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Connectome simulations identify a central pattern generator for fly walking

Connectome simulations identify a central pattern generator for fly walking Connectome simulations identify a central pattern generator for fly walking


  • Büschges, A. & Ache, J. M. Motor control on the move: from insights in insects to general mechanisms. Physiol. Rev. 105, 975–1031 https://doi.org/10.1152/physrev.00009.2024 (2025).

    Article 
    PubMed 

    Google Scholar
     

  • Gosgnach, S. The mammalian locomotor CPG: revealing the contents of the black box. J. Neurophysiol. 133, 472–478 https://doi.org/10.1152/jn.00238.2024 (2025).

    Article 
    PubMed 

    Google Scholar
     

  • Takemura, S.-Y. et al. A connectome of the male Drosophila ventral nerve cord. Preprint at https://doi.org/10.7554/eLife.97769.1 (2024).

  • Azevedo, A. et al. Connectomic reconstruction of a female Drosophila ventral nerve cord. Nature 631, 360–368 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bates, A. S. et al. Distributed control circuits across a brain-and-cord connectome. Nature 656, 957–970 (2026).

  • Berg, S. et al. Sexual dimorphism in the complete connectome of the Drosophila male central nervous system. Cell 189, 5504–5526.e15 (2025).

  • Sherrington, C. S. The Integrative Action of the Nervous System (Yale Univ. Press, 1920).

  • Brown, T. G. On the nature of the fundamental activity of the nervous centres; together with an analysis of the conditioning of rhythmic activity in progression, and a theory of the evolution of function in the nervous system. J. Physiol. 48, 18–46 (1914).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hughes, G. M. & Wiersma, C. A. G. The co-ordination of swimmeret movements in the crayfish, Procambarus Clarkii (Girard). J. Exp. Biol. 37, 657–670 https://doi.org/10.1242/jeb.37.4.657 (1960).

    Article 

    Google Scholar
     

  • Wilson, D. M. The central nervous control of flight in a locust. J. Exp. Biol. 38, 471–490 https://doi.org/10.1242/jeb.38.2.471 (1961).

    Article 

    Google Scholar
     

  • Daur, N., Nadim, F. & Bucher, D. The complexity of small circuits: the stomatogastric nervous system. Curr. Opin. Neurobiol. 41, 1–7 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Friesen, W. O. & Kristan, W. B. Leech locomotion: swimming, crawling, and decisions. Curr. Opin. Neurobiol. 17, 704–711 (2007).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Calabrese, R. L., Norris, B. J. & Wenning, A. The neural control of heartbeat in invertebrates. Curr. Opin. Neurobiol. 41, 68–77 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Del Negro, C. A., Funk, G. D. & Feldman, J. L. Breathing matters. Nat. Rev. Neurosci. 19, 351–367 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shepherd, G. M., Grillner, S. & Katz, P. S. in Handbook of Brain Microcircuits (eds Shepherd, G. M. & Grillner, S.) 561–568 https://doi.org/10.1093/med/9780190636111.003.0051 (Oxford Univ. Press, 2017).

  • Calabrese, R. L. & Marder, E. Degenerate neuronal and circuit mechanisms important for generating rhythmic motor patterns. Physiol. Rev. 105, 95–135 https://doi.org/10.1152/physrev.00003.2024 (2024).

  • Lindén, H., Petersen, P. C., Vestergaard, M. & Berg, R. W. Movement is governed by rotational neural dynamics in spinal motor networks. Nature 610, 526–531 (2022).

    Article 
    ADS 
    PubMed 

    Google Scholar
     

  • Strohmer, B., Najarro, E., Ausborn, J., Berg, R. W. & Tolu, S. Sparse firing in a hybrid central pattern generator for spinal motor circuits. Neur. Comput. 36, 759–780 https://doi.org/10.1162/neco_a_01660 (2024).

    Article 

    Google Scholar
     

  • Holmes, P., Full, R. J., Koditschek, D. & Guckenheimer, J. The dynamics of legged locomotion: models, analyses, and challenges. SIAM Rev. 48, 207–304 (2006).

    Article 
    ADS 
    MathSciNet 

    Google Scholar
     

  • Ijspeert, A. J. & Daley, M. A. Integration of feedforward and feedback control in the neuromechanics of vertebrate locomotion: a review of experimental, simulation and robotic studies. J. Exp. Biol. 226, jeb245784 https://doi.org/10.1242/jeb.245784 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Lesser, E. et al. Synaptic architecture of leg and wing premotor control networks in Drosophila. Nature 631, 369–377 (2024).

