband agonistic grunts and multiharmonic advertisement calls known as boatwhistles (Figure 1a, Tavolga, 1958; Winn, 1967; Maruska and Mensinger, 2009; Elemans et al., 2014; Bass et al., 2015).

Figure 1

Download asset Open asset

An experimental advantage of the vocal system of toadfishes and other teleosts (Bass and Baker, 1991) is that physical attributes of acoustic signals (PRR, fundamental frequency [F₀], duration, amplitude modulation) are directly established by a motor volley readily recorded intracranially from hindbrain occipital nerve roots, comparable to hypoglossal roots (Bass et al., 2008), in an intact neurophysiological preparation (Bass and Baker, 1990; Bass and Baker, 1991; Remage-Healey and Bass, 2006; Rubow and Bass, 2009). These roots give rise to the vocal nerve, which innervates a single pair of so-called ‘superfast’ muscles attached to the swim bladder that have physiological and molecular properties allowing them to generate power at frequencies 20–100-fold greater than fast and slow twitch muscle, respectively (avian syrinx, bat larynx and rattlesnake shaker also have superfast muscles) (Rome, 2006; Mead et al., 2017; Nelson et al., 2018). To achieve high contraction rates, toadfish superfast muscles require precise neural input provided by the vocal motor volley, termed fictive vocalization in electrophysiological preparations, that is a highly stereotyped, repetitive series of compound nerve potentials (VOC, Figure 1b) (motor volleys driving non-superfast muscles, e.g., those used in limb movement, show low temporal coincidence; McLean et al., 2007; Berkowitz, 2008; Chagnaud et al., 2012; Song et al., 2016). Individual VOC potentials arise from synchronous motoneuron activity in the midline vocal motor nucleus (VMN) and are matched 1:1 with individual motoneuron action potentials (APs) (Figure 1c; Bass and Baker, 1990; Chagnaud et al., 2012; Chagnaud and Bass, 2014) and synchronous contraction of superfast vocal muscles (Elemans et al., 2014). Each individual VOC potential represents the synchronous activity of VMN motoneurons; variable potential amplitude reflects different levels of synchronous motoneuron firing and motoneuron recruitment (Figure 1c; Chagnaud et al., 2012). VOC potential amplitude is easily quantified and serves as a convenient readout of the extent of VMN synchrony (Chagnaud et al., 2012). VOCs occur either spontaneously or can be evoked by brief trains of low amplitude, electrical microstimulation in midbrain sites comparable to the periaqueductal gray of birds and mammals (Kittelberger and Bass, 2013).

Motoneurons possess 3–5 main dendritic branches and an axon arising from a primary dendrite or soma that lacks collaterals and exits the brain ipsilaterally via the vocal tract (VoTr, Figure 1d, e) and joins the ipsilateral vocal nerve root (Bass and Baker, 1990; Chagnaud and Bass, 2014). Each VMN is bilaterally innervated by adjacent vocal pacemaker neurons (VPN, Figure 1d, e) that provide coherent excitatory input and determine VMN firing rate that directly translates into PRR or F₀ (see above) (Bass and Baker, 1990; Chagnaud et al., 2011; Chagnaud and Bass, 2014). Both VPN and VMN receive input from a more rostral vocal prepacemaker nucleus (VPP, Figure 1e) that encodes call duration (Chagnaud et al., 2011; Chagnaud and Bass, 2014).

Pappas and Bennett discovered the VMN (Pappas and Bennett, 1966) and showed, along with studies of electric fish (Bennett, 1971), the contribution of electrotonic coupling to motoneuronal synchrony. This included ultrastructure evidence for gap junction contacts between VMN motoneurons and axons of unidentified origin (likely VPN) (Pappas and Bennett, 1966; also see Bass and Marchaterre, 1989). More recent studies show that vocal nerve labeling with gap junction impassable tracers only leads to dense retrograde labeling of the ipsilateral VMN, while gap junction passable tracers lead to dense transneuronal, bilateral labeling of VMN, VPN, and VPP (e.g., see Figure 1d; Bass et al., 1994; Chagnaud and Bass, 2014). Besides electrotonic coupling, Pappas and Bennett, 1966 speculated that inhibitory input to the VMN arose from recurrent inhibitory activity (antidromic stimulation of vocal nerve led to hyperpolarization (HYP) at high, but not low, stimulation amplitudes). How this putative inhibition could be induced by activating motoneuron axons in the vocal nerve remained to be tested.

Recent studies of other motor systems provide a framework for Pappas and Bennett, 1966 observations by showing a role for electrotonic coupling between motoneurons and (inhibitory) premotoneurons in patterning vocalization in frogs (Lawton et al., 2017) and locomotion in adult zebrafish (Song et al., 2016) and larval flies (Matsunaga et al., 2017). We also recently reported in Gulf toadfish a subset of glycinergic VPN neurons transneuronally labeled with the gap junction passable tracer neurobiotin, strongly suggestive of electrotonic coupling within the vocal network (Rosner et al., 2018; see Figure 1f). Could the electrotonically coupled glycinergic premotoneurons be involved in the patterning of motoneuron firing in the toadfish vocal pattern generator, as shown for other motor systems?

Here, we took advantage of Pappas and Bennett, 1966 intracellular recording approach, especially the use of antidromic nerve stimulation to investigate electrotonic coupling, to reveal intrinsic and network properties underlying concurrent motoneuron firing in the VMN. We provide evidence demonstrating coherent, high-frequency excitatory input as well as inhibitory glycinergic input to VMN motoneurons contributing to coordinated network activity. We further show that a strong HYP in these motoneurons after spiking (also see Pappas and Bennett, 1966), not seen during intracellular current injection, enhances synchronized vocal network activity and that HYP amplitude directly depends on motoneuron population activation. Next, using pharmacology combined with antidromic stimulation, we provide evidence that glycinergic VPN neurons activated via electrotonic coupling can account for the HYP. In aggregate, these findings strongly suggest that the HYP is mediated by a glycinergic inhibition dependent on gap junctional coupling. Besides its potential involvement in motoneuronal patterning, as recently shown in locomotor systems (see above), this provides an adaptive mechanism to enhance temporal precision in the activation of acoustic signaling networks and perhaps time coding in other motor systems requiring high levels of precision.

All statistical results are summarized in a table in supplementary file 1 (see Materials and methods for details on the test used).

Synchronous motoneuron firing and post-spiking HYP

Midbrain stimulation led to membrane depolarizations in motoneurons that increased in amplitude until a single AP was fired (Figure 2a1, 2). This AP coincided with the first appearance of a strong post-spiking HYP and a single VOC nerve potential. With increasing stimulus strength, additional APs were detected that matched 1:1 with additional VOC potentials (Figure 2a3, 4). Each repetitive series of VOC potentials mimicked the temporal properties of sound pulses comprising natural grunts (see Figure 1a; Tavolga, 1958; Winn, 1967; Maruska and Mensinger, 2009; Elemans et al., 2014).

Figure 2

Download asset Open asset

Figure 2—source data 1

Comparison between motoneuron action potential and hyperpolarization amplitude during midbrain-evoked vocal and antidromic stimulation.

https://cdn.elifesciences.org/articles/59390/elife-59390-fig2-data1-v1.xlsx

Download elife-59390-fig2-data1-v1.xlsx

Across repetitions, the amplitude of the second VOC potential always exceeded the first (73.89 ± 4.4 µV vs. 36.6 ± 4.1 µV; n = 14 neurons; N = 4 fish; p<0.0001) (Figure 2a3–5). The reduced amplitude of the first VOC potential reflected a less synchronous and/or a partial activation of the motoneuron population (Figure 2a3). The first and last VOC potentials generally showed activity distributed over a broader time course (Figure 2b; also see inset, amplifying end of vocal nerve recording, e.g., of ‘weak’ synchrony). The amplitude of the first and last VOC potentials thus directly reflected the extent of synchronous motoneuron activation.

