---
title: "Part 4: References 351–end"
document_id: sobe-2025-main-ai
document_type: report_section
parent_document: ../report.md
section_id: main-section-17-part-4-references-351-end
section_order: 17
section_count: 17
language: en
license: CC-BY-4.0
---

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# Part 4: References 351–end

> Selective-retrieval section 17 of 17 from the [complete AI-readable report](../report.md). Content is copied without editorial summarization; relative asset, data, and code paths are rebased for this directory.

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- **Section ID:** `main-section-17-part-4-references-351-end`
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351. Rae, J. W., Borgeaud, S., Cai, T., Millican, K., Hoffmann, J., Song, F., Aslanides, J., Henderson, S., Ring, R., Young, S., Rutherford, E., Hennigan, T., Menick, J., Cassirer, A., Powell, R., Driessche, G. v. d., Hendricks, L. A., Rauh, M., Huang, P., . . . Irving, G. (2021). Scaling language models: Methods, analysis & insights from training gopher. arXiv. <https://arxiv.org/abs/2112.11446v2>
352. Raichle, M. E., & Gusnard, D. A. (2002). Appraising the brain’s energy budget. Proceedings of the National Academy of Sciences, 99(16), 10237–10239. <https://doi.org/10.1073/pnas.172399499>
353. Rama, S., Zbili, M., & Debanne, D. (2018). Signal propagation along the axon. Current Opinion in Neurobiology, 51, 37–44. <https://doi.org/10.1016/j.conb.2018.02.017>
354. Randi, F., Sharma, A. K., Dvali, S., & Leifer, A. M. (2023). Neural signal propagation atlas of caenorhabditis elegans. Nature, 623(7986), 406–414. <https://doi.org/10.1038/s41586-023-06683-4>
355. Reimann, M. W., King, J. G., Muller, E. B., Ramaswamy, S., & Markram, H. (2015). An algorithm to predict the connectome of neural microcircuits. Frontiers in Computational Neuroscience, 9. <https://doi.org/10.3389/fncom.2015.00120>
356. Reinhold, K., Lien, A. D., & Scanziani, M. (2015). Distinct recurrent versus afferent dynamics in cortical visual processing. Nature Neuroscience, 18(12), 1789–1797. <https://doi.org/10.1038/nn.4153>
357. Reva, M., Rössert, C., Arnaudon, A., Damart, T., Mandge, D., Tuncel, A., Ramaswamy, S., Markram, H., & Van Geit, W. (2023). A universal workflow for creation, validation, and generalization of detailed neuronal models. Patterns, 4(11), 100855. <https://doi.org/10.1016/j.patter.2023.100855>
358. Richardson, D. (2024). The ‘black’ world of US spy satellites. euro-sd.com. <https://euro-sd.com/2024/07/articles/39425/the-black-world-of-us-spy-satellites/>
359. Riedesel, C., Müller, I., Kaufmann, N., Adolf, A., Kämmer, N., Fritz, H., & Zeidler, D. (2019). Extending multibeam SEM technology to 331 beams. Proceedings of the 63rd Electron, Ion, and Photon Beam Technology and Nanofabrication Conference (EIPBN 2019). <https://eipbn.org/abstracts/2019/papers/10B-6.pdf>
360. Ripoll-Sánchez, L., Watteyne, J., Sun, H., Fernandez, R., Taylor, S. R., Weinreb, A., Bentley, B. L., Hammarlund, M., Miller, D. M., Hobert, O., Beets, I., Vértes, P. E., & Schafer, W. R. (2023). The neuropeptidergic connectome of c. Elegans. Neuron, 111(22), 3570–3589.e5. <https://doi.org/10.1016/j.neuron.2023.09.043>
361. Roberts, A. C., Bill, B. R., & Glanzman, D. L. (2013). Learning and memory in zebrafish larvae. Frontiers in Neural Circuits, 7. <https://doi.org/10.3389/fncir.2013.00126>
362. Rooke, R., Rasool, A., Schneider, J., & Levine, J. D. (2020). Drosophila melanogaster behaviour changes in different social environments based on group size and density. Communications Biology, 3(1), 304. <https://doi.org/10.1038/s42003-020-1024-z>
363. Root, C. M., Denny, C. A., Hen, R., & Axel, R. (2014). The participation of cortical amygdala in innate, odour-driven behaviour. Nature, 515(7526), 269–273. <https://doi.org/10.1038/nature13897>
364. Rosenblatt, F. (1957). The perceptron: A perceiving and recognizing automaton. Project PARA, Cornell Aeronautical Laboratory.
365. Rossi, L. F., Harris, K. D., & Carandini, M. (2020). Spatial connectivity matches direction selectivity in visual cortex. Nature, 588(7839), 648–652. <https://doi.org/10.1038/s41586-020-2894-4>
366. Rost, B. R., Wietek, J., Yizhar, O., & Schmitz, D. (2022). Optogenetics at the presynapse. Nature Neuroscience, 25(8), 984–998. <https://doi.org/10.1038/s41593-022-01113-6>
367. Roth, B. L. (2016). Dreadds for neuroscientists. Neuron, 89(4), 683–694. <https://doi.org/10.1016/j.neuron.2016.01.040>
368. Rothman, J. S. (2014). Modeling synapses. Encyclopedia of Computational Neuroscience (pp. 1–15). Springer, New York, NY. [https://doi.org/10.1007/978-1-4614-7320-6_240-1](https://doi.org/10.1007/978-1-4614-7320-6%5C_240-1)
369. Roux, B. (2002). Theoretical and computational models of ion channels. Current Opinion in Structural Biology, 12(2), 182–189. <https://doi.org/10.1016/S0959-440X(02)00307-X>
370. Rupprecht, P. T. R. (2021). Large-scale calcium imaging & noise levels. gcamp6f.com. <https://gcamp6f.com/2021/10/04/large-scale-calcium-imaging-noise-levels/>
371. Rübel, O., Tritt, A., Ly, R., Dichter, B. K., Ghosh, S., Niu, L., Baker, P., Soltesz, I., Ng, L., Svoboda, K., Frank, L., & Bouchard, K. E. (2022). The neurodata without borders ecosystem for neurophysiological data science. eLife, 11. <https://doi.org/10.7554/eLife.78362>