  • Stürner, T. et al. Comparative connectomics of Drosophila descending and ascending neurons. Nature 643, 158–172 (2025).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Marin, E. C. et al. Systematic annotation of a complete adult male Drosophila nerve cord connectome reveals principles of functional organisation. Preprint at https://doi.org/10.7554/eLife.97766.1 (2024).

  • Bargmann, C. I. & Marder, E. From the connectome to brain function. Nat. Methods 10, 483–490 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Scheffer, L. K. & Meinertzhagen, I. A. A connectome is not enough—what is still needed to understand the brain of Drosophila? J. Exp. Biol. 224, jeb242740 https://doi.org/10.1242/jeb.242740 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Shiu, P. K. et al. A Drosophila computational brain model reveals sensorimotor processing. Nature 634, 210–219 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kakaria, K. S. & de Bivort, B. L. Ring attractor dynamics emerge from a spiking model of the entire protocerebral bridge. Front. Behav. Neurosci. 11, 1662–5153 (2017).

  • Lappalainen, J. K. et al. Connectome-constrained networks predict neural activity across the fly visual system. Nature 634, 1132–1140 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cowley, B. R. et al. Mapping model units to visual neurons reveals population code for social behaviour. Nature 629, 1100–1108 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Duan, S., Dong, L. L. & Fiete, I. From synapses to dynamics: obtaining function from structure in a connectome constrained model of the head direction circuit. In Proc 39th Conference on Neural Information Processing Systems 128293–128320 (NeurIPS, 2026).

  • Büschges, A. Role of local nonspiking interneurons in the generation of rhythmic motor activity in the stick insect. J. Neurobiol. 27, 488–512 (1995).

    Article 
    PubMed 

    Google Scholar
     

  • Agrawal, S. et al. Central processing of leg proprioception in Drosophila. eLife 9, e60299, https://doi.org/10.7554/eLife.60299 (2020).

  • Azevedo, A. W. et al. A size principle for recruitment of Drosophila leg motor neurons. eLife 9, e56754 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Liu, T. X., Davoudian, P. A., Lizbinski, K. M. & Jeanne, J. M. Connectomic features underlying diverse synaptic connection strengths and subcellular computation. Curr. Biol. 32, 559–569 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Gouwens, N. W. & Wilson, R. I. Signal propagation in Drosophila central neurons. J. Neurosci. 29, 6239–6249 (2009).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Prinz, A. A., Billimoria, C. P. & Marder, E. Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons. J. Neurophysiol. 90, 3998–4015 (2003).

  • Sapkal, N. et al. Neural circuit mechanisms underlying context-specific halting in Drosophila. Nature 634, 191–200 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Scheffer, L. K. et al. A connectome and analysis of the adult Drosophila central brain. eLife 9, e57443 https://doi.org/10.7554/eLife.57443 (2020).

  • Buhmann, J. et al. Automatic detection of synaptic partners in a whole-brain Drosophila electron microscopy data set. Nat. Methods 18, 771–774 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pratt, B. G., Lee, S.-Y. J., Chou, G. M. & Tuthill, J. C. Miniature linear and split-belt treadmills reveal mechanisms of adaptive motor control in walking Drosophila. Curr. Biol. 34, 4368–4381 (2024).

  • Dallmann, C. J. et al. Selective presynaptic inhibition of leg proprioception in behaving Drosophila. Nature 647, 445–453 (2025).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Guo, L., Zhang, N. & Simpson, J. H. Descending neurons coordinate anterior grooming behavior in Drosophila. English. Curr. Biol. 32, 823–833 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Hampel, S., Franconville, R., Simpson, J. H. & Seeds, A. M. A neural command circuit for grooming movement control. eLife 4, e08758 https://doi.org/10.7554/eLife.08758 (2015).

  • Briggman, K. L. & Kristan, W. B. Multifunctional pattern-generating circuits. Ann. Rev. Neurosci. 31, 271–294 https://doi.org/10.1146/annurev.neuro.31.060407.125552 (2008).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Scheffer, L. K. Synapse detection efficiency in EM Drosophila connectomics. Preprint at bioRxiv https://doi.org/10.1101/2025.10.16.682869 (2025).