Intracellular motoneuron recordings showed broad depolarizations often present during the first and last VMN APs (Figure 2a3–5, b ) that often displayed spikelets strongly indicative of asynchronous motoneuron activity (Figure 2a4, b, c) (also see Chagnaud et al., 2012). The strong variability in amplitude of motoneuron APs and VOC potentials during a given VOC (e.g., Figure 2a2–5) was further reflected in different half-widths of the first APs riding on the broad depolarizations. These were significantly wider than those of subsequent APs whose amplitude correlated with a higher amplitude in the corresponding VOC potential (first AP half width: 0.81 ± 0.11 ms vs. second 0.62 ± 0.06 ms; n = 8, N = 3; p=0.001). As highlighted in Figure 2b (but also evident in Figure 2a4, c), larger, sharp-peaked VOC potential amplitudes (blue trace) are indicative of synchronous firing across the VMN population and correlated with narrower motoneuron APs (blue trace).

Antidromic activation via the vocal nerve (electrodes implanted in vocal muscles; see Materials and methods) revealed motoneuron APs lacking the prominent HYP at their respective threshold (blue trace, Figure 2c, right). This was consistent with intracellular square pulse current injections showing no clear HYP after AP firing (Figure 2d). Motoneuron AP and HYP amplitudes (relative to resting membrane potential [RMP]) were significantly larger during VOC activity than following antidromic activation at threshold for AP firing (AP: vocal 29.13 ± 1.48 mV vs. antidromic: 21.46 ± 2.93 mV; p=0.024; HYP: vocal −10.92 ± 1.16 mV vs. antidromic −2.95 ± 0.70 mV; n = 12, N = 5: p=0.001, see Figure 2c).

Motoneuron electrical coupling and HYP

To investigate the origin of different motoneuronal AP and HYP amplitudes observed for antidromic-evoked APs and those during VOC activity, motoneurons were stimulated antidromically at varying amplitudes via the ipsilateral vocal nerve root (ad-ipsi, blue traces; Figure 3). At low amplitudes, we detected small depolarizations (Figure 3) whose amplitude gradually increased with stimulation strength, that is, with increasing recruitment of motoneuron axons and with shapes and peak latencies indicative of electrotonic coupling (3.54 ± 0.28 ms; n = 9, N = 4). Collision experiments using antidromic-activated and intracellular-evoked APs (via intracellular current injection) revealed these APs could not be blocked, that is, they resulted from electrical coupling (Pappas and Bennett, 1966; Kiehn and Tresch, 2002, Figure 3—figure supplement 1).

Figure 3 with 3 supplements see all

Download asset Open asset

Figure 3—source data 1

Comparison between motoneuron action potential and hyperpolarization latency during midbrain-evoked vocal and antidromic stimulation.

https://cdn.elifesciences.org/articles/59390/elife-59390-fig3-data1-v1.xlsx

Download elife-59390-fig3-data1-v1.xlsx

With increasing amplitude of vocal nerve stimulation, the axon of the respectively recorded motoneuron was eventually recruited and an antidromic AP invaded the recorded motoneuron as shown by the significantly shorter peak latency (1.42 ± 0.31 ms after stimulation; n = 11, N = 4) compared to the previously mentioned subthreshold depolarization (p=0.003) (Figure 3). These APs showed no HYP, but instead were characterized by a slow decay (double exponential fit; average time constant τ1: 0.89 ± 0.25; time constant τ2: 7.76 ± 2.48; n = 5, N = 2) back to the RMP (example shown in magenta trace in Figure 3a). Surprisingly, an HYP started to appear with increasing antidromic stimulation amplitude (Figure 3). The AP and HYP peak amplitudes increased with stimulation strength until each reached a plateau (Figure 3a). A HYP was never observed during intracellular square pulse current injections (Figure 2d), raising the question on the origin of this HYP.

Phase plane plots of the membrane potential during ipsilateral antidromic activation revealed further changes in motoneuronal activity upon increasing stimulation amplitudes (Figure 3a, black traces). The gradual appearance of the HYP, together with the absence of the HYP upon initial AP firing ((Figure 3a)) suggested that a further recruitment of motoneurons via the antidromic stimulation underlies HYP generation ((Figure 3a)). Phase plane plots further revealed an additional component: a broadening of the depolarization after AP firing (magenta arrows, (Figure 3a)). As this depolarizing component was not present at the recruitment threshold, it cannot have originated from the gap junction-mediated coupling superimposed on the AP. In a few cases, this depolarizing component eventually led to a second AP firing (not shown).

A Renshaw cell-like recurrent inhibition in which spinal motoneurons use an axon collateral to activate a local inhibitory circuit (Renshaw, 1941; Eccles et al., 1954), comparable to the ‘apparently recurrent inhibition’ proposed by Pappas and Bennett, 1966 to account for inhibitory input to the VMN, could be ruled out as the origin of the HYP given the lack of motoneuron axon collaterals (Figure 1d, Chagnaud and Bass, 2014) and the rapid onset of the HYP. To exclude that a motoneuronal axon collateral could arise at the periphery and enter via one of the nearby dorsal roots, the dorsal roots were bilaterally cut in two experiments (Pappas and Bennett, 1966 used this method to show that antidromically evoked APs in motoneurons did not arise from afferents). There was no difference in HYP amplitude (% of baseline) between cut and uncut recordings during VOCs (before cut: −8.21 ± 3.1 mV vs. after: −7.51 ± 1.22 mV; n = 18, N = 2: p=0.8) (Figure 3—figure supplement 2) or during antidromic stimulation (before cut: −3.2 ± 0.59 mV vs. after: −4.84 ± 0.71 mV, n = 18, N = 2: p=0.117). These results excluded a motoneuronal collateral via one of the dorsal roots as the origin of the HYP.

Contralateral antidromic stimulation also revealed electrotonic potentials whose amplitude depended on stimulation strength (ad-contra, red traces; Figure 3a). Electrotonically mediated potentials eventually reached threshold and evoked an AP. The peak latency of these APs was significantly longer (3.47 ± 0.16 ms; n = 11, N = 4; p=0.004) than ones elicited ipsilaterally (Figure 3b), while the peak latency of the ipsilaterally evoked subthreshold depolarization did not differ, consistent with their common origin from electrotonic coupling (see above). As tract tracing and intracellular neuron fills showed that motoneurons only innervate the ipsilateral muscle (Chagnaud and Bass, 2014), electrical coupling alone is thus able to drive AP firing, independent of whether motoneurons belong to the ipsilateral or contralateral VMN population.

As with the ipsilateral antidromic activation, an HYP component could clearly be distinguished in the contralateral antidromic stimulation experiments (Figure 4a). This HYP occurred independent of AP firing, emphasizing the independence of the two events within a given motoneuron.

Figure 4

Download asset Open asset

Consistent with our findings in the closely related midshipman toadfish, Porichthys notatus (Chagnaud et al., 2012), the ability to initiate an AP via electrotonic coupling was in strong contrast to our intracellular current injections that failed to initiate an AP in most cases, even at high current intensities (>5 nA). In cases where intracellular current injection elicited an AP, motoneurons showed rapid adaptation of AP firing, likely due to weak somatic repolarization ability (see Figure 2d). Gulf toadfish are, however, able to repetitively contract their superfast vocal muscles for several hundred milliseconds (Figure 1a). How can the muscle achieve this if motoneurons cannot fire for extended time periods due to the rapid AP adaptation seen during square pulse current injections? To test whether the HYP was required to de-inactivate motoneurons, we stimulated the motoneurons in which current injection led to AP firing with pulse trains of different frequencies. In contrast to long (>50 ms) duration pulses (Figure 2d), motoneurons showed no signs of AP adaptation to pulse trains with brief (<5 ms) pulses, indicating the necessity of membrane repolarization for sustained motoneuron firing (Figure 3—figure supplement 3). Stimulation was reliable into the behaviorally relevant physiological range (the PRR/fundamental frequency of toadfish vocalizations) with train frequencies tested up to 110 Hz. The weak repolarization capability and low excitability of the motoneurons thus provide the means to prevent sustained AP firing, which would decrease the extent of firing synchrony and precision across the VMN population.

