Putting the theory into ‘burstlet theory’ with a biophysical model of burstlets and bursts in the respiratory preBötzinger complex

  1. Ryan S Phillips
  2. Jonathan E Rubin

  • Curated by eLife

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    Evaluation Summary:

    This manuscript is of significant interest to readers in the field of neural control of breathing and for researches interested in the generation of biological rhythms in general. The study assembles a sophisticated computational modelling approach to test long-standing theories and emerging views in neural control of breathing and more specifically on biophysical mechanisms of burstlet generation in the respiratory network (the preBötzinger complex network). This work is an important contribution to a better understanding of the respiratory rhythm generation, will help validate (or not) running hypotheses and will guide future experiments.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 and Reviewer #3 agreed to share their names with the authors.)

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Abstract

Inspiratory breathing rhythms arise from synchronized neuronal activity in a bilaterally distributed brainstem structure known as the preBötzinger complex (preBötC). In in vitro slice preparations containing the preBötC, extracellular potassium must be elevated above physiological levels (to 7–9 mM) to observe regular rhythmic respiratory motor output in the hypoglossal nerve to which the preBötC projects. Reexamination of how extracellular K + affects preBötC neuronal activity has revealed that low-amplitude oscillations persist at physiological levels. These oscillatory events are subthreshold from the standpoint of transmission to motor output and are dubbed burstlets. Burstlets arise from synchronized neural activity in a rhythmogenic neuronal subpopulation within the preBötC that in some instances may fail to recruit the larger network events, or bursts, required to generate motor output. The fraction of subthreshold preBötC oscillatory events (burstlet fraction) decreases sigmoidally with increasing extracellular potassium. These observations underlie the burstlet theory of respiratory rhythm generation. Experimental and computational studies have suggested that recruitment of the non-rhythmogenic component of the preBötC population requires intracellular Ca 2+ dynamics and activation of a calcium-activated nonselective cationic current. In this computational study, we show how intracellular calcium dynamics driven by synaptically triggered Ca 2+ influx as well as Ca 2+ release/uptake by the endoplasmic reticulum in conjunction with a calcium-activated nonselective cationic current can reproduce and offer an explanation for many of the key properties associated with the burstlet theory of respiratory rhythm generation. Altogether, our modeling work provides a mechanistic basis that can unify a wide range of experimental findings on rhythm generation and motor output recruitment in the preBötC.

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  1. Version published to 10.7554/elife.75713 on eLife
  2. eLife

    Author Response:

    Reviewer #1 (Public Review):

    Phillips and Rubin investigated the biophysical mechanisms that underly the generation of respiratory related burstlets and bursts in the rhythmogenic pre-Botzinger circuit. They show, suing a computational modelling approach, that synaptically mediated intracellular calcium concentrations and Ca2+ release and uptake via the endoplasmic reticulum are a unifying mechanism of the generation of the respiratory rhythm and its recruitment at the motoneuronal level. This explanatory model is able to unify contradictory experimental findings and is further evaluated by modelling the effect of opioids on the generation of burstlets and bursts in pre-Botzinger complex.

    The conclusions of this paper are mostly well supported by a sound computational modelling approach, however the current computational modelling data are largely based on experimental data of very few workgroups, while previous modelling approaches and experimental data that support anatomical network connectivity as a key feature for respiratory rhythm generation and transmission of burstlet/bursts to motorneuron pool were neglected.

    To our knowledge, the work supporting anatomical network connectivity as a key feature that the reviewer mentions (Ashhad & Feldman, Neuron, 2020; Slepukhin et al., arXiv: 2012.12486) makes the claim that in a general setting involving spread of activity by percolation without any additional biophysical mechanisms (such as synaptic plasticity, cf. Guerrier et al., PNAS, 2015), a specific distribution of synaptic weights is needed to produce burstlet generation that agrees with experimental findings. Our work shows that random connectivity without specially distributed weights is sufficient in a setting in which a fraction of neurons are endogenous bursters and the calcium-related recruitment mechanisms that we model are also present. Our approach of studying this biophysical framework independently from the alternative, based entirely on anatomical network connectivity, is important in order to characterize what properties can follow from the biophysical framework on its own. Once our paper is published, the two complementary frameworks and their corresponding predictions will be represented in the literature and can be tested in future experiments. We contend that this is more useful than if we had tried to combine both biophysical and connection-based frameworks in the present model, before considering the former on its own.

