Neurons come in a variety of shapes and sizes, which impacts their ability to get excited. A common electrophysiological test of neuronal excitability is the measurement of input resistance. Input resistance is assessed by somatic current injection and serves to predict how easily neurons depolarize in response to synaptic current. Lower-resistance neurons are considered less excitable since larger synaptic currents are required to depolarize from rest to threshold, following Ohm’s law (voltage=current×resistance). Since measurements are performed at resting potentials, the primary determinant of conductance reporting input resistance is assumed to be membrane leak channels, although voltage-dependent channels and synaptic noise can also contribute to input resistance (Morales et al., 1987; Paré et al., 1998; Picton et al., 2018; Yuan et al., 2005).

In addition to electrical or chemical synaptic excitation, mixed electrical-chemical synapses form gap junctions and release glutamate to recruit neurons (Nagy et al., 2019). Gap junctions provide a rapid, reliable source of depolarizing current and can enhance glutamate release at co-active mixed synapses (Alcamí and Pereda, 2019; Connors, 2017). While higher-density electrical synapses would more easily excite target neurons, at resting potentials gap junctions also provide a source of leak that decreases input resistance and creates parallel paths for current spread (Marder et al., 2017). In addition, since electrophysiological assessments of connectivity often rely on spike-triggered averages to reveal postsynaptic potentials (Mendell and Henneman, 1968), it could be challenging to disentangle the contribution of monosynaptic currents from coupling potentials propagating indirectly through electrical synapses (García-Pérez et al., 2004; Korn et al., 1973). Consequently, the existence of mixed synapses has the potential to not only confound electrophysiological assessments of neuronal excitability, but also connectivity.

Here, we have explored this possibility within the spinal locomotor circuitry of larval zebrafish. Mixed synapses formed by reticulospinal and propriospinal interneurons provide sources of excitation during locomotion in larval zebrafish (Bhatt et al., 2007; Kimura et al., 2006; McLean et al., 2008; Menelaou and McLean, 2019; Pujala and Koyama, 2019; Wang and McLean, 2014). They also appear early in zebrafish embryos (Miller et al., 2015; Saint-Amant and Drapeau, 2001) and persist into adulthood (Pallucchi et al., 2022; Song et al., 2016). Thus, it is likely that gap junctions in mixed synapses impact electrophysiological assessments in zebrafish of all ages.

Our previous studies of spinal motor neurons in larval zebrafish have identified slow, intermediate, and fast motor units recruited during faster swimming in order of size and input resistance (Bello-Rojas et al., 2019; McLean et al., 2007; Menelaou and McLean, 2012). This observation in fish is consistent with the ‘size principle’ originally formulated in felines (Henneman et al., 1965) and more recently observed in flies (Azevedo et al., 2020). In this scenario, larger neurons are less excitable at rest due to a greater number of membrane leak channels. However, among populations of premotor spinal interneurons that regulate motor neuron recruitment, the relationship between size, input resistance, and recruitment order is more complicated. For instance, soma size (McLean et al., 2007) and input resistance (Menelaou and McLean, 2019) are not always predictive of interneuron recruitment order, ‘violating’ the biophysical predictions of the size principle.

Mixed synapses could explain these discrepancies, assuming input resistance measures reflect not only interneuron size but also the specificity and density of gap junctions. To test this idea, we explored potential links between size, excitability, and connectivity among molecularly defined premotor excitatory and inhibitory interneurons that coordinate the head-tail propagation and left-right alternation of cyclical body bends during swimming at different speeds (Figure 1a and b). The data set included unpublished recordings (n=46) and new analysis of published recordings from excitatory chx10-labeled V2a interneurons (Menelaou and McLean, 2019), and inhibitory dbx1-labeled V0d interneurons (Menelaou and McLean, 2019) and dmrt3a-labeled dI6 interneurons (Kishore et al., 2020).