Network activity induces HYP

The presence of the HYP only at high antidromic stimulation amplitudes, that is, high levels of motoneuron recruitment, strongly suggested a network-dependent activation of the HYP. To test this hypothesis, we ipsilaterally evoked an AP antidromically in a VMN motoneuron (ad-ipsi) at low threshold stimulation (i.e., without an HYP), followed by stimulation of the contralateral nerve (ad-contra), which resulted in a small electrotonic depolarization (Figure 4a, b). Subsequently, the delay of this second stimulation was reduced up to the time point of the first ipsilateral nerve stimulation (Figure 4b). Once close to the antidromic AP, an HYP started to appear that increased in amplitude the closer the contralateral-evoked potential came to the ipsilateral-evoked antidromic AP (Figure 4b; heat map in Figure 4a; also see Video 1). These experiments suggested that an increase in overall depolarization in the vocal network is needed to generate the HYP. To test this hypothesis, we took advantage of the prominent, wide depolarization that often appeared in motoneurons at the end of a VOC (see Figures 1c and 2c). Similar to the above, we moved an ipsi- or contralateral, antidromically evoked depolarizing potential into this depolarization occurring at the end of a VOC (ad-ipsi and ad-contra, Figure 4c, d, respectively). With decreasing lag between the antidromically evoked potential and the depolarization during the VOC, an HYP started to appear in the contralateral-evoked potential that increased in amplitude (Figure 4c, d). This again showed the necessity of a network-wide depolarization in order to elicit the HYP.

Video 1

Download asset

Single motoneuron activity and HYP reflect network synchrony

To test the contribution of single motoneurons to network activity, we performed intracellular recordings of motoneurons using QX314 that blocks voltage-dependent sodium channels intracellularly (Yeh, 1978). After intracellular iontophoresis of QX314, motoneurons exhibited a small, but significant, decrease in AP amplitude compared to baseline conditions during VOCs (Figure 5a) (baseline: 31 ± 2.67 mV vs. QX314: 27.27 ± 2.53 mV; n = 7, N = 2; p=0.015). There was a much more prominent decrease in antidromically evoked AP amplitude relative to baseline (baseline: 21.92 ± 7.16 mV vs. QX314: 8.90 ± 6.82 mV; n = 7, N = 2; p=0.006) (Figure 5b). While a significant difference in the HYP during VOCs was observed (baseline: −9.87 ± 0.75 mV vs. QX314: −2.19 ± 0.95 mV; n = 7, N = 2; p=0.003), no significant change could be detected in the HYP following antidromic activation (baseline: −4.44 ± 3.57 mV vs. QX314: −4.3 ± 3.56 mV, n = 7, N = 2; p=0.703) (Figure 5a, b). This seemingly contradictory result is likely due to the electrotonic coupling of the network where other motoneurons contribute to the potential of individual motoneurons. Even though unlikely, due to the effect on the antidromic AP, an alternative interpretation is that an insufficient quantity of QX 314 was injected. Manipulation of only one out of the hundreds of motoneurons in VMN via QX314 injections is thus not sufficient to reveal the full extent of the HYP activity. These experiments demonstrated that (i) the contribution of the recorded motoneuronal AP firing to the firing of that motoneuron is rather small during VOC activity (the activity of the neuron is dominated by gap junction-coupled potentials); (ii) during a VOC, the HYP amplitude of the recorded neuron only partly depends on its firing an AP (also see above); and (iii) most of the activity displayed by a given motoneuron reflects population-level motoneuronal activity.

Figure 5

Download asset Open asset

Figure 5—source data 1

Comparison between motoneuron action potential and hyperpolarization amplitude during midbrain-evoked vocal and antidromic stimulation for QX314 treatment and carbenoxolone treatment.

https://cdn.elifesciences.org/articles/59390/elife-59390-fig5-data1-v1.xlsx

Download elife-59390-fig5-data1-v1.xlsx

Dependence of HYP activation on glycinergic inhibitory input to VMN

As noted in the Introduction, we previously identified a subpopulation of glycinergic VPN neurons, suggesting direct glycinergic input onto motoneurons (also see Figure 1f). To confirm and identify the location of glycine release onto vocal motoneurons, we used multicolor super-resolution structured illumination microscopy (SR-SIM) imaging, which can achieve an enhanced resolution of ~100 nm in the lateral (x–y) and ~300 nm in the axial (z) planes (Gustafsson, 2008). SR-SIM imaging demonstrated dense, punctate glycinergic-immunoreactive labeling throughout VMN. Overlap of glycinergic signal with labeling for synaptic vesicle protein (SV2) revealed prominent boutons directly abutting motoneuron somata (Figure 6a–c). Glycinergic boutons were also observed on motoneuron dendrites within the contralateral VMN (Figure 6d–h) and VPN (Figure 6d, i–k). These results demonstrated an anatomical basis for glycinergic release and inhibition spatially distributed across the entire somato-dendritic extent of vocal motoneurons.

Figure 6

Download asset Open asset

Having identified glycinergic contacts on the VMN motoneurons and that blocking gap junctional coupling was essential for the HYP (see above), we next carried out a series of experiments to more directly investigate if inhibition was responsible for the HYP (Figure 7a–c). We observed early on that at midbrain stimulation levels sub-threshold to VOC compound potential induction, tonic membrane HYPs (on average −2.48 ± 0.68 mV below RMP; n = 7; N = 2) could be detected, indicating activation of inhibitory inputs (Figure 7—figure supplement 1a, left; magenta trace: subthreshold, black trace: suprathreshold); these HYPs were not artifacts originating from electrical stimulation (Figure 7—figure supplement 1a, right, orange trace). Due to the high synchrony of motoneuron APs during vocal activity, field potentials could be detected even with our high-resistance electrodes (Figure 7—figure supplement 1a, right, black arrow). We also found that changing the chloride reversal potential by intracellular chloride injections via 3 M KCl-filled electrodes revealed a prominent inhibitory input to motoneurons during VOCs as the membrane potential showed significant changes in the degree of repolarization compared to baseline levels (Figure 7—figure supplement 1b, blue arrow). The HYP during VOCs was heavily reduced (baseline: −9.62 ± 1.34 mV vs. chloride injected: −1.20 ± 1.23 mV; n = 10, N = 3; p<0.001) (Figure 7—figure supplement 1b, blue trace), thus indicating an inhibitory contribution to vocal behavior. Antidromic stimulation still showed the HYP, however, at a reduced amplitude (baseline: −5.28 ± 1.95 mV vs. chloride injected: −3.30 ± 1.71 mV; n = 9, N = 3; p=0.093) (Figure 7—figure supplement 1c). As with QX314 intracellular injections, this seemingly contrary result is likely due to electrotonic coupling whereby manipulating one of hundreds of motoneurons does not reveal the full extent of inhibitory activity in the VMN.

Figure 7 with 1 supplement see all

Download asset Open asset

Figure 7—source data 1

Comparison between motoneuron action potential and hyperpolarization amplitude during midbrain-evoked vocal and antidromic stimulation for strychnine treatment.

https://cdn.elifesciences.org/articles/59390/elife-59390-fig7-data1-v1.xlsx

Download elife-59390-fig7-data1-v1.xlsx

To test the influence of glycinergic contacts onto motoneurons, we pressure injected the glycine receptor antagonist strychnine into the VMN. After strychnine injection, no change in AP amplitude during midbrain-evoked VOCs (Figure 7a) could be detected. Strychnine, however, abolished the HYP of motoneurons during VOCs (baseline −4.67 ± 1.88 mV vs. strychnine: 0.89 ± 1.88 mV, n = 20, N = 2; p<0.001) (Figure 7a). The HYP during antidromic stimulation completely disappeared (baseline: −3.85 ± 0.86 mV vs. strychnine: 0.40 ± 0.86 mV, n = 20, N = 2; p<0.001) (Figure 7a, insets). These results suggested that glycinergic neurons, activated via gap junctional coupling, were responsible for generating the HYP during antidromic stimulation.

Following strychnine injections, spontaneous VOCs appeared with similar decreases in motoneuron HYP amplitude not seen for spontaneous VOCs under control conditions without prior strychnine injections (Figure 7c). Since VMN motoneurons lack axon collaterals (Figure 1d), the results imply that gap junction coupling between motor and glycinergic VPN neurons is sufficient to drive AP firing of glycinergic neurons.

The interval between the first and second motoneuron AP significantly decreased following strychnine application (baseline: 9.61 ± 0.69 ms, strychnine 7.94 ± 0.69 ms; n = 10, N = 2; p=0.049). Variability of the interspike interval (ISI), as measured by the coefficient of variation, also increased significantly (baseline: 4.90 ± 3.95%, strychnine: 13.54 ± 3.95%; n = 10, N = 2; p=0.009). Thus, strychnine increases both frequency and variability of motoneuron firing that matches VOC output.