372. Saint-Amant, L., & Drapeau, P. (2000). Motoneuron activity patterns related to the earliest behavior of the zebrafish embryo. Journal of Neuroscience, 20(11), 3964–3972. <https://doi.org/10.1523/JNEUROSCI.20-11-03964.2000>
373. Sandberg, A., & Bostrom, N. (2008). Whole brain emulation: A roadmap. Future of Humanity Institute, University of Oxford. <https://ora.ox.ac.uk/objects/uuid:a6880196-34c7-47a0-80f1-74d32ab98788>
374. Sandberg, A. (2012). How many persons can there be: Brain reconstruction and big numbers. aleph.se. <https://www.aleph.se/andart/archives/2012/04/how_many_persons_can_there_be_brain_reconstruction_and_big_numbers.html>
375. Sapkal, N., Mancini, N., Kumar, D. S., Spiller, N., Murakami, K., Vitelli, G., Bargeron, B., Maier, K., Eichler, K., Jefferis, G. S. X. E., Shiu, P. K., Sterne, G. R., & Bidaye, S. S. (2024). Neural circuit mechanisms underlying context-specific halting in drosophila. Nature, 634(8032), 191–200. <https://doi.org/10.1038/s41586-024-07854-7>
376. Sarma, G. P., Lee, C. W., Portegys, T., Ghayoomie, V., Jacobs, T., Alicea, B., Cantarelli, M., Currie, M., Gerkin, R. C., Gingell, S., Gleeson, P., Gordon, R., Hasani, R. M., Idili, G., Khayrulin, S., Lung, D., Palyanov, A., Watts, M., & Larson, S. D. (2018). Openworm: Overview and recent advances in integrative biological simulation of caenorhabditis elegans. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 373(1758), 20170382. <https://doi.org/10.1098/rstb.2017.0382>
377. Sato, T. (2004). The earth simulator: Roles and impacts. Nuclear Physics B - Proceedings Supplements, 129-130, 102–108. <https://doi.org/10.1016/S0920-5632(03)02511-8>
378. Scarapicchia, V., Brown, C., Mayo, C., & Gawryluk, J. R. (2017). Functional magnetic resonance imaging and functional near-infrared spectroscopy: Insights from combined recording studies. Frontiers in Human Neuroscience, 11, 419. <https://doi.org/10.3389/fnhum.2017.00419>
379. Scatliffe, N., Casavant, S., Vittner, D., & Cong, X. (2019). Oxytocin and early parent-infant interactions: A systematic review. International Journal of Nursing Sciences, 6(4), 445–453. <https://doi.org/10.1016/j.ijnss.2019.09.009>
380. Schaffer, E. S., Mishra, N., Whiteway, M. R., Li, W., Vancura, M. B., Freedman, J., Patel, K. B., Voleti, V., Paninski, L., Hillman, E. M. C., Abbott, L. F., & Axel, R. (2023). The spatial and temporal structure of neural activity across the fly brain. Nature Communications, 14(1), 5572. <https://doi.org/10.1038/s41467-023-41261-2>
381. Scheffer, L. K., Xu, C. S., Januszewski, M., Lu, Z., Takemura, S., Hayworth, K. J., Huang, G. B., Shinomiya, K., Maitlin-Shepard, J., Berg, S., Clements, J., Hubbard, P. M., Katz, W. T., Umayam, L., Zhao, T., Ackerman, D., Blakely, T., Bogovic, J., Dolafi, T., . . . Plaza, S. M. (2020). A connectome and analysis of the adult drosophila central brain. eLife, 9. <https://doi.org/10.7554/eLife.57443>
382. Scheffer, L. K., & Meinertzhagen, I. A. (2021). A connectome is not enough –what is still needed to understand the brain of drosophila ?. Journal of Experimental Biology, 224(21). <https://doi.org/10.1242/jeb.242740>
383. Schlegel, P., Yin, Y., Bates, A. S., Dorkenwald, S., Eichler, K., Brooks, P., Han, D. S., Gkantia, M., dos Santos, M., Munnelly, E. J., Badalamente, G., Serratosa Capdevila, L., Sane, V. A., Fragniere, A. M. C., Kiassat, L., Pleijzier, M. W., Stürner, T., Tamimi, I. F. M., Dunne, C. R., . . . Jefferis, G. S. X. E. (2024). Whole-brain annotation and multi-connectome cell typing of drosophila. Nature, 634(8032), 139–152. <https://doi.org/10.1038/s41586-024-07686-5>
384. Schnell, B., Ros, I. G., & Dickinson, M. H. (2017). A descending neuron correlated with the rapid steering maneuvers of flying drosophila. Current Biology, 27(8), 1200–1205. <https://doi.org/10.1016/j.cub.2017.03.004>
385. Schoenholz, S. S., & Cubuk, E. D. (2019). Jax, m.d.: A framework for differentiable physics. arXiv. <https://arxiv.org/abs/1912.04232v2>
386. Schoonheim, P. J., Arrenberg, A. B., Bene, F. D., & Baier, H. (2010). Optogenetic localization and genetic perturbation of saccade-generating neurons in zebrafish. Journal of Neuroscience, 30(20), 7111–7120. <https://doi.org/10.1523/JNEUROSCI.5193-09.2010>
387. Schretter, C. E., Aso, Y., Robie, A. A., Dreher, M., Dolan, M., Chen, N., Ito, M., Yang, T., Parekh, R., Branson, K. M., & Rubin, G. M. (2020). Cell types and neuronal circuitry underlying female aggression in drosophila. eLife, 9. <https://doi.org/10.7554/eLife.58942>
388. Schrimpf, M., Kubilius, J., Lee, M. J., Murty, N. A. R., Ajemian, R., & DiCarlo, J. J. (2020). Integrative benchmarking to advance neurally mechanistic models of human intelligence. Neuron, 108(3), 413–423. <https://doi.org/10.1016/j.neuron.2020.07.040>
389. Schröder, S., Steinmetz, N. A., Krumin, M., Pachitariu, M., Rizzi, M., Lagnado, L., Harris, K. D., & Carandini, M. (2020). Arousal modulates retinal output. Neuron, 107(3), 487–495.e9. <https://doi.org/10.1016/j.neuron.2020.04.026>
390. Seeholzer, L. F., Seppo, M., Stern, D. L., & Ruta, V. (2018). Evolution of a central neural circuit underlies drosophila mate preferences. Nature, 559(7715), 564–569. <https://doi.org/10.1038/s41586-018-0322-9>