  • Sapkal, N. et al. Central versus peripheral neural control of a coordinated walking pattern in Drosophila. Preprint at bioRxiv https://doi.org/10.64898/2026.04.29.721658 (2026).

  • Lobato-Rios, V. et al. NeuroMechFly, a neuromechanical model of adult Drosophila melanogaster. Nat. Methods 19, 620–627 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Vaxenburg, R. et al. Whole-body physics simulation of fruit fly locomotion. Nature 643, 1312–1320 (2025).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Braun, J., Hurtak, F., Wang-Chen, S. & Ramdya, P. Descending networks transform command signals into population motor control. Nature 630, 686–694 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dallmann, C. J. et al. A dedicated brain circuit controls forward walking in Drosophila. Preprint at bioRxiv https://doi.org/10.64898/2026.01.04.697356 (2026).

  • Amari, S.-I. Mathematics of Neural Networks (Sangyo Tosho, 1978).

  • Onslow, A. C. E., Jones, M. W. & Bogacz, R. A canonical circuit for generating phase-amplitude coupling. PLoS ONE 9, e102591 (2014).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Alvarez, J. L., Morrison, K. & Curto, C. Attractor-Based Models for Sequences and Pattern Generation in Neural Circuits. Neur. Comput. 38, 257–291 (2026).

    Article 
    MathSciNet 

    Google Scholar
     

  • Wandler, F. D., Lemberger, B. K., McLean, D. L. & Murray, J. M. Coordinated spinal locomotor network dynamics emerge from cell-type-specific connectivity patterns. eLife 14, RP106658 (2025).

  • Dvornyk, V., Vinogradova, O. & Nevo, E. Origin and evolution of circadian clock genes in prokaryotes. Proc. Natl Acad. Sci. USA 100, 2495–2500 https://doi.org/10.1073/pnas.0130099100 (2003).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Elowitz, M. B. & Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature 403, 335–338 (2000).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Stricker, J. et al. A fast, robust and tunable synthetic gene oscillator. Nature 456, 516–519 (2008).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Marder, E., Kedia, S. & Morozova, E. O. New insights from small rhythmic circuits. Curr. Opin. Neurobiol. 76, 102610 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Aldarondo, D. et al. A virtual rodent predicts the structure of neural activity across behaviours. Nature 632, 594–602 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Brunton, B. W., Abe, E. T., Hu, L. J. & Tuthill, J. C. The digital sphinx: can a worm brain control a fly body? Preprint at https://doi.org/10.7554/eLife.111516.1 (2026).

  • Plaza, S. M. et al. neuPrint: An open access tool for EM connectomics. Front. Neuroinform. 16, 1662–5196 (2022).

  • Dorkenwald, S. et al. CAVE: Connectome Annotation Versioning Engine. Nat. Methods 22, 1112–1120 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dayan, P. & Abbott, L. F. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (MIT Press, 2005).

  • Schneider-Mizell, C. M. et al. Quantitative neuroanatomy for connectomics in Drosophila. eLife 5, e12059 https://doi.org/10.7554/eLife.12059 (2016).

  • Bradbury, J. et al. JAX: composable transformations of Python+NumPy programs v.0.3.13. GitHub http://github.com/jax-ml/jax (2018).

  • Kidger, P. On neural differential equations. Preprint at https://doi.org/10.48550/arXiv.2202.02435 (2022).

  • Zung, J. L. et al. An updated catalogue of split-GAL4 driver lines for descending neurons in Drosophila melanogaster. Preprint at https://doi.org/10.7554/eLife.107450.1 (2025).

  • Mathis, A. et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21, 1281–1289 https://doi.org/10.1038/s41593-018-0209-y (2018).

  • Karashchuk, L. et al. Anipose: a toolkit for robust markerless 3D pose estimation. Cell Rep. 36, 109730 https://doi.org/10.1016/j.celrep.2021.109730 (2021).

  • Moore, R. J. et al. FicTrac: A visual method for tracking spherical motion and generating fictive animal paths. J. Neurosci. Methods 225, 106–119 https://doi.org/10.1016/j.jneumeth.2014.01.010 (2014).

  • Pratt, B. G. et al. Proprioceptive limit detectors contribute to sensorimotor control of the Drosophila leg. Nat. Commun. 17, 2664 (2026).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     



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