Typically, VOCs show sharp peaks between successive potentials, similar to sound pulses within natural grunts (e.g., Maruska and Mensinger, 2009; McIver et al., 2014). Spontaneous VOCs following injection with strychnine had potentials varying in width (Figure 7c) and multiple peaks, reminiscent of VOCs associated with weak VMN synchronization (Figure 2b).

Vocal premotoneurons are excited by gap junctional coupling

Pappas and Bennett, 1966 also recorded from ‘prejunctional fibers’ using both orthodromic and antidromic stimulation, although they did not know the origin of these fibers. We have since identified these as VPN neurons (Bass and Baker, 1990; Chagnaud et al., 2011; Chagnaud and Bass, 2014). The pattern of VPN activity directly predicts the pattern of VMN activity, the intervals of VOC potentials, and vocalization PRR (Figure 8a, Chagnaud et al., 2012). Antidromic stimulation of the vocal nerve leads to membrane depolarizations (via electrotonic input) in premotor VPN neurons in the closely related midshipman (Bass and Baker, 1990). We recorded from toadfish VPN neurons to test whether gap junctional coupling is in fact able to induce AP firing in the VPN population, which would be needed to activate or inhibit motoneurons. Toadfish VPN neurons generate two depolarizing components, one before and one during VOCs, that might reflect motoneuron activity leaking through gap junctions (Figure 8a, Chagnaud and Bass, 2014). During antidromic stimulation of the vocal nerve, VPN neurons showed small subthreshold potentials similar to VMN neurons indicative of gap junctional coupling, as well as APs at higher stimulation amplitudes (Figure 8b). Thus, antidromic activation in motoneurons could elicit AP firing in VPN premotoneurons. The latency of these APs (ipsi: 4.66 ± 0.01 ms, n = 3; N = 1; contra: 5.43 ± 0.16 ms; n = 3; N = 1) roughly coincided with the depolarization following antidromically elicited APs in motoneurons (Figure 3a, magenta arrow).

Figure 8

Download asset Open asset

To test if a network component might also be important to activate VPN neurons, we stimulated the ipsilateral and contralateral vocal nerve (at varying latencies) to antidromically generate subthreshold membrane depolarizations in VPN neurons. A decrease in latency between the ipsilateral and contralateral stimulation pulses eventually led to AP firing in VPN neurons, thus showing that AP firing in VPN neurons also depends on gap junctional activation of the vocal network (Figure 8c). VPN activation could thus have led to motoneuron depolarization (Figure 3, magenta arrows) and activation of gap junction-coupled glycinergic neurons (Rosner et al., 2018) that, in turn, inhibit motoneurons and generated the HYP.

Together, our experiments support the hypothesis that gap junction-mediated activation of glycinergic neurons can account for remarkable synchrony in motoneuron firing and likely also contributes to pattern generation. The level of synchronicity in the VMN network is perhaps rivaled only by that of fish electromotor systems where electrotonic coupling and intrinsic neuronal properties seem to play a predominant role (Bennett, 1971; Moortgat et al., 1998; Moortgat et al., 2000a; Moortgat et al., 2000b).

Frogs, birds, marine mammals, and humans typically come to mind when we think about sound-producing vertebrate species (Chen and Wiens, 2020). Yet, it is also widespread among fishes. Bony vertebrates include the Sarcopterygii, the vast majority of which are tetrapods, and Actinopterygii, which represent more than half of living vertebrate species, close to 99% of which are teleosts, including toadfishes (Nelson et al., 2016). Teleost families with evidence for soniferous behavior contain nearly two-thirds of actinopterygian species (Rice et al., 2020). Concurrent activation of neurons required for rapid and precise activation of muscle groups underlying acoustic signaling in different lineages of fishes (Chagnaud et al., 2012; Kéver et al., 2020) and in tetrapods (Mead et al., 2017; Kwong-Brown et al., 2019) might all benefit from gap junction-mediated glycinergic inhibition.

Feedback or feed-forward inhibition?

Our antidromic stimulation experiment suggests a feedback loop of glycinergic inhibitory neurons activated upon by depolarization of the VMN–VPN network. Antidromic activation is a highly artificial condition in which depolarizations are first elicited in VMN, and then transmitted via gap junctions to VPN. Thus, a recurrent inhibitory pathway is activated during antidromic stimulation. However, the shorter latency of the inhibitory potential during vocalization compared to antidromic activation argues against a recurrent inhibition hypothesis and favors a feed-forward hypothesis. During natural vocal behavior, VPN would be feeding forward to VMN, given that VPN fires before VMN during VOCs (Bass and Baker, 1990; Chagnaud et al., 2011; Chagnaud and Bass, 2014). Consequently, excitatory VPN neurons could activate VPN’s glycinergic neurons, via elecrotonic coupling, before the motoneurons (Figure 9). This condition is far more likely considering the temporal appearance of the HYP during vocal behavior compared to antidromic (unnatural) conditions. Electrotonic coupling of VPN excitatory neurons to glycinergic VPN neurons thus most likely activates the latter, which causes feed-forward inhibitory action upon motoneurons. Electrophysiological recordings from VPN glycinergic neurons during vocal activity are, however, needed to verify this hypothesis. If proven, we expect these neurons to fire rhythmically like other VPN neurons directly coupled to VMN (Chagnaud et al., 2011; also see Figure 8a). Anatomical evidence for local activation within VPN comes from transneuronal labeling of local VPN neurons after filling an intracellulary-recorded VPN neuron with neurobiotin (Chagnaud et al., 2011; Chagnaud and Bass, 2014). While optogenetic inactivation of VPN could provide evidence for this anatomical substrate in the antidromic activation, optogenetic tools are not yet available in toadfishes. Due to the close proximity of VPN to VMN, and its bilateral organization into extended columns along the length of VMN, pharmacological or surgical approaches appear impossible in VPN. Due to the difficulties in obtaining VPN recordings, dual intracellular recordings from VMN and VPN neurons have proven very difficult in the intact preparation (B. Chagnaud, personal observations).

Figure 9

Download asset Open asset

How might antidromic nerve stimulation result in glycine release within such a short time window of a few milliseconds to create HYP? This can only be via electrotonic coupling of glycinergic VPN neurons to the vocal network. Activation of motoneuron axons by antidromic stimulation results in motoneuron depolarization as the AP invades the soma and dendrites. Due to synchronous axon activation, the potential of a substantial number of motoneurons is changed (at high stimulation amplitudes). The potential change in motoneurons is conducted to VPN premotoneurons via electrotonic junctions as premotoneurons are smaller and much more easily excited (Chagnaud et al., 2011) than motoneurons. This, in turn, would activate other VPN neurons, including glycinergic ones electrically coupled to VMN and VPN neurons via gap junctions. While transneuronal labeling supports glycinergic VPN neurons being coupled to the VMN network (Rosner et al., 2018), we do not know if they contact motoneurons or premotoneurons via gap junctions. Unfortunately, we have so far been unsuccessful using electron microscopy together with the available antibodies for dual labeling of glycinergic and gap junction synapses. In any case, glycinergic neurons are activated and project via a chemical synapse onto motoneurons.

Inhibitory action in this network can serve multiple functions. First, it repolarizes motoneurons, a feature essential to VMN’s ability to repetitively fire APs, as seen by our pulse train experiments. Second, it generates a window of decreased excitability that prevents motoneuron misfiring outside of the VPN rhythm due to the long time course of inhibitory action compared to the normal activation rhythm of ca. 100 Hz (see Video 2). This is essential to the ability of the next incoming excitatory input of rhythmically firing VPN neurons to set the firing frequency of vocal motoneurons. A gap junction-mediated, feed-forward glycinergic inhibition could account for temporal patterning in the millisecond time range that characterizes the vocal network and behavior of toadfishes (Pappas and Bennett, 1966; Bass and Baker, 1991; Chagnaud et al., 2011; Chagnaud et al., 2012). A similar mechanism has previously been shown for the Mauthner cell escape circuit (Furukawa and Furshpan, 1963; Diamond and Roper, 1973; Diamond et al., 1973; Faber et al., 1989; Zottoli and Faber, 2000; Korn and Faber, 2005; Sillar, 2009) and may operate in other vocal systems in teleosts and tetrapods that are dependent on comparable levels of temporal precision (Sturdy et al., 2003; Rome, 2006; Bass et al., 2015; Mead et al., 2017; Nelson et al., 2018; Kwong-Brown et al., 2019).