    The current model is exclusively focused on biophysical properties and in particular calcium dynamics to generate a unifying computational model for respiratory rhythm generation. However, a previous model and experimental data suggest the emergence of rhythmogenic activity in pre-Botzinger complex may be largely determined by the local network connectivity as well as by the connectivity of the pre-Botzinger complex with the extended medullary and caudal pontine respiratory circuit. It would be interesting if this crucial component of a truly unifying model could be added or at least needs to be discussed appropriately.

    We fully acknowledge that our work focuses on rhythm-generation and pattern-generation mechanisms in the preBotC. It is quite possible that these mechanisms can only function at full capacity when the neurons involved receive inputs from other parts of the extended respiratory circuit (e.g., Jones and Dutschmann, J. Neurophysiol., 2016). These inputs, for example, might help set the level of excitability of preBotC neurons (cf. Smith et al., J. Neurophysiol., 2007). Although adding any sort of detailed representation of this extended circuit is beyond the scope of this work, we agree that it is important to mention, to help put our work in a bigger picture context, and we have added text about this point in our revised Discussion and in our responses below to Reviewer #1’s Recommendations for the authors.

    Reviewer #2 (Public Review):

    Since its isolation in the transverse slice in 1991 (1), researchers have studied the preBotC with a focus on 2 related questions: how respiratory rhythm is generated, and how this rhythm is transformed into the pattern of inspiratory bursts, which are recorded at hypoglossal rootlets (XIIn), and in more intact preparations, from cervical ventral roots. The discovery of burstlets has provided support for the conjecture that the preBotC can be functionally parcellated into rhythm-generating and pattern-forming networks (2).

    Others have proposed that burstlets are rhythmogenic (2-4), via a stochastic percolation mechanism that synchronizes tonic spiking into burstlets, which in turn give rise to inspiratory drive. Phillips and Rubin challenge this account for its lack of detail and argue that it is weakly supported by features of burstlets that can be otherwise accounted for (burstlet onset and slope; percolation of activation following photostimulation of network subsets). Their points are well-taken, but two points need to be made. First, the percolation conjecture has gained traction because other more conventional mechanisms for rhythmogenesis have been shown not to apply, as nicely summarized in (4, 5). In particular, rhythmogenesis arising out of the activity of endogenous bursters (as is the case in their model) has failed to find empirical support: optical recordings of respiratory networks under conditions of synaptic blockade have not revealed appreciable numbers of endogenous bursters (this despite the fact that groups are recording from slices expressing genetically-encoded Ca2+ indicators in glutamatergic neurons in preBotC, using two-photon microscopy), older studies using conventional patch clamp methods identified negligible numbers of endogenous bursters (6, 7), and pharmacological disruption of specific endogenous bursting mechanisms has not silenced respiratory rhythm in the slice (8). Second, and more importantly, the processes that they model mediate the amplification of burstlets to bursts, and thus don't have obvious bearing on whether or not burstlets are generated by stochastic percolation or endogenous burster activity. As they state (lines 605-607) "the findings about burstlets and bursts presented in this work would have been obtained if the burstlet rhythm was imposed (Fig. 1) or if burstlets were generated by some other means". Thus a discussion of the inadequacies of current percolation models of respiratory rhythmogenesis seems to be irrelevant to their main points, and fails to acknowledge that other more plausible mechanisms have been set aside because they were undermined by experimental findings. They should either explain why a percolation-based mechanism for the emergence of burstlets is incompatible with their model for CIRC-mediated amplification of burstlets to network bursts, or they should remove these arguments, which distract from their main findings.