Figure 1

Download asset Open asset

Figure 1—source data 1

Source data from panels d, e and g in Figure 1.

https://cdn.elifesciences.org/articles/64063/elife-64063-fig1-data1-v1.xlsx

Download elife-64063-fig1-data1-v1.xlsx

Analysis of two distinct morphological classes of excitatory V2a neurons with either descending (V2a-D) or bifurcating (V2a-B) axon trajectories revealed largely overlapping sizes, but distinct input resistance values (Figure 1c and d, left). For inhibitory dI6 and V0d interneurons the opposite was true—neurons had largely overlapping input resistances, but distinct sizes (Figure 1d, middle). Within V2a subtypes, we found no significant relationship between size and input resistance, although lower resistance V2a-B neurons were largest (Figure 1d, left). As a result, combining the two subtypes generated a significant relationship within the V2a population (ρ₍₂₄₈₎=–0.61, p<0.001, n=250), albeit a weaker one compared to motor neurons (Figure 1d, right). For dI6 neurons, there was an even weaker correlation and no significant correlation for V0d neurons (Figure 1d, middle). These observations suggest size and membrane leak channels are not the exclusive arbiters of input resistance among interneurons.

Next, if mixed synapses were contributing to measurements of input resistance, we would expect lower membrane time constants in lower resistance neurons (Getting, 1974). Theoretically, increases in size should not impact time constants, following τ=resistance×capacitance, however gap junctions can provide higher density leak conductances that impact membrane resistance more than capacitance. Consistent with this idea, membrane time constants were significantly correlated with input resistance within and between interneuron populations, reaching 25 ms in the highest resistance neurons (Figure 1e, left, middle). The same relationship is observed in motor neurons one synapse downstream (Wang and McLean, 2014), however, values never exceeded 5 ms (Figure 1e, right). Thus, membrane time constants better predict input resistance than soma size for motor neurons and interneurons, consistent with the idea that mixed synapse density is contributing to measurements of resting excitability.

To further test this idea, we examined cyclical excitatory drive during ‘fictive’ escape swimming in response to a brief electrical stimulus (Figure 1f), which includes mixed inputs from reticulospinal and propriospinal sources (Menelaou and McLean, 2019; Pujala and Koyama, 2019; Wang and McLean, 2014). As expected from components of swimming locomotor circuitry, all interneuron populations received oscillatory excitatory drive at a range of cyclical swimming frequencies (Figure 1h–j). Peaks in maximum oscillatory drive could occur at different frequencies among higher resistance interneurons—for example, at lower frequencies that occur near the end of the swim episode for V2a neurons (Figure 1h, top) or at higher frequencies near the beginning for V0d neurons (Figure 1j, top). This is consistent with differences in synaptic-specificity, rather than input resistance, dictating interneuron recruitment probability at different frequencies (Ampatzis et al., 2014).

Within and between interneuron populations, the lowest resistance neurons received the largest amount of excitatory current (Figure 1g). Analysis of V2a neurons revealed a significant correlation in bifurcating and morphologically unidentified V2a neurons that was not apparent in descending V2a neurons (Figure 1g, left). Similarly, there was a significant correlation in V0d neurons that was not observed in dI6 neurons (Figure 1g, middle). Notably, V0d neurons are recruited more reliably at higher frequencies compared to dI6 neurons, which operate over a broader frequency range (Kishore et al., 2020; Satou et al., 2020). Pooled excitatory and inhibitory interneuron analysis closely resembled the relationship between excitatory drive and input resistance observed in motor neurons (Kishore et al., 2014), where peak levels are higher commensurate with larger sizes (Figure 1g, right). These data suggest that higher-density mixed synapses to spinal neurons recruited preferentially during high frequency escape swimming contribute to lower input resistances.