391. Seelig, J. D., Chiappe, M. E., Lott, G. K., Reiser, M. B., & Jayaraman, V. (2011). Calcium imaging in drosophila during walking and flight behavior. Biophysical Journal, 100(3), 97a. <https://doi.org/10.1016/j.bpj.2010.12.735>
392. Seki, F., Hikishima, K., Komaki, Y., Hata, J., Uematsu, A., Okahara, N., Yamamoto, M., Shinohara, H., Sasaki, E., & Okano, H. (2017). Developmental trajectories of macroanatomical structures in common marmoset brain. Neuroscience, 364, 143–156. <https://doi.org/10.1016/j.neuroscience.2017.09.021>
393. Seo, D., Neely, R. M., Shen, K., Singhal, U., Alon, E., Rabaey, J. M., Carmena, J. M., & Maharbiz, M. M. (2016). Wireless recording in the peripheral nervous system with ultrasonic neural dust. Neuron, 91(3), 529–539. <https://doi.org/10.1016/j.neuron.2016.06.034>
394. Seo, D. (2018). Neural dust: Ultrasonic biological interface. UC Berkeley EECS. <https://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-146.html>
395. Serrano, A., Berthelet, J., Naik, S. H., & Merino, D. (2022). Mastering the use of cellular barcoding to explore cancer heterogeneity. Nature Reviews Cancer, 22(11), 609–624. <https://doi.org/10.1038/s41568-022-00500-2>
396. Seth, A., Hicks, J. L., Uchida, T. K., Habib, A., Dembia, C. L., Dunne, J. J., Ong, C. F., DeMers, M. S., Rajagopal, A., Millard, M., Hamner, S. R., Arnold, E. M., Yong, J. R., Lakshmikanth, S. K., Sherman, M. A., Ku, J. P., & Delp, S. L. (2018). Opensim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement. PLOS Computational Biology, 14(7), e1006223. <https://doi.org/10.1371/journal.pcbi.1006223>
397. Shadmehr, R., & Mussa-Ivaldi, F. A. (1994). Adaptive representation of dynamics during learning of a motor task. Journal of Neuroscience, 14(5), 3208–3224. <https://doi.org/10.1523/JNEUROSCI.14-05-03208.1994>
398. Shapiro, M. G., Goodwill, P. W., Neogy, A., Yin, M., Foster, F. S., Schaffer, D. V., & Conolly, S. M. (2014). Biogenic gas nanostructures as ultrasonic molecular reporters. Nature Nanotechnology, 9(4), 311–316. <https://doi.org/10.1038/nnano.2014.32>
399. Shapson-Coe, A., Januszewski, M., Berger, D. R., Pope, A., Wu, Y., Blakely, T., Schalek, R. L., Li, P. H., Wang, S., Maitin-Shepard, J., Karlupia, N., Dorkenwald, S., Sjostedt, E., Leavitt, L., Lee, D., Troidl, J., Collman, F., Bailey, L., Fitzmaurice, A., . . . Lichtman, J. W. (2024). A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution. Science, 384(6696), eadk4858. <https://doi.org/10.1126/science.adk4858>
400. Shaw, D. E., Adams, P. J., Azaria, A., Bank, J. A., Batson, B., Bell, A., Bergdorf, M., Bhatt, J., Butts, J. A., Correia, T., Dirks, R. M., Dror, R. O., Eastwood, M. P., Edwards, B., Even, A., Feldmann, P., Fenn, M., Fenton, C. H., Forte, A., . . . Yuh, K. A. (2021). Anton 3: Twenty microseconds of molecular dynamics simulation before lunch. Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 1–11). Association for Computing Machinery. <https://doi.org/10.1145/3458817.3487397>
401. Shen, F. Y., Harrington, M. M., Walker, L. A., Cheng, H. P. J., Boyden, E. S., & Cai, D. (2020). Light microscopy based approach for mapping connectivity with molecular specificity. Nature Communications, 11(1), 4632. <https://doi.org/10.1038/s41467-020-18422-8>
402. Sheng, M., & Kim, E. (2011). The postsynaptic organization of synapses. Cold Spring Harbor Perspectives in Biology, 3(12), a005678. <https://doi.org/10.1101/cshperspect.a005678>
403. Sherman, D., Worrell, J. W., Cui, Y., & Feldman, J. L. (2015). Optogenetic perturbation of prebötzinger complex inhibitory neurons modulates respiratory pattern. Nature Neuroscience, 18(3), 408–414. <https://doi.org/10.1038/nn.3938>
404. Shin, T. W., Wang, H., Zhang, C., An, B., Lu, Y., Zhang, E., Lu, X., Karagiannis, E. D., Kang, J. S., Emenari, A., Symvoulidis, P., Asano, S., Lin, L., Costa, E. K., Marblestone, A. H., Kasthuri, N., Tsai, L., & Boyden, E. S. (2025). Dense, continuous membrane labeling and expansion microscopy visualization of ultrastructure in tissues. Nature Communications, 16(1), 1579. <https://doi.org/10.1038/s41467-025-56641-z>
405. Shiu, P. K., Sterne, G. R., Spiller, N., Franconville, R., Sandoval, A., Zhou, J., Simha, N., Kang, C. H., Yu, S., Kim, J. S., Dorkenwald, S., Matsliah, A., Schlegel, P., Yu, S., McKellar, C. E., Sterling, A., Costa, M., Eichler, K., Bates, A. S., . . . Scott, K. (2024). A drosophila computational brain model reveals sensorimotor processing. Nature, 634(8032), 210–219. <https://doi.org/10.1038/s41586-024-07763-9>
406. Shouval, H. Z., Castellani, G. C., Blais, B. S., Yeung, L. C., & Cooper, L. N. (2002). Converging evidence for a simplified biophysical model of synaptic plasticity. Biological Cybernetics, 87(5), 383–391. <https://doi.org/10.1007/s00422-002-0362-x>
407. Siegle, J. H., Jia, X., Durand, S., Gale, S., Bennett, C., Graddis, N., Heller, G., Ramirez, T. K., Choi, H., Luviano, J. A., Groblewski, P. A., Ahmed, R., Arkhipov, A., Bernard, A., Billeh, Y. N., Brown, D., Buice, M. A., Cain, N., Caldejon, S., . . . Koch, C. (2021). Survey of spiking in the mouse visual system reveals functional hierarchy. Nature, 592(7852), 86–92. <https://doi.org/10.1038/s41586-020-03171-x>
408. Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., & Hassabis, D. (2016). Mastering the game of go with deep neural networks and tree search. Nature, 529(7587), 484–489. <https://doi.org/10.1038/nature16961>
409. Simeon, Q., Venâncio, L., Skuhersky, M. A., Nayebi, A., Boyden, E. S., & Yang, G. R. (2024). Scaling properties for artificial neural network models of a small nervous system. SoutheastCon 2024 (pp. 516–524). <https://doi.org/10.1109/SoutheastCon52093.2024.10500049>
410. Skocik, M. J., & Long, L. N. (2014). On the capabilities and computational costs of neuron models. IEEE Transactions on Neural Networks and Learning Systems, 25(8), 1474–1483. <https://doi.org/10.1109/TNNLS.2013.2294016>
411. Skuhersky, M., Wu, T., Yemini, E., Nejatbakhsh, A., Boyden, E., & Tegmark, M. (2022). Toward a more accurate 3D atlas of c. Elegans neurons. BMC Bioinformatics, 23(1), 195. <https://doi.org/10.1186/s12859-022-04738-3>
412. Slyusarev, G. S., Skalon, E. K., & Starunov, V. V. (2024). Evolution of orthonectida body plan. Evolution & Development, 26(4), e12462. <https://doi.org/10.1111/ede.12462>
413. Snapp, E. L. (2009). Fluorescent proteins: A cell biologist’s user guide. Trends in Cell Biology, 19(11), 649–655. <https://doi.org/10.1016/j.tcb.2009.08.002>
414. Sponheim, C., Papadourakis, V., Collinger, J. L., Downey, J., Weiss, J., Pentousi, L., Elliott, K., & Hatsopoulos, N. G. (2021). Longevity and reliability of chronic unit recordings using the utah, intracortical multi-electrode arrays. Journal of Neural Engineering, 18(6), 066044. <https://doi.org/10.1088/1741-2552/ac3eaf>
415. Sprague, D. Y., Rusch, K., Dunn, R. L., Borchardt, J. M., Ban, S., Bubnis, G., Chiu, G. C., Wen, C., Suzuki, R., Chaudhary, S., Lee, H. J., Yu, Z., Dichter, B., Ly, R., Onami, S., Lu, H., Kimura, K. D., Yemini, E., & Kato, S. (2024). Unifying community-wide whole-brain imaging datasets enables robust automated neuron identification and reveals determinants of neuron positioning in c. Elegans. bioRxiv. <https://doi.org/10.1101/2024.04.28.591397>
416. Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., Kluska, A., Lewkowycz, A., Agarwal, A., Power, A., Ray, A., Warstadt, A., Kocurek, A. W., Safaya, A., Tazarv, A., . . . Wu, Z. (2022). Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv. <https://arxiv.org/abs/2206.04615v3>
417. Stagkourakis, S., Smiley, K. O., Williams, P., Kakadellis, S., Ziegler, K., Bakker, J., Brown, R. S. E., Harkany, T., Grattan, D. R., & Broberger, C. (2020). A neuro-hormonal circuit for paternal behavior controlled by a hypothalamic network oscillation. Cell, 182(4), 960–975.e15. <https://doi.org/10.1016/j.cell.2020.07.007>
418. Stampfl, A. P. J., Liu, Z., Hu, J., Sawada, K., Takano, H., Kohmura, Y., Ishikawa, T., Lim, J., Je, J., Low, C., Teo, A., Tok, E. S., Tan, T. W., Ban, K., Libedinsky, C., Tan, F. C. K., Chen, K., Yang, A., Chuang, C., . . . Margaritondo, G. (2023). Synapse: An international roadmap to large brain imaging. Physics Reports, 999, 1–60. <https://doi.org/10.1016/j.physrep.2022.11.003>
419. Stampfl, A. P. J., Shen, Z., Poo, M., Sun, Y., Yuan, Q., Zhang, K., Tai, R., Tian, X., Kohmura, Y., Ishikawa, T., Lim, J., Je, J., Low, C., Hwu, Y., Rojviriya, C., Rugmai, S., Rujirawat, S., & Margaritondo, G. (2024). Synapse: Brain mapping takes an international dimension. Synchrotron Radiation News, 37(5), 40–49. <https://doi.org/10.1080/08940886.2024.2414729>
420. Steen, R. G., Hamer, R. M., & Lieberman, J. A. (2007). Measuring brain volume by MR imaging: Impact of measurement precision and natural variation on sample size requirements. American Journal of Neuroradiology, 28(6), 1119–1125. <https://doi.org/10.3174/ajnr.A0537>
421. Steib, E., Vagena-Pantoula, C., & Vermot, J. (2023). Tissuexm protocol for ultrastructure expansion microscopy of zebrafish larvae and mouse embryos. STAR Protocols, 4(2), 102257. <https://doi.org/10.1016/j.xpro.2023.102257>
422. Sterling, P., & Laughlin, S. (2015). Principles of neural design. direct.mit.edu. <https://doi.org/10.7551/mitpress/9780262028707.001.0001>
423. Stevens, R., & Orr, G. (2020). Bioimaging capabilities to enable mapping of the neural connections in a complex brain. US Department of Energy (USDOE), Washington DC (United States). <https://doi.org/10.2172/1821173>
424. Stevenson, I. H., & Kording, K. P. (2011). How advances in neural recording affect data analysis. Nature Neuroscience, 14(2), 139–142. <https://doi.org/10.1038/nn.2731>
425. Stiefel, K. M., & Brooks, D. S. (2019). Why is there no successful whole brain simulation (yet)?. Biological Theory, 14(2), 122–130. <https://doi.org/10.1007/s13752-019-00319-5>
426. Stimberg, M., Brette, R., & Goodman, D. F. (2019). Brian 2, an intuitive and efficient neural simulator. eLife, 8, e47314. <https://doi.org/10.7554/eLife.47314>
427. Stringer, C., & Pachitariu, M. (2024). Analysis methods for large-scale neuronal recordings. Science, 386(6722), eadp7429. <https://doi.org/10.1126/science.adp7429>
428. Strotton, M., Hosogane, T., di Michiel, M., Moch, H., Varga, Z., & Bodenmiller, B. (2023). Multielement z-tag imaging by x-ray fluorescence microscopy for next-generation multiplex imaging. Nature Methods, 20(9), 1310–1322. <https://doi.org/10.1038/s41592-023-01977-x>