Video 2

Download asset

All data generated or analysed during this study are freely accessible on the Harvard dataverse file repository.

1. 1. Chagnaud BP

   (2021) dataverse.harvard.edu/

   Replication data for Gap junction mediated glycinergic inhibition ensures precise temporal patterning in vocal behavior.

   https://doi.org/10.7910/DVN/RZLWHE

1. 1. Alcamí P
   2. Pereda AE

   (2019) Beyond plasticity: the dynamic impact of electrical synapses on neural circuits

   Nature Reviews Neuroscience 20:253–271.

   https://doi.org/10.1038/s41583-019-0133-5

   • PubMed
   • Google Scholar
2. 1. Amorim MCP
   2. Vasconcelos RO
   3. Fonseca PJ

   (2015)

   Sound Communication in Fishes

   1–33, Fish sounds and mate choice, Sound Communication in Fishes, Springer.

   • Google Scholar
3. 1. Amorim MC
   2. Conti C
   3. Modesto T
   4. Gonçalves A
   5. Fonseca PJ

   (2015b) Agonistic sounds signal male quality in the lusitanian toadfish

   Physiology & Behavior 149:192–198.

   https://doi.org/10.1016/j.physbeh.2015.06.002

   • PubMed
   • Google Scholar
4. 1. Barkan CL
   2. Zornik E

   (2019) Feedback to the future: motor neuron contributions to central pattern generator function

   The Journal of Experimental Biology 222:jeb193318.

   https://doi.org/10.1242/jeb.193318

   • PubMed
   • Google Scholar
5. 1. Bass AH
   2. Marchaterre MA
   3. Baker R

   (1994) Vocal-acoustic pathways in a teleost fish

   The Journal of Neuroscience 14:4025–4039.

   https://doi.org/10.1523/JNEUROSCI.14-07-04025.1994

   • PubMed
   • Google Scholar
6. 1. Bass AH
   2. Gilland EH
   3. Baker R

   (2008) Evolutionary origins for social vocalization in a vertebrate hindbrain-spinal compartment

   Science 321:417–421.

   https://doi.org/10.1126/science.1157632

   • PubMed
   • Google Scholar
7. Book

   1. Bass A
   2. Chagnaud B
   3. Feng N

   (2015)

   Comparative neurobiology of sound production in fishes

   In: Ladich F, editors. Sound Communication in Fishes. Animal Signals and Communication. Vienna: Springer. pp. 33–75.

   • Google Scholar
8. 1. Bass AH
   2. Baker R

   (1990) Sexual dimorphisms in the vocal control system of a teleost fish: morphology of physiologically identified neurons

   Journal of Neurobiology 21:1155–1168.

   https://doi.org/10.1002/neu.480210802

   • PubMed
   • Google Scholar
9. 1. Bass A
   2. Baker R

   (1991) Evolution of homologous vocal control traits

   Brain, Behavior and Evolution 38:240–254.

   https://doi.org/10.1159/000114391

   • PubMed
   • Google Scholar
10. 1. Bass AH
    2. Clark CW

    (2003)

    Acoustic Communication

    15–64, The physical acoustics of underwater sound communication, Acoustic Communication, Springer.

    • Google Scholar
11. 1. Bass AH
    2. Marchaterre MA

    (1989) Sound-generating (sonic) motor system in a teleost fish (Porichthys notatus): sexual polymorphisms and general synaptology of sonic motor nucleus

    The Journal of Comparative Neurology 286:154–169.

    https://doi.org/10.1002/cne.902860203

    • PubMed
    • Google Scholar
12. 1. Bass AH
    2. McKibben JR

    (2003b) Neural mechanisms and behaviors for acoustic communication in teleost fish

    Progress in Neurobiology 69:1–26.

    https://doi.org/10.1016/S0301-0082(03)00004-2

    • PubMed
    • Google Scholar
13. Software

    1. Bates D
    2. Maechler M
    3. Bolker B
    4. Walker S
    5. Bojesen H
    6. Christensen R

    (2015)

    lme4: Linear mixed-effects models using Eigen and S4, version R package version 1.1–7

    lme4: Linear mixed-effects models using Eigen and S4.
14. 1. Beaumont M
    2. Maccaferri G

    (2011) Is connexin36 critical for GABAergic hypersynchronization in the Hippocampus?

    The Journal of Physiology 589:1663–1680.

    https://doi.org/10.1113/jphysiol.2010.201491

    • PubMed
    • Google Scholar
15. Book

    1. Bennett MVL

    (1971)

    Electric organs

    In: Hoar W. S, Randall D. J, editors. Fish Physiology. New York London: Academic Press. pp. 347–491.

    • Google Scholar
16. 1. Bennett MV
    2. Zukin RS

    (2004) Electrical coupling and neuronal synchronization in the mammalian brain

    Neuron 41:495–511.

    https://doi.org/10.1016/S0896-6273(04)00043-1

    • PubMed
    • Google Scholar
17. 1. Berkowitz A

    (2008) Physiology and morphology of shared and specialized spinal interneurons for locomotion and scratching

    Journal of Neurophysiology 99:2887–2901.

    https://doi.org/10.1152/jn.90235.2008

    • PubMed
    • Google Scholar
18. 1. Bowling DL
    2. Dunn JC
    3. Smaers JB
    4. Garcia M
    5. Sato A
    6. Hantke G
    7. Handschuh S
    8. Dengg S
    9. Kerney M
    10. Kitchener AC
    11. Gumpenberger M
    12. Fitch WT

    (2020) Rapid evolution of the primate larynx?

    PLOS Biology 18:e3000764.

    https://doi.org/10.1371/journal.pbio.3000764

    • PubMed
    • Google Scholar
19. 1. Brantley RK
    2. Bass AH

    (1994) Alternative male spawning tactics and acoustic signals in the plainfin midshipman fish Porichthys notatus girard (Teleostei, Batrachoididae)

    Ethology 96:213–232.

    https://doi.org/10.1111/j.1439-0310.1994.tb01011.x

    • Google Scholar
20. 1. Buckley K
    2. Kelly RB

    (1985) Identification of a transmembrane glycoprotein specific for secretory vesicles of neural and endocrine cells

    The Journal of Cell Biology 100:1284–1294.

    https://doi.org/10.1083/jcb.100.4.1284

    • PubMed
    • Google Scholar
21. 1. Capranica RR

    (1992) The untuning of the tuning curve: is it time?

    Seminars in Neuroscience 4:401–408.

    https://doi.org/10.1016/1044-5765(92)90048-7

    • Google Scholar
22. 1. Chagnaud BP
    2. Baker R
    3. Bass AH

    (2011) Vocalization frequency and duration are coded in separate hindbrain nuclei

    Nature Communications 2:1–11.

    https://doi.org/10.1038/ncomms1349

    • Google Scholar
23. 1. Chagnaud BP
    2. Zee MC
    3. Baker R
    4. Bass AH

    (2012) Innovations in motoneuron synchrony drive rapid temporal modulations in vertebrate acoustic signaling

    Journal of Neurophysiology 107:3528–3542.

    https://doi.org/10.1152/jn.00030.2012

    • PubMed
    • Google Scholar
24. Software

    1. Chagnaud BP

    (2020) Vocal_nerve_analysis

    Github.

    https://github.com/Toadfishlab/Vocal_nerve_analysis
25. 1. Chagnaud BP
    2. Bass AH

    (2014) Vocal behavior and vocal central pattern generator organization diverge among toadfishes

    Brain, Behavior and Evolution 84:51–65.

    https://doi.org/10.1159/000362916

    • PubMed
    • Google Scholar
26. 1. Chen Z
    2. Wiens JJ

    (2020) The origins of acoustic communication in vertebrates

    Nature Communications 11:1–8.