To test this idea more directly, we performed a series of analyses on previous recordings of synaptic connectivity originating from V2a neurons recruited specifically at high swimming frequencies (Menelaou and McLean, 2019). We have previously shown that larger, lower resistance bifurcating V2a neurons form predominantly mixed synapses with interneurons and motor neurons, characterized by an early electrical component and a later glutamatergic component (Figure 2a), which are sensitive to the AMPA receptor blocker NBQX and the gap junction blocker 18-β-glycyrrhetinic acid (18-βGA), respectively (Menelaou and McLean, 2019). Higher resistance descending V2a neurons target the same neurons, but use predominantly glutamatergic synapses (Figure 1b), characterized by a faster, larger NBQX-sensitive component and a slower, lower amplitude 18-βGA-sensitive component most obvious during failures (Menelaou and McLean, 2019).

Figure 2

Download asset Open asset

Figure 2—source data 1

Source data for panels d-l in Figure 2.

https://cdn.elifesciences.org/articles/64063/elife-64063-fig2-data1-v1.xlsx

Download elife-64063-fig2-data1-v1.xlsx

If mixed synapses are contributing to input resistance, we would expect larger PSPs in lower resistance neurons. On the other hand, if membrane leak channels are the primary determinant of shunt, we would expect smaller PSPs. When we plotted the amplitude of the electrical component of mixed synapses against input resistance, we found a significant negative correlation—larger amplitude electrical excitatory postsynaptic potentials (eEPSPs) are observed in lower resistance neurons (Figure 2c and d). Higher-density eEPSPs to lower resistance neurons were also reflected in the ‘Ohmic’ current calculated using V=IR (Figure 2e), with individual values exceeding 100 pA. In addition, larger amplitude eEPSPs were linked to lower failure rates of the chemical component (Figure 2f). This is consistent with enhanced synchronous chemical excitation by depolarization spreading through the gap junctions of co-active mixed synapses (Liu et al., 2020; Pereda et al., 2004; Song et al., 2016). No link to failure rates was observed for purely chemical EPSPs (cEPSPs), despite a similar range of amplitudes (Figure 2g).

Next, we took advantage of the fact that descending V2a neurons can target the same interneurons and motor neurons with purely glutamatergic synapses to see if excitation from chemical EPSPs would be weaker in lower resistance neurons at rest, per the biophysical predictions of Ohm’s law. Consistent with previous studies (Burke, 1968; Mendell and Henneman, 1971), the amplitude of chemical EPSPs was positively correlated with input resistance (Figure 2h), giving the impression that lower resistance neurons are less excitable, despite increases in Ohmic current up to 100 pA that could accommodate for decreased excitability (Figure 2i). We also observed a positive correlation between chemical EPSP decay time constant and input resistance (Figure 2j), as expected if mixed synapses are contributing to resistance measures and impacting integrative properties.

Thus far, the data suggest that gap junctions within mixed synapses are contributing to measurements of resting excitability in the zebrafish spinal cord. If so, we would expect that input resistance measurements would be sensitive to 18-βGA. Analysis of resistances before and after application revealed increased values in small and large neurons (Figure 2k), consistent with the broad contribution of gap junctions to electrophysiological measurements of excitability. The impact on excitability was specific to electrical synapses, since blockade of AMPA receptors or both AMPA and NMDA receptors had no significant effect on input resistance (Figure 2l, left). Critically, the relative impact of gap junction blockade was greatest in the lowest resistance neurons (Figure 2l, right). This is consistent with measurements of excitatory currents (Figure 1g and h) and postsynaptic potentials (Figure 2d and e), and a larger contribution of mixed synapses to leak measurements in lower resistance neurons.

Finally, to verify if mixed synapse density alone could explain our experimental observations, we turned to a user-friendly computational model (NeuroSim5; see Materials and methods). Different-sized model somata received simulated cEPSPs from a single source with identical amplitudes and waveforms (Figure 3a and b), assuming synaptic scaling with size following τ=RC. However, individual electrical synapse conductances were weighted to achieve larger eEPSPs in larger neurons (Figure 3b), per our observations. To simulate different patterns of synaptic convergence from mixed sources related to size, the largest neurons received 100% of total inputs, while the smallest only 10% (Figure 3a). This approximates the tenfold difference in excitatory synapse number reported in larval zebrafish motor neurons (Bello-Rojas et al., 2019). By increasing the number of convergent sources, the model allowed us to explore both size-dependent and size-independent changes in resting excitability via increases in electrical synapse density.