429. Sun, F., Zeng, J., Jing, M., Zhou, J., Feng, J., Owen, S. F., Luo, Y., Li, F., Wang, H., Yamaguchi, T., Yong, Z., Gao, Y., Peng, W., Wang, L., Zhang, S., Du, J., Lin, D., Xu, M., Kreitzer, A. C., . . . Li, Y. (2018). A genetically encoded fluorescent sensor enables rapid and specific detection of dopamine in flies, fish, and mice. Cell, 174(2), 481–496.e19. <https://doi.org/10.1016/j.cell.2018.06.042>
430. Sun, F., Zhou, J., Dai, B., Qian, T., Zeng, J., Li, X., Zhuo, Y., Zhang, Y., Wang, Y., Qian, C., Tan, K., Feng, J., Dong, H., Lin, D., Cui, G., & Li, Y. (2020). Next-generation GRAB sensors for monitoring dopaminergic activity in vivo. Nature Methods, 17(11), 1156–1166. <https://doi.org/10.1038/s41592-020-00981-9>
431. Suzuki, M., Goto, T., Tsuji, T., & Ohtake, H. (2005). A dynamic body model of the nematode c. Elegans with neural oscillators. Journal of Robotics and Mechatronics, 17(3), 318–326. <https://doi.org/10.20965/jrm.2005.p0318>
432. Svara, F., Förster, D., Kubo, F., Januszewski, M., dal Maschio, M., Schubert, P. J., Kornfeld, J., Wanner, A. A., Laurell, E., Denk, W., & Baier, H. (2022). Automated synapse-level reconstruction of neural circuits in the larval zebrafish brain. Nature Methods, 19(11), 1357–1366. <https://doi.org/10.1038/s41592-022-01621-0>
433. Svara, F. N., Kornfeld, J., Denk, W., & Bollmann, J. H. (2018). Volume EM reconstruction of spinal cord reveals wiring specificity in speed-related motor circuits. Cell Reports, 23(10), 2942–2954. <https://doi.org/10.1016/j.celrep.2018.05.023>
434. Symvoulidis, P., Lauri, A., Stefanoiu, A., Cappetta, M., Schneider, S., Jia, H., Stelzl, A., Koch, M., Perez, C. C., Myklatun, A., Renninger, S., Chmyrov, A., Lasser, T., Wurst, W., Ntziachristos, V., & Westmeyer, G. G. (2017). Neubtracker—imaging neurobehavioral dynamics in freely behaving fish. Nature Methods, 14(11), 1079–1082. <https://doi.org/10.1038/nmeth.4459>
435. Szigeti, B., Gleeson, P., Vella, M., Khayrulin, S., Palyanov, A., Hokanson, J., Currie, M., Cantarelli, M., Idili, G., & Larson, S. (2014). Openworm: An open-science approach to modeling caenorhabditis elegans. Frontiers in Computational Neuroscience, 8. <https://doi.org/10.3389/fncom.2014.00137>
436. Takemura, S., Hayworth, K. J., Huang, G. B., Januszewski, M., Lu, Z., Marin, E. C., Preibisch, S., Xu, C. S., Bogovic, J., Champion, A. S., Cheong, H. S., Costa, M., Eichler, K., Katz, W., Knecht, C., Li, F., Morris, B. J., Ordish, C., Rivlin, P. K., . . . Berg, S. (2024). A connectome of the male drosophila ventral nerve cord. elifesciences.org. <https://doi.org/10.7554/eLife.97769.1>
437. Tang, J., LeBel, A., Jain, S., & Huth, A. G. (2023). Semantic reconstruction of continuous language from non-invasive brain recordings. Nature Neuroscience, 26(5), 858–866. <https://doi.org/10.1038/s41593-023-01304-9>
438. Tang, Y., Nyengaard, J. R., De Groot, D. M., & Gundersen, H. J. G. (2001). Total regional and global number of synapses in the human brain neocortex. Synapse, 41(3), 258–273. <https://doi.org/10.1002/syn.1083>
439. Tavakoli, M. R., Lyudchik, J., Januszewski, M., Vistunou, V., Agudelo Dueñas, N., Vorlaufer, J., Sommer, C., Kreuzinger, C., Oliveira, B., Cenameri, A., Novarino, G., Jain, V., & Danzl, J. G. (2025). Light-microscopy-based connectomic reconstruction of mammalian brain tissue. Nature, 642(8067), 398–410. <https://doi.org/10.1038/s41586-025-08985-1>
440. Taylor, P. (2025). Total installed base of data storage capacity in the global datasphere 2020–2025. statista.com. <https://www.statista.com/statistics/1185900/worldwide-datasphere-storage-capacity-installed-base/>
441. Taylor, S. R., Santpere, G., Weinreb, A., Barrett, A., Reilly, M. B., Xu, C., Varol, E., Oikonomou, P., Glenwinkel, L., McWhirter, R., Poff, A., Basavaraju, M., Rafi, I., Yemini, E., Cook, S. J., Abrams, A., Vidal, B., Cros, C., Tavazoie, S., . . . Miller, D. M. (2021). Molecular topography of an entire nervous system. Cell, 184(16), 4329–4347.e23. <https://doi.org/10.1016/j.cell.2021.06.023>
442. Tedersoo, L., Küngas, R., Oras, E., Köster, K., Eenmaa, H., Leijen, Ä., Pedaste, M., Raju, M., Astapova, A., Lukner, H., Kogermann, K., & Sepp, T. (2021). Data sharing practices and data availability upon request differ across scientific disciplines. Scientific Data, 8(1), 192. <https://doi.org/10.1038/s41597-021-00981-0>
443. Tian, X., Lin, T., Lin, P., Tsai, M., Chen, H., Chen, W., Lee, C., Tu, C., Hsu, J., Hsieh, T., Tung, Y., Wang, C., Lin, S., Chu, L., Tseng, F., Hsueh, Y., Lee, C., Chen, P., & Chen, B. (2024). Rapid lightsheet fluorescence imaging of whole drosophila brains at nanoscale resolution by potassium acrylate-based expansion microscopy. Nature Communications, 15(1), 10911. <https://doi.org/10.1038/s41467-024-55305-8>
444. Todorov, E., Erez, T., & Tassa, Y. (2012). Mujoco: A physics engine for model-based control. 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 5026–5033). <https://doi.org/10.1109/IROS.2012.6386109>
445. Tokunaga, T., Sato, N., Arai, M., Nakamura, T., & Ishihara, T. (2024). Mechanism of sensory perception unveiled by simultaneous measurement of membrane voltage and intracellular calcium. Communications Biology, 7(1), 1150. <https://doi.org/10.1038/s42003-024-06778-2>