    We acknowledge that different experiments, done using different approaches and in various settings, have yielded heterogeneous results about the prevalence and importance of endogenous bursters in respiratory rhythm generation. Importantly, the emphasis in our work is on the biophysical mechanisms associated with neural recruitment in the burstlet-to-burst transition. We do not make the claim that a percolation-based mechanism for the emergence of burstlets is incompatible with our ideas about this transition. We have added a critical phrase to this text to clarify this point. Importantly, in the revision process, we have also performed additional simulations, now included in the paper, which show that our model gives similar recruitment and amplification of activity when INaP-based bursting is replaced by imposing rhythmic activity on the burstlet population; see Figure 4-Figure Supplement 1.

    It would be useful if the authors could elaborate on the applicability of their findings to less reduced preparations. The emergence of burstlets as well as the transition from predominantly burstlets to predominantly bursts is strongly dependent on network excitability, which is controlled by varying [K+]bath. Importantly, in their simulations burstlet activity falls silent at [K+]bath < 4 mM, and robust motor output only emerges for [K+]bath > 8 mM. While these modeling results jibe well with experimental results in the slice preparation, in more intact preparations, robust and stable respiratory rhythm is maintained at physiological levels ([K+]bath = 3 mM); in intact animals, 9 mM [K+]o is lethal. This raises the question of whether their model has explanatory power for respiratory rhythmogenesis in more intact preparations, or whether it is limited to describing fictive respiration in the slice.

    We thank the reviewer for highlighting this important point. The relevance of the current simulations to more intact preparations is now discussed in the revised manuscript. In in vitro slice preparations the preBotC generally lacks sufficient intrinsic excitability to generate an inspiratory rhythm presumably due to the loss of excitatory drive from higher brainstem centers such as the KF, PB, RTN, RO; see Smith et al., J. Neurophysiol 2007. As such, elevating extracellular potassium in in vitro slice preparations is a standard procedure in the field as it is required for generating rhythmic output under these conditions. However, elevating the preBotC excitability by other means such as manipulations of extracellular Ca2+ does not appear to significantly impact preBotC rhythmicity (Ruangkittisakul et al., Respir Physiol Neurobiol. 2011) or the generation of bursts and burstlets (Kam et al., J. Neurosci. 2013). Moreover, burstlets can be seen in vivo via manipulations of preBotC and BotC excitability (Kam et al., J. Neurosci. 2013). Therefore, understanding burstlet and burst generation in the reduced preBotC slice preparation is highly relevant in more intact preparations. Investigation of how these higher brainstem centers impact preBotC inspiratory rhythm and pattern generation are beyond the scope of this study.

    Another aspect to this problem is that the rhythmogenic mechanism they have incorporated in their model has strong dependence on [K+]bath; this straight-forwardly accounts for the transition from quiescence to burstlets, since within their models, endogenous bursters are quiescent at lower excitability levels. Of greater interest is the extent to which the [K+]bath dependence of the transition from burstlets to inspiratory bursting is again due to their choice of endogenous burster implementation. This is important, because it might enable CIRCA-mediated transitions from burstlets to network bursting that show less steep voltage dependence, and which are robust at more physiological [K+]o, thus enhancing the generalizability of their model. This was certainly a feature of an early study, in which I(CAN) was proposed as a mechanism for endogenous bursting, with weaker voltage dependence than the I(NaP)-based endogenous bursters implemented here (7).

    Our response to this comment has several aspects, which we present sequentially below.

    1. Qualitatively, the Kbath dependence of our model matches well with the Kbath dependence shown in Kallurkar et al., eNeuro 2019. That is, the burstlet frequency and bustlet amplitude increase with Kbath. We are unaware of any data that shows a different Kbath vs burstlet frequency relationship. Moreover, if PSynCa is increased with Kbath our model produces changes in the burstlet fraction that closely match those seen experimentally.

    2. It is not clear why we would want to show a less steep Kbath-dependence (we are assuming that was what the reviewer was commenting about, rather than voltage dependence) with the burstlet to burst transition (burstlet fraction). First, our model actually has a very shallow slope in the Kbath vs burstlet fraction curve if Kbath is the only parameter varied (PSynCa=fixed). This can be seen by considering a horizontal slice through Fig. 4 D-F. In order to match the slope of the Kbath vs burstlet fraction curve in experimental data we actually needed to make the slope between Kbath and the burstlet fraction steeper. This was achieved by increasing PsynCa with Kbath, Fig. 4I. The justification of increasing PSynCa is presented on lines 422-442 of the discussion.