Figure 3

Download asset Open asset

Figure 3—source data 1

Source data for panels d, e, f, and h in Figure 3.

https://cdn.elifesciences.org/articles/64063/elife-64063-fig3-data1-v1.xlsx

Download elife-64063-fig3-data1-v1.xlsx

As expected, increases in the number of convergent sources of electrical synapses led to systematic decreases in measurements of input resistance using simulated current injection (Figure 3c), which were most obvious in the largest, lowest resistance neurons (Figure 3d,e). We observed systematic decreases not only in the amplitude (Figure 3c), but also the time constant (Figure 3f) of simulated chemical PSPs, increasing the dynamic range of integration. By varying the amount of convergent sources of electrical synapses, size and input resistance could be uncoupled (Figure 3d). This helps explain how interneurons can be the same size with different input resistances (Figure 1d, left) or different sizes but the same input resistances (Figure 1d, middle). If mixed synapses are often used in high-frequency swimming circuitry, then differences in convergence could also explain recruitment out of order based on size and input resistance (McLean et al., 2007; Menelaou and McLean, 2019)—higher density electrical synapses lower resistance at rest independent of size, but during activity provide stronger excitation to increase recruitment probability.

One consequence of increasing the convergence of different sources of electrical synapses is the creation of parallel paths for current to spread (Marder et al., 2017). To examine this more closely, we simulated targeted chemical inputs to neurons of different sizes at maximum electrical synapse convergence (Figure 3g, left) and measured the amplitude of the coupling potentials (Figure 3g, right). As expected, the amplitude of the direct chemical EPSP varied as a function of size and convergence ratio (Figure 3g), with current distributed predominantly to neurons sharing the most afferents (Figure 3h). The low pass filtering provided by electrical synapses generated slower coupling potentials that were orders of magnitude lower in amplitude (Figure 3g and h). Simulated coupling potentials resembled slower, lower amplitude 18-βGA-sensitive potentials observed during chemical failures (Figure 2b, bottom) and in unconnected pairs (Menelaou and McLean, 2019). We and others have argued these filtered potentials reflect indirect electrical continuity through gap junctions (García-Pérez et al., 2004; Korn et al., 1973), suggesting they more accurately reflect shared afferents than direct efferents.

Collectively, our findings suggest that mixed synapses contribute to excitability underestimations in zebrafish spinal cord and help reconcile reported violations of the size principle. We also find that mixed synapses can contribute to connectivity overestimations by acting as conduits for synaptic current and confirm coupling potentials are easily distinguished based on kinetics (García-Pérez et al., 2004; Menelaou and McLean, 2019). Studies of the premotor Ia reflex pathway in cats have revealed dual and single component EPSPs with different kinetics (Burke, 1968; Mendell and Henneman, 1971). These were attributed to filtering by motor neuron dendrites and differences in the density of leak channels (Gustafsson and Pinter, 1984; Rall et al., 1967), but could also reflect axon collaterals connecting motor neuron somata via mixed synapses. In mammals, mixed synapses are found in the spinal cord (Rash et al., 1996), Ia circuit function is disrupted in connexin36 mutants (Bautista et al., 2012), and larger, faster motor units receive denser connexin36 innervation from excitatory V0 interneurons (Recabal-Beyer et al., 2022). So, mixed synapses could also help reconcile conflicting observations related to size, synaptic drive, and neuronal excitability in mammalian spinal cord (Burke, 1981; Enoka and Stuart, 1984; Henneman, 1985).