446. Torigoe, M., Islam, T., Kakinuma, H., Fung, C. C. A., Isomura, T., Shimazaki, H., Aoki, T., Fukai, T., & Okamoto, H. (2021). Zebrafish capable of generating future state prediction error show improved active avoidance behavior in virtual reality. Nature Communications, 12(1), 5712. <https://doi.org/10.1038/s41467-021-26010-7>
447. Trinh, T. H., Wu, Y., Le, Q. V., He, H., & Luong, T. (2024). Solving olympiad geometry without human demonstrations. Nature, 625(7995), 476–482. <https://doi.org/10.1038/s41586-023-06747-5>
448. Trivedi, C. A., & Bollmann, J. H. (2013). Visually driven chaining of elementary swim patterns into a goal-directed motor sequence: A virtual reality study of zebrafish prey capture. Frontiers in Neural Circuits, 7. <https://doi.org/10.3389/fncir.2013.00086>
449. Turishcheva, P., Fahey, P. G., Hansel, L., Froebe, R., Ponder, K., Vystrčilová, M., Willeke, K. F., Bashiri, M., Wang, E., Ding, Z., Tolias, A. S., Sinz, F. H., & Ecker, A. S. (2023). The dynamic sensorium competition for predicting large-scale mouse visual cortex activity from videos. arXiv. <https://arxiv.org/abs/2305.19654v2>
450. Turrini, L., Ricci, P., Sorelli, M., de Vito, G., Marchetti, M., Vanzi, F., & Pavone, F. S. (2024). Two-photon all-optical neurophysiology for the dissection of larval zebrafish brain functional and effective connectivity. Communications Biology, 7(1), 1261. <https://doi.org/10.1038/s42003-024-06731-3>
451. Tuthill, J. C. (2009). Lessons from a compartmental model of a drosophila neuron. Journal of Neuroscience, 29(39), 12033–12034. <https://doi.org/10.1523/JNEUROSCI.3348-09.2009>
452. Tuthill, J. C., & Wilson, R. I. (2016). Parallel transformation of tactile signals in central circuits of drosophila. Cell, 164(5), 1046–1059. <https://doi.org/10.1016/j.cell.2016.01.014>
453. Umezaki, Y., Hidalgo, S., Nguyen, E., Nguyen, T., Suh, J., Uchino, S. S., Chiu, J. C., & Hamada, F. N. (2024). Taste triggers a homeostatic temperature control in drosophila. elifesciences.org. <https://doi.org/10.7554/eLife.94703.1>
454. Urai, A. E., Doiron, B., Leifer, A. M., & Churchland, A. K. (2022). Large-scale neural recordings call for new insights to link brain and behavior. Nature Neuroscience, 25(1), 11–19. <https://doi.org/10.1038/s41593-021-00980-9>
455. Vahdat, A. (2024). Speed, scale and reliability: 25 years of google data-center networking evolution. Google Cloud Blog. <https://cloud.google.com/blog/products/networking/speed-scale-reliability-25-years-of-data-center-networking>
456. Valadez-Godínez, S., Sossa, H., & Santiago-Montero, R. (2020). On the accuracy and computational cost of spiking neuron implementation. Neural Networks, 122, 196–217. <https://doi.org/10.1016/j.neunet.2019.09.026>
457. Van Camp, K. A., Baggerman, G., Blust, R., & Husson, S. J. (2017). Peptidomics of the zebrafish danio rerio: In search for neuropeptides. Journal of Proteomics, 150, 290–296. <https://doi.org/10.1016/j.jprot.2016.09.015>
458. Van Geit, W., Gevaert, M., Chindemi, G., Rössert, C., Courcol, J., Muller, E. B., Schürmann, F., Segev, I., & Markram, H. (2016). Bluepyopt: Leveraging open source software and cloud infrastructure to optimise model parameters in neuroscience. Frontiers in Neuroinformatics, 10. <https://doi.org/10.3389/fninf.2016.00017>
459. Varshney, L. R., Chen, B. L., Paniagua, E., Hall, D. H., & Chklovskii, D. B. (2011). Structural properties of the caenorhabditis elegans neuronal network. PLOS Computational Biology, 7(2), e1001066. <https://doi.org/10.1371/journal.pcbi.1001066>
460. Vaxenburg, R., Siwanowicz, I., Merel, J., Robie, A. A., Morrow, C., Novati, G., Stefanidi, Z., Both, G., Card, G. M., Reiser, M. B., Botvinick, M. M., Branson, K. M., Tassa, Y., & Turaga, S. C. (2025). Whole-body physics simulation of fruit fly locomotion. Nature, 643(8074), 1312–1320. <https://doi.org/10.1038/s41586-025-09029-4>
461. Velicky, P., Miguel, E., Michalska, J. M., Lyudchik, J., Wei, D., Lin, Z., Watson, J. F., Troidl, J., Beyer, J., Ben-Simon, Y., Sommer, C., Jahr, W., Cenameri, A., Broichhagen, J., Grant, S. G. N., Jonas, P., Novarino, G., Pfister, H., Bickel, B., & Danzl, J. G. (2023). Dense 4D nanoscale reconstruction of living brain tissue. Nature Methods, 20(8), 1256–1265. <https://doi.org/10.1038/s41592-023-01936-6>
462. Vesuna, S., Kauvar, I. V., Richman, E., Gore, F., Oskotsky, T., Sava-Segal, C., Luo, L., Malenka, R. C., Henderson, J. M., Nuyujukian, P., Parvizi, J., & Deisseroth, K. (2020). Deep posteromedial cortical rhythm in dissociation. Nature, 586(7827), 87–94. <https://doi.org/10.1038/s41586-020-2731-9>
463. Vincent, Trevor J., Thiessen, J. D., Kurjewicz, L. M., Germscheid, S. L., Turner, A. J., Zhilkin, P., Alexander, M. E., & Martin, M. (2010). Longitudinal brain size measurements in app/ps1 transgenic mice. Magnetic Resonance Insights, 4, MRI.S5885. <https://doi.org/10.4137/MRI.S5885>
464. Vinograd, A., Nair, A., Kim, J. H., Linderman, S. W., & Anderson, D. J. (2024). Causal evidence of a line attractor encoding an affective state. Nature, 634(8035), 910–918. <https://doi.org/10.1038/s41586-024-07915-x>
465. Vishwanathan, A., Sood, A., Wu, J., Ramirez, A. D., Yang, R., Kemnitz, N., Ih, D., Turner, N., Lee, K., Tartavull, I., Silversmith, W. M., Jordan, C. S., David, C., Bland, D., Sterling, A., Seung, H. S., Goldman, M. S., & Aksay, E. R. F. (2024). Predicting modular functions and neural coding of behavior from a synaptic wiring diagram. Nature Neuroscience, 27(12), 2443–2454. <https://doi.org/10.1038/s41593-024-01784-3>