    3. Indeed, some earlier work has identified Cd2+ sensitive and presumably Ca2+/ICAN dependent endogenous bursters and these bursters showed weaker voltage-dependence (Thoby-Brisson Journal of neurophysiology 2001). However, more recent studies have shown that ICAN is not involved in rhythm generation (Koizumi et al. 2018, Picardo et al., 2019). Similarly, application of Cd2+ which should eliminate Ca2+/ICAN dependent bursters does not affect rhythm generation, and instead blocks preBotC motor output (Kam et al., 2013, Sun et al., 2019). That is, Cd2+ application eliminates preBotC network bursts but does not affect the ongoing burstlet rhythm.

    4. The Kbath value of approximately 5mM where endogenous bursting first emerges in our model is also in agreement with existing experimental data (Del Negro et al., 2001, Mellen et al., 2010).

    5. In our model, a burstlet transitions into a burst when postsynaptic calcium transients in the pattern generating subpopulation are of a sufficient magnitude to trigger CICR, which leads to ICAN activation, depolarization, and ultimately recruitment of these neurons into a network burst. The rhythm in the burstlet population is driving the postsynaptic Ca2+ transient in the pattern generating subpopulation. The mechanism that is driving the burstlet rhythm has no impact on the dynamics of the postsynaptic Ca2+ transients, CICR, ICAN activation or recruitment of the pattern generating population. Therefore, the mechanism of rhythm generation does not impact the proposed mechanism underlying the burstlet to burst transition. However, to illustrate this point we imposed a rhythm in the neurons comprising the rhythmogenic subpopulation. This was achieved by imposing a Poisson spike train in each neuron where the frequency was zero during the interburst interval. At the start of the burstlet the frequency linearly ramps up over 250ms to an imposed maximum firing rate and then linearly ramps back down to zero over an additional 250mS, see Figure 4-figure supplement 1A. The burstlet frequency was set by changing the interburst interval and the amplitude was set by changing the maximum firing rate at the peak of the burstlet, Figure 4-figure supplement 1 panels A and B. These simulations show that this network produces qualitatively similar bursts and burstlets which depend on a CICR-mediated burstlet-to-burst recruitment process. Moreover, as in Fig. 4, the burstlet fraction primarily depends on Psynca, whereas the burst frequency depends on Psynca and the burstlet frequency.

    1. Smith JC, Ellenberger HH, Ballanyi K, Richter DW, Feldman JL. Pre-Botzinger complex: a brainstem region that may generate respiratory rhythm in mammals. Science. 1991;254(5032):726-9.
    2. Kam K, Worrell JW, Janczewski WA, Cui Y, Feldman JL. Distinct inspiratory rhythm and pattern generating mechanisms in the preBotzinger complex. J Neurosci. 2013;33(22):9235-45.
    3. Ashhad S, Feldman JL. Emergent Elements of Inspiratory Rhythmogenesis: Network Synchronization and Synchrony Propagation. Neuron. 2020;106(3):482-97 e4.
    4. Kallurkar PS, Grover C, Picardo MCD, Del Negro CA. Evaluating the Burstlet Theory of Inspiratory Rhythm and Pattern Generation. eNeuro. 2020;7(1).
    5. Del Negro CA, Funk GD, Feldman JL. Breathing matters. Nat Rev Neurosci. 2018;19(6):351-67.
    6. Del Negro CA, Koshiya N, Butera RJ, Jr., Smith JC. Persistent sodium current, membrane properties and bursting behavior of pre-botzinger complex inspiratory neurons in vitro. J Neurophysiol. 2002;88(5):2242-50.
    7. Thoby-Brisson M, Ramirez JM. Identification of two types of inspiratory pacemaker neurons in the isolated respiratory neural network of mice. J Neurophysiol. 2001;86(1):104-12.
    8. Del Negro CA, Morgado-Valle C, Feldman JL. Respiratory rhythm: an emergent network property? Neuron. 2002;34(5):821-30.
    9. Del Negro CA, Johnson SM, Butera RJ, Smith JC. Models of respiratory rhythm generation in the pre-Botzinger complex. III. Experimental tests of model predictions. J Neurophysiol. 2001;86(1):59-74.