More broadly, connexin36 and vesicular glutamate transporter co-localization are observed in a variety of brain circuits optimized for processing high-frequency information reliably at speed (Nagy et al., 2019). The use of electrical synapses likely expands neuronal integrative properties beyond what can be achieved by leak channels and size alone (Alcamí and Pereda, 2019; Galarreta and Hestrin, 2001). This would enable rate and/or timing codes to execute orderly patterns of recruitment among targeted ensembles of motor neurons and spinal interneurons using temporal summation (Wang and McLean, 2014), as observed in other sensory and motor circuits (Ainsworth et al., 2012; Sober et al., 2018). In this scenario, links between input resistance and recruitment order arise by gradations in mixed synapse density and convergence among various sized neurons. This could explain why the size principle best predicts function among motor neurons, which have the maximum capacity for synaptic convergence as the ‘final common path’ for all behavioral output (Sherrington, 1906).

All data generated or analyzed during this study are included in the manuscript and supporting files. Source data files are provided for Figures 1-3.

1. 1. Ainsworth M
   2. Lee S
   3. Cunningham MO
   4. Traub RD
   5. Kopell NJ
   6. Whittington MA

   (2012) Rates and rhythms: a synergistic view of frequency and temporal coding in neuronal networks

   Neuron 75:572–583.

   https://doi.org/10.1016/j.neuron.2012.08.004

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

   (2014) Separate microcircuit modules of distinct v2a interneurons and motoneurons control the speed of locomotion

   Neuron 83:934–943.

   https://doi.org/10.1016/j.neuron.2014.07.018

   • PubMed
   • Google Scholar
4. 1. Azevedo AW
   2. Dickinson ES
   3. Gurung P
   4. Venkatasubramanian L
   5. Mann RS
   6. Tuthill JC

   (2020) A size principle for recruitment of Drosophila leg motor neurons

   eLife 9:e56754.

   https://doi.org/10.7554/eLife.56754

   • PubMed
   • Google Scholar
5. 1. Balciunas D
   2. Davidson AE
   3. Sivasubbu S
   4. Hermanson SB
   5. Welle Z
   6. Ekker SC

   (2004) Enhancer trapping in zebrafish using the sleeping Beauty transposon

   BMC Genomics 5:62.

   https://doi.org/10.1186/1471-2164-5-62

   • PubMed
   • Google Scholar
6. 1. Bautista W
   2. Nagy JI
   3. Dai Y
   4. McCrea DA

   (2012) Requirement of neuronal connexin36 in pathways mediating presynaptic inhibition of primary afferents in functionally mature mouse spinal cord

   The Journal of Physiology 590:3821–3839.

   https://doi.org/10.1113/jphysiol.2011.225987

   • PubMed
   • Google Scholar
7. 1. Bello-Rojas S
   2. Istrate AE
   3. Kishore S
   4. McLean DL

   (2019) Central and peripheral innervation patterns of defined axial motor units in larval zebrafish

   The Journal of Comparative Neurology 527:2557–2572.

   https://doi.org/10.1002/cne.24689

   • PubMed
   • Google Scholar
8. 1. Bhatt DH
   2. McLean DL
   3. Hale ME
   4. Fetcho JR

   (2007) Grading movement strength by changes in firing intensity versus recruitment of spinal interneurons

   Neuron 53:91–102.

   https://doi.org/10.1016/j.neuron.2006.11.011

   • PubMed
   • Google Scholar
9. 1. Burke RE

   (1968) Group IA synaptic input to fast and slow twitch motor units of cat triceps surae

   The Journal of Physiology 196:605–630.

   https://doi.org/10.1113/jphysiol.1968.sp008526

   • PubMed
   • Google Scholar
10. Book

    1. Burke RE

    (1981)

    Motor units: anatomy, physiology, and functional organization

    In: Brooks VB, editors. In Handbook of Physiology, The Nervous System. Motor Control: Am. Physiol. Soc. pp. 345–422.