    https://doi.org/10.1038/s41467-020-14356-3

    • Google Scholar
27. 1. Demski LS
    2. Gerald JW

    (1972) Sound production evoked by electrical stimulation of the brain in toadfish (Opsanus beta)

    Animal Behaviour 20:507–513.

    https://doi.org/10.1016/S0003-3472(72)80015-0

    • Google Scholar
28. 1. Diamond J
    2. Roper S
    3. Yasargil GM

    (1973) The membrane effects, and sensitivity to strychnine, of neural inhibition of the mauthner cell, and its inhibition by glycine and GABA

    The Journal of Physiology 232:87–111.

    https://doi.org/10.1113/jphysiol.1973.sp010258

    • PubMed
    • Google Scholar
29. 1. Diamond J
    2. Roper S

    (1973) Analysis of mauthner cell responses to iontophoretically delivered pulses of GABA, glycine and L-glutamate

    The Journal of Physiology 232:113–128.

    https://doi.org/10.1113/jphysiol.1973.sp010259

    • PubMed
    • Google Scholar
30. 1. Eccles JC
    2. Fatt P
    3. Koketsu K

    (1954) Cholinergic and inhibitory synapses in a pathway from motor-axon collaterals to motoneurones

    The Journal of Physiology 126:524–562.

    https://doi.org/10.1113/jphysiol.1954.sp005226

    • PubMed
    • Google Scholar
31. 1. Elemans CP
    2. Mensinger AF
    3. Rome LC

    (2014) Vocal production complexity correlates with neural instructions in the oyster toadfish (Opsanus tau)

    Journal of Experimental Biology 217:1887–1893.

    https://doi.org/10.1242/jeb.097444

    • PubMed
    • Google Scholar
32. 1. Faber DS
    2. Fetcho JR
    3. Korn H

    (1989) Neuronal networks underlying the escape response in goldfish. general implications for motor control

    Annals of the New York Academy of Sciences 563:11–33.

    https://doi.org/10.1111/j.1749-6632.1989.tb42187.x

    • PubMed
    • Google Scholar
33. 1. Falgairolle M
    2. Puhl JG
    3. Pujala A
    4. Liu W
    5. O'Donovan MJ

    (2017) Motoneurons regulate the central pattern generator during drug-induced locomotor-like activity in the neonatal mouse

    eLife 6:e26622.

    https://doi.org/10.7554/eLife.26622

    • PubMed
    • Google Scholar
34. 1. Fay RR
    2. Edds-Walton PL

    (2008)

    Fish Bioacoustics

    49–97, Structures and Functions of the Auditory Nervous System ofFishes, Fish Bioacoustics, Springer.

    • Google Scholar
35. 1. Feng NY
    2. Fergus DJ
    3. Bass AH

    (2015) Neural transcriptome reveals molecular mechanisms for temporal control of vocalization across multiple timescales

    BMC Genomics 16:1–25.

    https://doi.org/10.1186/s12864-015-1577-2

    • Google Scholar
36. 1. Fine ML
    2. Lenhardt ML

    (1983) Shallow-water propagation of the toadfish mating call

    Comparative Biochemistry and Physiology Part A: Physiology 76:225–231.

    https://doi.org/10.1016/0300-9629(83)90319-5

    • Google Scholar
37. 1. Fine ML
    2. Thorson RF

    (2008) Use of passive acoustics for assessing behavioral interactions in individual toadfish

    Transactions of the American Fisheries Society 137:627–637.

    https://doi.org/10.1577/T04-134.1

    • Google Scholar
38. 1. Fish JF

    (1972)

    Behavior of Marine Animals

    386–434, The effect of sound playback on the toadfish, Behavior of Marine Animals, Springer.

    • Google Scholar
39. 1. Furukawa T
    2. Furshpan EJ

    (1963) Two inhibitory mechanisms in the mauthner neurons of goldfish

    Journal of Neurophysiology 26:140–176.

    https://doi.org/10.1152/jn.1963.26.1.140

    • PubMed
    • Google Scholar
40. 1. Gerald JW

    (1971) Sound production during courtship in six species of sunfish (Centrarchidae)

    Evolution 25:75–87.

    https://doi.org/10.1111/j.1558-5646.1971.tb01859.x

    • Google Scholar
41. 1. Gerhardt HC
    2. Humfeld SC
    3. Marshall VT

    (2007) Temporal order and the evolution of complex acoustic signals

    Proceedings of the Royal Society B: Biological Sciences 274:1789–1794.

    https://doi.org/10.1098/rspb.2007.0451

    • Google Scholar
42. 1. Grillner S
    2. El Manira A

    (2020) Current principles of motor control, with special reference to vertebrate locomotion

    Physiological Reviews 100:271–320.

    https://doi.org/10.1152/physrev.00015.2019

    • PubMed
    • Google Scholar
43. 1. Grothe B

    (2003) New roles for synaptic inhibition in sound localization

    Nature Reviews Neuroscience 4:540–550.

    https://doi.org/10.1038/nrn1136

    • PubMed
    • Google Scholar
44. 1. Gustafsson MGL

    (2008) Super-resolution light microscopy Goes live

    Nature Methods 5:385–387.

    https://doi.org/10.1038/nmeth0508-385

    • Google Scholar
45. 1. Hauser M
    2. Agnetta B
    3. Perez C

    (1998) Orienting asymmetries in rhesus monkeys: the effect of time-domain changes on acoustic perception

    Animal Behaviour 56:41–47.

    https://doi.org/10.1006/anbe.1998.0738

    • PubMed
    • Google Scholar
46. 1. Kapfer C
    2. Glickfeld LL
    3. Atallah BV
    4. Scanziani M

    (2007) Supralinear increase of recurrent inhibition during sparse activity in the somatosensory cortex

    Nature Neuroscience 10:743–753.

    https://doi.org/10.1038/nn1909

    • PubMed
    • Google Scholar
47. 1. Kéver L
    2. Bass AH
    3. Parmentier E
    4. Chagnaud BP

    (2020) Neuroanatomical and neurophysiological mechanisms of acoustic and weakly electric signaling in synodontid catfish

    Journal of Comparative Neurology 528:2602–2619.

    https://doi.org/10.1002/cne.24920

    • Google Scholar
48. 1. Kiehn O
    2. Tresch MC

    (2002) Gap junctions and motor behavior

    Trends in Neurosciences 25:108–115.

    https://doi.org/10.1016/S0166-2236(02)02038-6

    • PubMed
    • Google Scholar
49. 1. Kittelberger JM
    2. Bass AH

    (2013) Vocal-motor and auditory connectivity of the midbrain periaqueductal gray in a teleost fish

    Journal of Comparative Neurology 521:791–812.

    https://doi.org/10.1002/cne.23202

    • PubMed
    • Google Scholar
50. 1. Korn H
    2. Faber DS

    (2005) The mauthner cell half a century later: a neurobiological model for decision-making?

    Neuron 47:13–28.

    https://doi.org/10.1016/j.neuron.2005.05.019

    • PubMed
    • Google Scholar
51. 1. Kros L
    2. Lindeman S
    3. Eelkman Rooda OHJ
    4. Murugesan P
    5. Bina L
    6. Bosman LWJ
    7. De Zeeuw CI
    8. Hoebeek FE

    (2017) Synchronicity and rhythmicity of purkinje cell firing during generalized Spike-and-Wave discharges in a natural mouse model of absence epilepsy

    Frontiers in Cellular Neuroscience 11:346.

    https://doi.org/10.3389/fncel.2017.00346

    • PubMed
    • Google Scholar
52. 1. Kuznetsova A
    2. Brockhoff PB
    3. Christensen RHB

    (2017) lmerTest Package: tests in linear mixed effects models

    Journal of Statistical Software 82:1–26.

    https://doi.org/10.18637/jss.v082.i13

    • Google Scholar
53. 1. Kwong-Brown U
    2. Tobias ML
    3. Elias DO
    4. Hall IC
    5. Elemans CP
    6. Kelley DB

    (2019) The return to water in ancestral Xenopus was accompanied by a novel mechanism for producing and shaping vocal signals

    eLife 8:e39946.

    https://doi.org/10.7554/eLife.39946

    • PubMed
    • Google Scholar
54. Book

    1. Ladich F
    2. Collin S
    3. Moller P
    4. Kapoor BG

    (2006)

    Communication in Fishes

    Enfield, NH: Science Publishers.