466. Vladimirov, N., Mu, Y., Kawashima, T., Bennett, D. V., Yang, C., Looger, L. L., Keller, P. J., Freeman, J., & Ahrens, M. B. (2014). Light-sheet functional imaging in fictively behaving zebrafish. Nature Methods, 11(9), 883–884. <https://doi.org/10.1038/nmeth.3040>
467. Vogelstein, J. T., Park, Y., Ohyama, T., Kerr, R. A., Truman, J. W., Priebe, C. E., & Zlatic, M. (2014). Discovery of brainwide neural-behavioral maps via multiscale unsupervised structure learning. Science, 344(6182), 386–392. <https://doi.org/10.1126/science.1250298>
468. Voleti, V., Patel, K. B., Li, W., Perez Campos, C., Bharadwaj, S., Yu, H., Ford, C., Casper, M. J., Yan, R. W., Liang, W., Wen, C., Kimura, K. D., Targoff, K. L., & Hillman, E. M. C. (2019). Real-time volumetric microscopy of in vivo dynamics and large-scale samples with SCAPE 2.0. Nature Methods, 16(10), 1054–1062. <https://doi.org/10.1038/s41592-019-0579-4>
469. Volkov, M., Machikhin, A., Bukova, V., Khokhlov, D., Burlakov, A., & Krylov, V. (2022). Optical transparency and label-free vessel imaging of zebrafish larvae in shortwave infrared range as a tool for prolonged studying of cardiovascular system development. Scientific Reports, 12(1), 20884. <https://doi.org/10.1038/s41598-022-25386-w>
470. Wagenmaker, A., Mi, L., Rozsa, M., Bull, M. S., Svoboda, K., Daie, K., Golub, M. D., & Jamieson, K. (2024). Active learning of neural population dynamics using two-photon holographic optogenetics. arXiv. <https://arxiv.org/abs/2412.02529v4>
471. Wan, J., Peng, W., Li, X., Qian, T., Song, K., Zeng, J., Deng, F., Hao, S., Feng, J., Zhang, P., Zhang, Y., Zou, J., Pan, S., Shin, M., Venton, B. J., Zhu, J. J., Jing, M., Xu, M., & Li, Y. (2021). A genetically encoded sensor for measuring serotonin dynamics. Nature Neuroscience, 24(5), 746–752. <https://doi.org/10.1038/s41593-021-00823-7>
472. Wang-Chen, S., Stimpfling, V. A., Lam, T. K. C., Özdil, P. G., Genoud, L., Hurtak, F., & Ramdya, P. (2024). Neuromechfly v2: Simulating embodied sensorimotor control in adult drosophila. Nature Methods, 21(12), 2353–2362. <https://doi.org/10.1038/s41592-024-02497-y>
473. Wang, C., Zhang, T., Chen, X., He, S., Li, S., & Wu, S. (2023). Brainpy, a flexible, integrative, efficient, and extensible framework for general-purpose brain dynamics programming. eLife, 12. <https://doi.org/10.7554/eLife.86365>
474. Wang, E. Y., Fahey, P. G., Ding, Z., Papadopoulos, S., Ponder, K., Weis, M. A., Chang, A., Muhammad, T., Patel, S., Ding, Z., Tran, D., Fu, J., Schneider-Mizell, C. M., da Costa, N. M., Reid, R. C., Collman, F., da Costa, N. M., Franke, K., Ecker, A. S., . . . Tolias, A. S. (2025). Foundation model of neural activity predicts response to new stimulus types. Nature, 640(8058), 470–477. <https://doi.org/10.1038/s41586-025-08829-y>
475. Wee, C. L., Nikitchenko, M., Wang, W., Luks-Morgan, S. J., Song, E., Gagnon, J. A., Randlett, O., Bianco, I. H., Lacoste, A. M. B., Glushenkova, E., Barrios, J. P., Schier, A. F., Kunes, S., Engert, F., & Douglass, A. D. (2019). Zebrafish oxytocin neurons drive nocifensive behavior via brainstem premotor targets. Nature Neuroscience, 22(9), 1477–1492. <https://doi.org/10.1038/s41593-019-0452-x>
476. Welniak–Kaminska, M., Fiedorowicz, M., Orzel, J., Bogorodzki, P., Modlinska, K., Stryjek, R., Chrzanowska, A., Pisula, W., & Grieb, P. (2019). Volumes of brain structures in captive wild-type and laboratory rats: 7T magnetic resonance in vivo automatic atlas-based study. PLOS ONE, 14(4), e0215348. <https://doi.org/10.1371/journal.pone.0215348>
477. White, A. J., & Rundle, H. D. (2015). Territory defense as a condition-dependent component of male reproductive success IN DROSOPHILA SERRATA: Territoriality IN DROSOPHILA SERRATA. Evolution, 69(2), 407–418. <https://doi.org/10.1111/evo.12580>
478. White, J. G., Southgate, E., Thomson, J. N., & Brenner, S. (1986). The structure of the nervous system of the nematode caenorhabditis elegans. Philosophical Transactions of the Royal Society of London. B, Biological Sciences, 314(1165), 1–340. <https://doi.org/10.1098/rstb.1986.0056>
479. White, R. M., Sessa, A., Burke, C., Bowman, T., LeBlanc, J., Ceol, C., Bourque, C., Dovey, M., Goessling, W., Burns, C. E., & Zon, L. I. (2008). Transparent adult zebrafish as a tool for in vivo transplantation analysis. Cell Stem Cell, 2(2), 183–189. <https://doi.org/10.1016/j.stem.2007.11.002>
480. Wildenberg, G. A., Rosen, M. R., Lundell, J., Paukner, D., Freedman, D. J., & Kasthuri, N. (2021). Primate neuronal connections are sparse in cortex as compared to mouse. Cell Reports, 36(11). <https://doi.org/10.1016/j.celrep.2021.109709>
481. Winding, M., Pedigo, B. D., Barnes, C. L., Patsolic, H. G., Park, Y., Kazimiers, T., Fushiki, A., Andrade, I. V., Khandelwal, A., Valdes-Aleman, J., Li, F., Randel, N., Barsotti, E., Correia, A., Fetter, R. D., Hartenstein, V., Priebe, C. E., Vogelstein, J. T., Cardona, A., & Zlatic, M. (2023). The connectome of an insect brain. Science, 379(6636), eadd9330. <https://doi.org/10.1126/science.add9330>