    • Google Scholar
11. 1. Connors BW

    (2017) Synchrony and so much more: diverse roles for electrical synapses in neural circuits

    Developmental Neurobiology 77:610–624.

    https://doi.org/10.1002/dneu.22493

    • PubMed
    • Google Scholar
12. 1. Enoka RM
    2. Stuart DG

    (1984) Henneman ’ S ‘ size principle ’: current issues

    Trends in Neurosciences 7:226–228.

    https://doi.org/10.1016/S0166-2236(84)80210-6

    • Google Scholar
13. 1. Galarreta M
    2. Hestrin S

    (2001) Electrical synapses between GABA-releasing interneurons

    Nature Reviews. Neuroscience 2:425–433.

    https://doi.org/10.1038/35077566

    • PubMed
    • Google Scholar
14. 1. García-Pérez E
    2. Vargas-Caballero M
    3. Velazquez-Ulloa N
    4. Minzoni A
    5. De-Miguel FF

    (2004) Synaptic integration in electrically coupled neurons

    Biophysical Journal 86:646–655.

    https://doi.org/10.1016/S0006-3495(04)74142-9

    • PubMed
    • Google Scholar
15. 1. Getting PA

    (1974) Modification of neuron properties by electrotonic synapses. I. input resistance, time constant, and integration

    Journal of Neurophysiology 37:846–857.

    https://doi.org/10.1152/jn.1974.37.5.846

    • PubMed
    • Google Scholar
16. 1. Gustafsson B
    2. Pinter MJ

    (1984) Relations among passive electrical properties of lumbar alpha-motoneurones of the cat

    The Journal of Physiology 356:401–431.

    https://doi.org/10.1113/jphysiol.1984.sp015473

    • PubMed
    • Google Scholar
17. 1. Henneman E
    2. Somjen G
    3. Carpenter DO

    (1965) Functional significance of cell size in spinal motoneurons

    Journal of Neurophysiology 28:560–580.

    https://doi.org/10.1152/jn.1965.28.3.560

    • PubMed
    • Google Scholar
18. 1. Henneman E

    (1985) The size-principle: a deterministic output emerges from a set of probabilistic connections

    The Journal of Experimental Biology 115:105–112.

    https://doi.org/10.1242/jeb.115.1.105

    • PubMed
    • Google Scholar
19. 1. Kimura Y
    2. Okamura Y
    3. Higashijima S

    (2006) Alx, a zebrafish homolog of CHX10, marks ipsilateral descending excitatory interneurons that participate in the regulation of spinal locomotor circuits

    The Journal of Neuroscience 26:5684–5697.

    https://doi.org/10.1523/JNEUROSCI.4993-05.2006

    • PubMed
    • Google Scholar
20. 1. Kishore S
    2. Bagnall MW
    3. McLean DL

    (2014) Systematic shifts in the balance of excitation and inhibition coordinate the activity of axial motor pools at different speeds of locomotion

    The Journal of Neuroscience 34:14046–14054.

    https://doi.org/10.1523/JNEUROSCI.0514-14.2014

    • PubMed
    • Google Scholar
21. 1. Kishore S
    2. Cadoff EB
    3. Agha MA
    4. McLean DL

    (2020) Orderly compartmental mapping of premotor inhibition in the developing zebrafish spinal cord

    Science 370:431–436.

    https://doi.org/10.1126/science.abb4608

    • PubMed
    • Google Scholar
22. 1. Korn H
    2. Sotelo C
    3. Crepel F

    (1973) Electronic coupling between neurons in the rat lateral vestibular nucleus

    Experimental Brain Research 16:255–275.

    https://doi.org/10.1007/BF00233330

    • PubMed
    • Google Scholar
23. 1. Liu P
    2. Chen B
    3. Mailler R
    4. Wang ZW

    (2020) Antidromic-rectifying gap junctions amplify chemical transmission at functionally mixed electrical-chemical synapses

    Nature Communications 8:14818.

    https://doi.org/10.1038/ncomms14818

    • PubMed
    • Google Scholar
24. 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
25. 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
26. 1. McLean DL
    2. Masino MA
    3. Koh IYY
    4. Lindquist WB
    5. Fetcho JR