    • Google Scholar
55. 1. Lawton KJ
    2. Perry WM
    3. Yamaguchi A
    4. Zornik E

    (2017) Motor neurons tune premotor activity in a vertebrate central pattern generator

    The Journal of Neuroscience 37:3264–3275.

    https://doi.org/10.1523/JNEUROSCI.2755-16.2017

    • PubMed
    • Google Scholar
56. 1. Llinás RR

    (2014) Intrinsic electrical properties of mammalian neurons and CNS function: a historical perspective

    Frontiers in Cellular Neuroscience 8:320.

    https://doi.org/10.3389/fncel.2014.00320

    • PubMed
    • Google Scholar
57. 1. Luke SG

    (2017) Evaluating significance in linear mixed-effects models in R

    Behavior Research Methods 49:1494–1502.

    https://doi.org/10.3758/s13428-016-0809-y

    • PubMed
    • Google Scholar
58. 1. Marder E
    2. Gutierrez GJ
    3. Nusbaum MP

    (2017) Complicating connectomes: electrical coupling creates parallel pathways and degenerate circuit mechanisms

    Developmental Neurobiology 77:597–609.

    https://doi.org/10.1002/dneu.22410

    • PubMed
    • Google Scholar
59. 1. Maruska KP
    2. Boyle KS
    3. Dewan LR
    4. Tricas TC

    (2007) Sound production and spectral hearing sensitivity in the hawaiian sergeant damselfish, Abudefduf abdominalis

    Journal of Experimental Biology 210:3990–4004.

    https://doi.org/10.1242/jeb.004390

    • PubMed
    • Google Scholar
60. 1. Maruska KP
    2. Mensinger AF

    (2009) Acoustic characteristics and variations in grunt vocalizations in the oyster toadfish Opsanus tau

    Environmental Biology of Fishes 84:325–337.

    https://doi.org/10.1007/s10641-009-9446-y

    • Google Scholar
61. 1. Matsunaga T
    2. Kohsaka H
    3. Nose A

    (2017) Gap Junction-Mediated signaling from motor neurons regulates motor generation in the central circuits of larval Drosophila

    The Journal of Neuroscience 37:2045–2060.

    https://doi.org/10.1523/JNEUROSCI.1453-16.2017

    • PubMed
    • Google Scholar
62. 1. McIver EL
    2. Marchaterre MA
    3. Rice AN
    4. Bass AH

    (2014) Novel underwater soundscape: acoustic repertoire of plainfin midshipman fish

    Journal of Experimental Biology 217:2377–2389.

    https://doi.org/10.1242/jeb.102772

    • Google Scholar
63. 1. McKibben JR
    2. Bass AH

    (1998) Behavioral assessment of acoustic parameters relevant to signal recognition and preference in a vocal fish

    The Journal of the Acoustical Society of America 104:3520–3533.

    https://doi.org/10.1121/1.423938

    • PubMed
    • Google Scholar
64. 1. McKibben JR
    2. Bass AH

    (2001) Effects of temporal envelope modulation on acoustic signal recognition in a vocal fish, the plainfin midshipman

    The Journal of the Acoustical Society of America 109:2934–2943.

    https://doi.org/10.1121/1.1373441

    • PubMed
    • Google Scholar
65. 1. McLean DL
    2. Fan J
    3. Higashijima S
    4. Hale ME
    5. Fetcho JR

    (2007) A topographic map of recruitment in spinal cord

    Nature 446:71–75.

    https://doi.org/10.1038/nature05588

    • PubMed
    • Google Scholar
66. 1. Mead AF
    2. Osinalde N
    3. Ørtenblad N
    4. Nielsen J
    5. Brewer J
    6. Vellema M
    7. Adam I
    8. Scharff C
    9. Song Y
    10. Frandsen U
    11. Blagoev B
    12. Kratchmarova I
    13. Elemans CP

    (2017) Fundamental constraints in synchronous muscle limit superfast motor control in vertebrates

    eLife 6:e29425.

    https://doi.org/10.7554/eLife.29425

    • PubMed
    • Google Scholar
67. 1. Moortgat KT
    2. Keller CH
    3. Bullock TH
    4. Sejnowski TJ

    (1998) Submicrosecond pacemaker precision is behaviorally modulated: the gymnotiform electromotor pathway

    PNAS 95:4684–4689.

    https://doi.org/10.1073/pnas.95.8.4684

    • PubMed
    • Google Scholar
68. 1. Moortgat KT
    2. Bullock TH
    3. Sejnowski TJ

    (2000a) Precision of the pacemaker nucleus in a weakly electric fish: network versus cellular influences

    Journal of Neurophysiology 83:971–983.

    https://doi.org/10.1152/jn.2000.83.2.971

    • PubMed
    • Google Scholar
69. 1. Moortgat KT
    2. Bullock TH
    3. Sejnowski TJ

    (2000b) Gap junction effects on precision and frequency of a model pacemaker network

    Journal of Neurophysiology 83:984–997.

    https://doi.org/10.1152/jn.2000.83.2.984

    • PubMed
    • Google Scholar
70. 1. Myrberg AA
    2. Riggio RJ

    (1985) Acoustically mediated individual recognition by a coral reef fish (Pomacentrus partitus)

    Animal Behaviour 33:411–416.

    https://doi.org/10.1016/S0003-3472(85)80065-8

    • Google Scholar
71. 1. Myrberg AA
    2. Spires JY

    (1972)

    Sound discrimination by the bicolor damselfish, eupomacentrus partitus

    The Journal of Experimental Biology 57:727–735.

    • Google Scholar
72. Book

    1. Nelson JS
    2. Grande TC
    3. Wilson MVH

    (2016)

    Fishes of the World, 5 Edition

    Hoboken, NJ: John Wiley & Sons, Inc.

    • Google Scholar
73. 1. Nelson FE
    2. Hollingworth S
    3. Marx JO
    4. Baylor SM
    5. Rome LC

    (2018) Small Ca²⁺ releases enable hour-long high-frequency contractions in midshipman swimbladder muscle

    Journal of General Physiology 150:127–143.

    https://doi.org/10.1085/jgp.201711760

    • PubMed
    • Google Scholar
74. 1. Pappas GD
    2. Bennett MV

    (1966) Specialized junctions involved in electrical transmission between neurons

    Annals of the New York Academy of Sciences 137:495–508.

    https://doi.org/10.1111/j.1749-6632.1966.tb50177.x

    • PubMed
    • Google Scholar
75. 1. Pereda AE

    (2014) Electrical synapses and their functional interactions with chemical synapses

    Nature Reviews Neuroscience 15:250–263.

    https://doi.org/10.1038/nrn3708

    • PubMed
    • Google Scholar
76. 1. Ramirez JM
    2. Baertsch N

    (2018) Defining the rhythmogenic elements of mammalian breathing

    Physiology 33:302–316.

    https://doi.org/10.1152/physiol.00025.2018

    • PubMed
    • Google Scholar
77. 1. Remage-Healey L
    2. Bass AH

    (2005) Rapid elevations in both steroid hormones and vocal signaling during playback challenge: a field experiment in gulf toadfish

    Hormones and Behavior 47:297–305.

    https://doi.org/10.1016/j.yhbeh.2004.11.017

    • PubMed
    • Google Scholar
78. 1. Remage-Healey L
    2. Bass AH

    (2006) A rapid neuromodulatory role for steroid hormones in the control of reproductive behavior

    Brain Research 1126:27–35.

    https://doi.org/10.1016/j.brainres.2006.06.049

    • Google Scholar
79. 1. Renshaw B

    (1941) Influence of discharge of motoneurons upon excitation of neighboring motoneurons

    Journal of Neurophysiology 4:167–183.

    https://doi.org/10.1152/jn.1941.4.2.167

    • Google Scholar
80. Preprint

    1. Rice AN
    2. Farina SC
    3. Makowski AJ
    4. Kaatz IM
    5. Lobel PS
    6. Bemis WE
    7. Bass AH

    (2020) Evolution and ecology is widespread acoustic signaling behavior across fishes

    bioRxiv.

    https://doi.org/10.1101/2020.09.14.296335

    • Google Scholar
81. 1. Riede T
    2. Thomson SL
    3. Titze IR
    4. Goller F