    Reviewer #3 (Public Review):

    A key contribution of this study includes a demonstration that two sets of neurons coupled via excitation can drive network activity similar to that observed in mXII nerve during breathing. In particular, the authors formulate a link between synaptic excitation and intracellular calcium induced Ca2+ release mechanisms via positive feedback from ICaN. This forms the basis for recruitment of non-rhythmogenic neurons by rhythm generating neurons. Such a formulation seems to help explain the burstlet theory and support a percolation theory of network bursts put forward in the field.

    The manuscript is well written. Figures and figure legends are clear, and justify the results stated. Methodology is well laid out; however, missed references in many places where it is not clear where the equations came from (e.g., equations 19 through 24 and elsewhere). The authors state that the model code "will" be available on ModelDB. This should instead be submitted with the manuscript for review. Later, the code must be made available on a GitHub repository for wide dissemination and future updates by others. ModelDB has models which can only be downloaded but not extended for wider use. Oftentimes this leads to lack of technical help for future users and limits model use and enhancement.

    We thank the reviewer for the positive comments about readability and clarity. The reviewer makes an excellent point about ensuring that code sharing occurs in a way that allows for future use. We have made our model code available on GitHub at the following link:

    https://github.com/RyanSeanPhillips/Putting-the-theory-into-burstlet-theory

    The authors put forward a plausible mechanistic explanation for Ca2+ dependent recruitment of non-rhythmogenic neurons by linking synaptic excitation from the rhythm generating neurons to CICR in the former. Although attractive, there are numerous factors which control intracellular Ca2+ signaling and buffering. It would be important to clarify whether the assumption that dendritic depolarization due to synaptic inputs directly contributes to CICR as postulated in this model (the term PsynCa*Isyn in equation 19), has any (direct/indirect) empirical support either in preBotz neurons or elsewhere to ensure that this is not purely conjectural.

    The link between postsynaptic Ca2+ transients and CICR in the pattern generating preBotC subpopulation and equation (19) has empirical support from multiple studies, which are discussed on lines 422-444. Specifically, Mironov et al., 2008 showed that synaptically triggered Ca2+ transients in the distal dendrites of preBotC inspiratory neurons travel in a wave to the soma, where they activate TRPM4 currents (𝐼𝐶𝐴𝑁). This idea is further supported by Del Negro et al., 2011, which showed that dendritic Ca2+ transients precede inspiratory bursts, and Phillips et al., 2018, which showed that Cd2+ sensitive voltage-gated Ca2+ channels are primarily located in distal dendritic compartments. Taken together, these studies suggest that excitatory synaptic inputs to distal dendrites of preBotC inspiratory neurons trigger postsynaptic Ca2+ transients. In the model, we capture the synaptically triggered postsynaptic Ca2+ transient with equation (19), which specifies that a percentage (PSynCa) of the total postsynaptic current (ISyn) is carried by Ca2+ ions.

  3. eLife

    Evaluation Summary:

    This manuscript is of significant interest to readers in the field of neural control of breathing and for researches interested in the generation of biological rhythms in general. The study assembles a sophisticated computational modelling approach to test long-standing theories and emerging views in neural control of breathing and more specifically on biophysical mechanisms of burstlet generation in the respiratory network (the preBötzinger complex network). This work is an important contribution to a better understanding of the respiratory rhythm generation, will help validate (or not) running hypotheses and will guide future experiments.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 and Reviewer #3 agreed to share their names with the authors.)

  4. eLife

    Reviewer #1 (Public Review):

    Phillips and Rubin investigated the biophysical mechanisms that underly the generation of respiratory related burstlets and bursts in the rhythmogenic pre-Botzinger circuit. They show, suing a computational modelling approach, that synaptically mediated intracellular calcium concentrations and Ca2+ release and uptake via the endoplasmic reticulum are a unifying mechanism of the generation of the respiratory rhythm and its recruitment at the motoneuronal level. This explanatory model is able to unify contradictory experimental findings and is further evaluated by modelling the effect of opioids on the generation of burstlets and bursts in pre-Botzinger complex.