    (2008) Continuous shifts in the active set of spinal interneurons during changes in locomotor speed

    Nature Neuroscience 11:1419–1429.

    https://doi.org/10.1038/nn.2225

    • PubMed
    • Google Scholar
27. 1. Mendell LM
    2. Henneman E

    (1968) Terminals of single Ia fibers: distribution within a pool of 300 homonymous motor neurons

    Science 160:96–98.

    https://doi.org/10.1126/science.160.3823.96

    • PubMed
    • Google Scholar
28. 1. Mendell LM
    2. Henneman E

    (1971) Terminals of single Ia fibers: location, density, and distribution within a pool of 300 homonymous motoneurons

    Journal of Neurophysiology 34:171–187.

    https://doi.org/10.1152/jn.1971.34.1.171

    • PubMed
    • Google Scholar
29. 1. Menelaou E
    2. McLean DL

    (2012) A gradient in endogenous rhythmicity and oscillatory drive matches recruitment order in an axial motor pool

    The Journal of Neuroscience 32:10925–10939.

    https://doi.org/10.1523/JNEUROSCI.1809-12.2012

    • PubMed
    • Google Scholar
30. 1. Menelaou E
    2. McLean DL

    (2019) Hierarchical control of locomotion by distinct types of spinal v2a interneurons in zebrafish

    Nature Communications 10:4197.

    https://doi.org/10.1038/s41467-019-12240-3

    • PubMed
    • Google Scholar
31. 1. Miller AC
    2. Voelker LH
    3. Shah AN
    4. Moens CB

    (2015) Neurobeachin is required postsynaptically for electrical and chemical synapse formation

    Current Biology 25:16–28.

    https://doi.org/10.1016/j.cub.2014.10.071

    • PubMed
    • Google Scholar
32. 1. Morales FR
    2. Boxer PA
    3. Fung SJ
    4. Chase MH

    (1987) Basic electrophysiological properties of spinal cord motoneurons during old age in the cat

    Journal of Neurophysiology 58:180–194.

    https://doi.org/10.1152/jn.1987.58.1.180

    • PubMed
    • Google Scholar
33. 1. Nagy JI
    2. Pereda AE
    3. Rash JE

    (2019) On the occurrence and enigmatic functions of mixed (chemical plus electrical) synapses in the mammalian CNS

    Neuroscience Letters 695:53–64.

    https://doi.org/10.1016/j.neulet.2017.09.021

    • PubMed
    • Google Scholar
34. 1. Pallucchi I
    2. Bertuzzi M
    3. Michel JC
    4. Miller AC
    5. El Manira A

    (2022) Transformation of an early-established motor circuit during maturation in zebrafish

    Cell Reports 39:110654.

    https://doi.org/10.1016/j.celrep.2022.110654

    • PubMed
    • Google Scholar
35. 1. Paré D
    2. Shink E
    3. Gaudreau H
    4. Destexhe A
    5. Lang EJ

    (1998) Impact of spontaneous synaptic activity on the resting properties of cat neocortical pyramidal neurons in vivo

    Journal of Neurophysiology 79:1450–1460.

    https://doi.org/10.1152/jn.1998.79.3.1450

    • PubMed
    • Google Scholar
36. 1. Pereda AE
    2. Rash JE
    3. Nagy JI
    4. Bennett MVL

    (2004) Dynamics of electrical transmission at Club endings on the Mauthner cells

    Brain Research. Brain Research Reviews 47:227–244.

    https://doi.org/10.1016/j.brainresrev.2004.06.010

    • PubMed
    • Google Scholar
37. 1. Picton LD
    2. Sillar KT
    3. Zhang HY

    (2018) Control of Xenopus tadpole locomotion via selective expression of Ih in excitatory interneurons

    Current Biology 28:3911–3923.

    https://doi.org/10.1016/j.cub.2018.10.048

    • PubMed
    • Google Scholar
38. 1. Pujala A
    2. Koyama M

    (2019) Chronology-based architecture of descending circuits that underlie the development of locomotor repertoire after birth

    eLife 8:e42135.