    (2019) The evolution of the syrinx: an acoustic theory

    PLOS Biology 17:e2006507.

    https://doi.org/10.1371/journal.pbio.2006507

    • PubMed
    • Google Scholar
82. 1. Rome LC

    (2006) Design and function of superfast muscles: new insights into the physiology of skeletal muscle

    Annual Review of Physiology 68:193–221.

    https://doi.org/10.1146/annurev.physiol.68.040104.105418

    • PubMed
    • Google Scholar
83. 1. Rose GJ

    (2014) Time computations in anuran auditory systems

    Frontiers in Physiology 5:206.

    https://doi.org/10.3389/fphys.2014.00206

    • PubMed
    • Google Scholar
84. 1. Rosner E
    2. Rohmann KN
    3. Bass AH
    4. Chagnaud BP

    (2018) Inhibitory and modulatory inputs to the vocal central pattern generator of a teleost fish

    Journal of Comparative Neurology 526:1368–1388.

    https://doi.org/10.1002/cne.24411

    • Google Scholar
85. 1. Rozental R
    2. Srinivas M
    3. Spray DC

    (2001)

    Methods in Molecular Biology: Connexin Methods and Protocols

    How to Close a Gap Junction Channel, Methods in Molecular Biology: Connexin Methods and Protocols, Totowa, NJ, Humana Press Inc.

    • Google Scholar
86. 1. Rubow TK
    2. Bass AH

    (2009) Reproductive and diurnal rhythms regulate vocal motor plasticity in a teleost fish

    Journal of Experimental Biology 212:3252–3262.

    https://doi.org/10.1242/jeb.032748

    • PubMed
    • Google Scholar
87. 1. Schikorski T
    2. Braun N
    3. Zimmermann H

    (1994) Immunocytochemical characterization of the synaptic innervation of a single spinal neuron, the electric catfish electromotoneuron

    The Journal of Comparative Neurology 343:647–657.

    https://doi.org/10.1002/cne.903430413

    • PubMed
    • Google Scholar
88. 1. Sillar KT

    (2009) Mauthner cells

    Current Biology 19:R353–R355.

    https://doi.org/10.1016/j.cub.2009.02.025

    • Google Scholar
89. 1. Singer W

    (1999) Neuronal synchrony: a versatile code for the definition of relations?

    Neuron 24:49–65.

    https://doi.org/10.1016/S0896-6273(00)80821-1

    • PubMed
    • Google Scholar
90. 1. Smith GT
    2. Lu Y
    3. Zakon HH

    (2000) Parvocells: a novel interneuron type in the pacemaker nucleus of a weakly electric fish

    The Journal of Comparative Neurology 423:427–439.

    https://doi.org/10.1002/1096-9861(20000731)423:3<427::AID-CNE6>3.0.CO;2-S

    • PubMed
    • Google Scholar
91. 1. Sober SJ
    2. Sponberg S
    3. Nemenman I
    4. Ting LH

    (2018) Millisecond spike timing codes for motor control

    Trends in Neurosciences 41:644–648.

    https://doi.org/10.1016/j.tins.2018.08.010

    • PubMed
    • Google Scholar
92. 1. Song J
    2. Ampatzis K
    3. Björnfors ER
    4. El Manira A

    (2016) Motor neurons control locomotor circuit function retrogradely via gap junctions

    Nature 529:399–402.

    https://doi.org/10.1038/nature16497

    • PubMed
    • Google Scholar
93. 1. Stil A
    2. Drapeau P

    (2016) Neuronal labeling patterns in the spinal cord of adult transgenic zebrafish

    Developmental Neurobiology 76:642–660.

    https://doi.org/10.1002/dneu.22350

    • PubMed
    • Google Scholar
94. 1. Sturdy CB
    2. Wild JM
    3. Mooney R

    (2003) Respiratory and telencephalic modulation of vocal motor neurons in the zebra finch

    The Journal of Neuroscience 23:1072–1086.

    https://doi.org/10.1523/JNEUROSCI.23-03-01072.2003

    • PubMed
    • Google Scholar
95. 1. Tavolga WN

    (1958)

    Underwater sounds produced by two species of toadfish, Opsanus tau and Opsanus beta

    Bulletin Marine Science 8:278–284.

    • Google Scholar
96. 1. Thorson RF
    2. Fine ML

    (2002) Crepuscular changes in emission rate and parameters of the boatwhistle advertisement call of the gulf toadfish, Opsanus beta

    Environmental Biology of Fishes 63:321–331.

    https://doi.org/10.1023/A:1014334425821

    • Google Scholar
97. 1. Tiesinga PHE
    2. Sejnowski TJ

    (2001) Precision of pulse-coupled networks of integrate-and-fire neurons

    Network: Computation in Neural Systems 12:215–233.

    https://doi.org/10.1080/net.12.2.215.233

    • Google Scholar
98. 1. Uhlhaas PJ
    2. Roux F
    3. Rodriguez E
    4. Rotarska-Jagiela A
    5. Singer W

    (2010) Neural synchrony and the development of cortical networks

    Trends in Cognitive Sciences 14:72–80.

    https://doi.org/10.1016/j.tics.2009.12.002

    • PubMed
    • Google Scholar
99. 1. Uhlhaas PJ
    2. Singer W

    (2006) Neural synchrony in brain disorders: relevance for cognitive dysfunctions and pathophysiology

    Neuron 52:155–168.

    https://doi.org/10.1016/j.neuron.2006.09.020

    • Google Scholar
100. 1. Van Vreeswijk C
     2. Abbott LF
     3. Ermentrout GB

     (1994) When inhibition not excitation synchronizes neural firing

     Journal of Computational Neuroscience 1:313–321.

     https://doi.org/10.1007/BF00961879

     • PubMed
     • Google Scholar
101. 1. Vasconcelos RO
     2. Carriço R
     3. Ramos A
     4. Modesto T
     5. Fonseca PJ
     6. Amorim MCP

     (2012) Vocal behavior predicts reproductive success in a teleost fish

     Behavioral Ecology 23:375–383.

     https://doi.org/10.1093/beheco/arr199

     • Google Scholar
102. Book

     1. Wickham H

     (2016)

     ggplot2: elegant graphics for data analysis

     Springer.

     • Google Scholar
103. Book

     1. Winn H

     (1967)

     Vocal facilitation and biological significance of toadfish sounds

     In: WN T, editors. Marine Bio-Acoustics. Oxford: Pergamon Press. pp. 283–304.

     • Google Scholar
104. 1. Winn HE

     (1972)

     Behavior of Marine Animals

     361–385, Acoustic discrimination by the toadfish with comments on signal systems, Behavior of Marine Animals, Springer.

     • Google Scholar
105. 1. Yeh JZ

     (1978) Sodium inactivation mechanism modulates QX-314 block of sodium channels in squid axons

     Biophysical Journal 24:569–574.

     https://doi.org/10.1016/S0006-3495(78)85403-4

     • PubMed
     • Google Scholar
106. 1. Zottoli SJ
     2. Faber DS

     (2000) ■ Review: the mauthner cell: what has it taught Us?

     The Neuroscientist 6:26–38.

     https://doi.org/10.1177/107385840000600111

     • Google Scholar

Author details

1. Boris P Chagnaud

   Institute of Biology, Karl-Franzens-University Graz, Graz, Austria

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   boris.chagnaud@uni-graz.at

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5939-8541

2. Jonathan T Perelmuter

   Department of Neurobiology and Behavior, Cornell University, Ithaca, NY, United States

   Contribution

   Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9785-8211

3. Paul M Forlano

   1. Department of Biology, Brooklyn College, City University of New York, Brooklyn, NY, United States
   2. Subprograms in Behavioral and Cognitive Neuroscience, Neuroscience, and Ecology, Evolutionary Biology and Behavior, The Graduate Center, City University of New York, New York, NY, United States

   Contribution

   Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4258-5708

4. Andrew H Bass

   Department of Neurobiology and Behavior, Cornell University, Ithaca, NY, United States

   Contribution

   Conceptualization, Formal analysis, Funding acquisition, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   ahb3@cornell.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0182-6715

• 790

  views

• 85

  downloads

• 10

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.