    The conclusions of this paper are mostly well supported by a sound computational modelling approach, however the current computational modelling data are largely based on experimental data of very few workgroups, while previous modelling approaches and experimental data that support anatomical network connectivity as a key feature for respiratory rhythm generation and transmission of burstlet/bursts to motorneuron pool were neglected.

    The current model is exclusively focused on biophysical properties and in particular calcium dynamics to generate a unifying computational model for respiratory rhythm generation. However, a previous model and experimental data suggest the emergence of rhythmogenic activity in pre-Botzinger complex may be largely determined by the local network connectivity as well as by the connectivity of the pre-Botzinger complex with the extended medullary and caudal pontine respiratory circuit. It would be interesting if this crucial component of a truly unifying model could be added or at least needs to be discussed appropriately.

  5. eLife

    Reviewer #2 (Public Review):

    Since its isolation in the transverse slice in 1991 (1), researchers have studied the preBotC with a focus on 2 related questions: how respiratory rhythm is generated, and how this rhythm is transformed into the pattern of inspiratory bursts, which are recorded at hypoglossal rootlets (XIIn), and in more intact preparations, from cervical ventral roots. The discovery of burstlets has provided support for the conjecture that the preBotC can be functionally parcellated into rhythm-generating and pattern-forming networks (2).

    Others have proposed that burstlets are rhythmogenic (2-4), via a stochastic percolation mechanism that synchronizes tonic spiking into burstlets, which in turn give rise to inspiratory drive. Phillips and Rubin challenge this account for its lack of detail and argue that it is weakly supported by features of burstlets that can be otherwise accounted for (burstlet onset and slope; percolation of activation following photostimulation of network subsets). Their points are well-taken, but two points need to be made. First, the percolation conjecture has gained traction because other more conventional mechanisms for rhythmogenesis have been shown not to apply, as nicely summarized in (4, 5). In particular, rhythmogenesis arising out of the activity of endogenous bursters (as is the case in their model) has failed to find empirical support: optical recordings of respiratory networks under conditions of synaptic blockade have not revealed appreciable numbers of endogenous bursters (this despite the fact that groups are recording from slices expressing genetically-encoded Ca2+ indicators in glutamatergic neurons in preBotC, using two-photon microscopy), older studies using conventional patch clamp methods identified negligible numbers of endogenous bursters (6, 7), and pharmacological disruption of specific endogenous bursting mechanisms has not silenced respiratory rhythm in the slice (8). Second, and more importantly, the processes that they model mediate the amplification of burstlets to bursts, and thus don't have obvious bearing on whether or not burstlets are generated by stochastic percolation or endogenous burster activity. As they state (lines 605-607) "the findings about burstlets and bursts presented in this work would have been obtained if the burstlet rhythm was imposed (Fig. 1) or if burstlets were generated by some other means". Thus a discussion of the inadequacies of current percolation models of respiratory rhythmogenesis seems to be irrelevant to their main points, and fails to acknowledge that other more plausible mechanisms have been set aside because they were undermined by experimental findings. They should either explain why a percolation-based mechanism for the emergence of burstlets is incompatible with their model for CIRC-mediated amplification of burstlets to network bursts, or they should remove these arguments, which distract from their main findings.

    It would be useful if the authors could elaborate on the applicability of their findings to less reduced preparations. The emergence of burstlets as well as the transition from predominantly burstlets to predominantly bursts is strongly dependent on network excitability, which is controlled by varying [K+]bath. Importantly, in their simulations burstlet activity falls silent at [K+]bath < 4 mM, and robust motor output only emerges for [K+]bath > 8 mM. While these modeling results jibe well with experimental results in the slice preparation, in more intact preparations, robust and stable respiratory rhythm is maintained at physiological levels ([K+]bath = 3 mM); in intact animals, 9 mM [K+]o is lethal. This raises the question of whether their model has explanatory power for respiratory rhythmogenesis in more intact preparations, or whether it is limited to describing fictive respiration in the slice.