    https://doi.org/10.7554/eLife.42135

    • PubMed
    • Google Scholar
39. 1. Rall W
    2. Burke RE
    3. Smith TG
    4. Nelson PG
    5. Frank K

    (1967) Dendritic location of synapses and possible mechanisms for the monosynaptic EPSP in motoneurons

    Journal of Neurophysiology 30:1169–1193.

    https://doi.org/10.1152/jn.1967.30.5.1169

    • PubMed
    • Google Scholar
40. 1. Rash JE
    2. Dillman RK
    3. Bilhartz BL
    4. Duffy HS
    5. Whalen LR
    6. Yasumura T

    (1996) Mixed synapses discovered and mapped throughout mammalian spinal cord

    PNAS 93:4235–4239.

    https://doi.org/10.1073/pnas.93.9.4235

    • PubMed
    • Google Scholar
41. 1. Recabal-Beyer AJ
    2. Senecal JMM
    3. Senecal JEM
    4. Lynn BD
    5. Nagy JI

    (2022) On the organization of connexin36 expression in electrically coupled cholinergic v0c neurons (partition cells) in the spinal cord and their C-terminal innervation of motoneurons

    Neuroscience 485:91–115.

    https://doi.org/10.1016/j.neuroscience.2022.01.015

    • PubMed
    • Google Scholar
42. 1. Saint-Amant L
    2. Drapeau P

    (2001) Synchronization of an embryonic network of identified spinal interneurons solely by electrical coupling

    Neuron 31:1035–1046.

    https://doi.org/10.1016/s0896-6273(01)00416-0

    • PubMed
    • Google Scholar
43. 1. Satou Chie
    2. Kimura Y
    3. Higashijima S

    (2012) Generation of multiple classes of V0 neurons in zebrafish spinal cord: progenitor heterogeneity and temporal control of neuronal diversity

    The Journal of Neuroscience 32:1771–1783.

    https://doi.org/10.1523/JNEUROSCI.5500-11.2012

    • PubMed
    • Google Scholar
44. 1. Satou C
    2. Sugioka T
    3. Uemura Y
    4. Shimazaki T
    5. Zmarz P
    6. Kimura Y
    7. Higashijima SI

    (2020) Functional diversity of glycinergic commissural inhibitory neurons in larval zebrafish

    Cell Reports 30:3036–3050.

    https://doi.org/10.1016/j.celrep.2020.02.015

    • PubMed
    • Google Scholar
45. Book

    1. Sherrington CS

    (1906)

    The Integrative Action of the Nervous System

    New Haven, CT: Yale University Press.

    • Google Scholar
46. 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
47. 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
48. 1. Wang WC
    2. McLean DL

    (2014) Selective responses to tonic descending commands by temporal summation in a spinal motor pool

    Neuron 83:708–721.

    https://doi.org/10.1016/j.neuron.2014.06.021

    • PubMed
    • Google Scholar
49. 1. Yuan W
    2. Burkhalter A
    3. Nerbonne JM

    (2005) Functional role of the fast transient outward K+ current Ia in pyramidal neurons in (rat) primary visual cortex

    The Journal of Neuroscience 25:9185–9194.

    https://doi.org/10.1523/JNEUROSCI.2858-05.2005

    • PubMed
    • Google Scholar

Author details

1. Evdokia Menelaou

   Department of Neurobiology, Northwestern University, Evanston, United States

   Contribution

   Formal analysis, Investigation, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

2. Sandeep Kishore

   Department of Neurobiology, Northwestern University, Evanston, United States

   Contribution

   Formal analysis, Investigation, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

3. David L McLean

   Department of Neurobiology, Northwestern University, Evanston, United States

   Contribution

   Conceptualization, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Writing – original draft, Writing – review and editing

   For correspondence

   david-mclean@northwestern.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6337-2301

• 1,321

  views

• 177

  downloads

• 3

  citations

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