    Another aspect to this problem is that the rhythmogenic mechanism they have incorporated in their model has strong dependence on [K+]bath; this straight-forwardly accounts for the transition from quiescence to burstlets, since within their models, endogenous bursters are quiescent at lower excitability levels. Of greater interest is the extent to which the [K+]bath dependence of the transition from burstlets to inspiratory bursting is again due to their choice of endogenous burster implementation. This is important, because it might enable CIRCA-mediated transitions from burstlets to network bursting that show less steep voltage dependence, and which are robust at more physiological [K+]o, thus enhancing the generalizability of their model. This was certainly a feature of an early study, in which I(CAN) was proposed as a mechanism for endogenous bursting, with weaker voltage dependence than the I(NaP)-based endogenous bursters implemented here (7).

    1. Smith JC, Ellenberger HH, Ballanyi K, Richter DW, Feldman JL. Pre-Botzinger complex: a brainstem region that may generate respiratory rhythm in mammals. Science. 1991;254(5032):726-9.
    2. Kam K, Worrell JW, Janczewski WA, Cui Y, Feldman JL. Distinct inspiratory rhythm and pattern generating mechanisms in the preBotzinger complex. J Neurosci. 2013;33(22):9235-45.
    3. Ashhad S, Feldman JL. Emergent Elements of Inspiratory Rhythmogenesis: Network Synchronization and Synchrony Propagation. Neuron. 2020;106(3):482-97 e4.
    4. Kallurkar PS, Grover C, Picardo MCD, Del Negro CA. Evaluating the Burstlet Theory of Inspiratory Rhythm and Pattern Generation. eNeuro. 2020;7(1).
    5. Del Negro CA, Funk GD, Feldman JL. Breathing matters. Nat Rev Neurosci. 2018;19(6):351-67.
    6. Del Negro CA, Koshiya N, Butera RJ, Jr., Smith JC. Persistent sodium current, membrane properties and bursting behavior of pre-botzinger complex inspiratory neurons in vitro. J Neurophysiol. 2002;88(5):2242-50.
    7. Thoby-Brisson M, Ramirez JM. Identification of two types of inspiratory pacemaker neurons in the isolated respiratory neural network of mice. J Neurophysiol. 2001;86(1):104-12.
    8. Del Negro CA, Morgado-Valle C, Feldman JL. Respiratory rhythm: an emergent network property? Neuron. 2002;34(5):821-30.
    9. Del Negro CA, Johnson SM, Butera RJ, Smith JC. Models of respiratory rhythm generation in the pre-Botzinger complex. III. Experimental tests of model predictions. J Neurophysiol. 2001;86(1):59-74.

  6. eLife

    Reviewer #3 (Public Review):

    A key contribution of this study includes a demonstration that two sets of neurons coupled via excitation can drive network activity similar to that observed in mXII nerve during breathing. In particular, the authors formulate a link between synaptic excitation and intracellular calcium induced Ca2+ release mechanisms via positive feedback from ICaN. This forms the basis for recruitment of non-rhythmogenic neurons by rhythm generating neurons. Such a formulation seems to help explain the burstlet theory and support a percolation theory of network bursts put forward in the field.

    The manuscript is well written. Figures and figure legends are clear, and justify the results stated. Methodology is well laid out; however, missed references in many places where it is not clear where the equations came from (e.g., equations 19 through 24 and elsewhere). The authors state that the model code "will" be available on ModelDB. This should instead be submitted with the manuscript for review. Later, the code must be made available on a GitHub repository for wide dissemination and future updates by others. ModelDB has models which can only be downloaded but not extended for wider use. Oftentimes this leads to lack of technical help for future users and limits model use and enhancement.

    The authors put forward a plausible mechanistic explanation for Ca2+ dependent recruitment of non-rhythmogenic neurons by linking synaptic excitation from the rhythm generating neurons to CICR in the former. Although attractive, there are numerous factors which control intracellular Ca2+ signaling and buffering. It would be important to clarify whether the assumption that dendritic depolarization due to synaptic inputs directly contributes to CICR as postulated in this model (the term PsynCa*Isyn in equation 19), has any (direct/indirect) empirical support either in preBotz neurons or elsewhere to ensure that this is not purely conjectural.

  7. Version published to 10.1101/2021.11.19.469304v1 on bioRxiv