482. Witvliet, D., Mulcahy, B., Mitchell, J. K., Meirovitch, Y., Berger, D. R., Wu, Y., Liu, Y., Koh, W. X., Parvathala, R., Holmyard, D., Schalek, R. L., Shavit, N., Chisholm, A. D., Lichtman, J. W., Samuel, A. D. T., & Zhen, M. (2021). Connectomes across development reveal principles of brain maturation. Nature, 596(7871), 257–261. <https://doi.org/10.1038/s41586-021-03778-8>
483. Woodward, J. (2004). Making things happen: A theory of causal explanation. academic.oup.com. <https://doi.org/10.1093/0195155270.001.0001>
484. Wu, Y., Wu, S., Wang, X., Lang, C., Zhang, Q., Wen, Q., & Xu, T. (2022). Rapid detection and recognition of whole brain activity in a freely behaving caenorhabditis elegans. PLOS Computational Biology, 18(10), e1010594. <https://doi.org/10.1371/journal.pcbi.1010594>
485. Wu, Y., Huang, X., Wei, Z., Cheng, H., Xin, C., Chen, Z., Chen, B., Wu, Y., Wang, H., Zhang, T., Shi, R., Gao, X., Liang, Y., Zhao, P., & Chen, G. (2024). Towards resource efficiency: Practical insights into large-scale spark workloads at bytedance. Proc. VLDB Endow., 17(12), 3759–3771. <https://doi.org/10.14778/3685800.3685804>
486. Wu, Z., He, K., Chen, Y., Li, H., Pan, S., Li, B., Liu, T., Xi, F., Deng, F., Wang, H., Du, J., Jing, M., & Li, Y. (2022). A sensitive GRAB sensor for detecting extracellular ATP in vitro and in vivo. Neuron, 110(5), 770–782.e5. <https://doi.org/10.1016/j.neuron.2021.11.027>
487. Wullimann, M. F., Rupp, B., & Reichert, H. (1996). Neuroanatomy of the zebrafish brain. SpringerLink. <https://doi.org/10.1007/978-3-0348-8979-7>
488. Xu, C., Nedergaard, M., Fowell, D. J., Friedl, P., & Ji, N. (2024). Multiphoton fluorescence microscopy for in vivo imaging. Cell, 187(17), 4458–4487. <https://doi.org/10.1016/j.cell.2024.07.036>
489. Xu, M., Jarrell, T. A., Wang, Y., Cook, S. J., Hall, D. H., & Emmons, S. W. (2013). Computer assisted assembly of connectomes from electron micrographs: Application to caenorhabditis elegans. PLOS ONE, 8(1), e54050. <https://doi.org/10.1371/journal.pone.0054050>
490. Xue, F., Li, F., Zhang, K., Ding, L., Wang, Y., Zhao, X., Xu, F., Zhang, D., Sun, M., Lau, P., Zhu, Q., Zhou, P., & Bi, G. (2023). Multi-region calcium imaging in freely behaving mice with ultra-compact head-mounted fluorescence microscopes. National Science Review, 11(1). <https://doi.org/10.1093/nsr/nwad294>
491. Yamaura, H., Igarashi, J., & Yamazaki, T. (2020). Simulation of a human-scale cerebellar network model on the k computer. Frontiers in Neuroinformatics, 14. <https://doi.org/10.3389/fninf.2020.00016>
492. Yang, B., Lange, M., Millett-Sikking, A., Zhao, X., Bragantini, J., VijayKumar, S., Kamb, M., Gómez-Sjöberg, R., Solak, A. C., Wang, W., Kobayashi, H., McCarroll, M. N., Whitehead, L. W., Fiolka, R. P., Kornberg, T. B., York, A. G., & Royer, L. A. (2022). Daxi—high-resolution, large imaging volume and multi-view single-objective light-sheet microscopy. Nature Methods, 19(4), 461–469. <https://doi.org/10.1038/s41592-022-01417-2>
493. Yang, H. H., St-Pierre, F., Sun, X., Ding, X., Lin, M. Z., & Clandinin, T. R. (2016). Subcellular imaging of voltage and calcium signals reveals neural processing in vivo. Cell, 166(1), 245–257. <https://doi.org/10.1016/j.cell.2016.05.031>
494. Yavuz, E., Turner, J., & Nowotny, T. (2016). Genn: A code generation framework for accelerated brain simulations. Scientific Reports, 6(1), 18854. <https://doi.org/10.1038/srep18854>
495. Yemini, E., Lin, A., Nejatbakhsh, A., Varol, E., Sun, R., Mena, G. E., Samuel, A. D. T., Paninski, L., Venkatachalam, V., & Hobert, O. (2021). Neuropal: A multicolor atlas for whole-brain neuronal identification in c. Elegans. Cell, 184(1), 272–288.e11. <https://doi.org/10.1016/j.cell.2020.12.012>
496. Yurkovic, A., Wang, O., Basu, A. C., & Kravitz, E. A. (2006). Learning and memory associated with aggression in drosophila melanogaster. Proceedings of the National Academy of Sciences, 103(46), 17519–17524. <https://doi.org/10.1073/pnas.0608211103>
497. Zador, A., Escola, S., Richards, B., Ölveczky, B., Bengio, Y., Boahen, K., Botvinick, M., Chklovskii, D., Churchland, A., Clopath, C., DiCarlo, J., Ganguli, S., Hawkins, J., Körding, K., Koulakov, A., LeCun, Y., Lillicrap, T., Marblestone, A., Olshausen, B., . . . Tsao, D. (2023). Catalyzing next-generation artificial intelligence through neuroai. Nature Communications, 14(1), 1597. <https://doi.org/10.1038/s41467-023-37180-x>
498. Zhang, S., Ye, J., Miao, C., Tsao, A., Cerniauskas, I., Ledergerber, D., Moser, M., & Moser, E. I. (2013). Optogenetic dissection of entorhinal-hippocampal functional connectivity. Science, 340(6128), 1232627. <https://doi.org/10.1126/science.1232627>
499. Zhang, X., Petruzziello, F., Zani, F., Fouillen, L., Andren, P. E., Solinas, G., & Rainer, G. (2012). High identification rates of endogenous neuropeptides from mouse brain. Journal of Proteome Research, 11(5), 2819–2827. <https://doi.org/10.1021/pr3001699>
500. Zhang, Y., Rózsa, M., Liang, Y., Bushey, D., Wei, Z., Zheng, J., Reep, D., Broussard, G. J., Tsang, A., Tsegaye, G., Narayan, S., Obara, C. J., Lim, J., Patel, R., Zhang, R., Ahrens, M. B., Turner, G. C., Wang, S. S., Korff, W. L., . . . Looger, L. L. (2023). Fast and sensitive gcamp calcium indicators for imaging neural populations. Nature, 615(7954), 884–891. <https://doi.org/10.1038/s41586-023-05828-9>
501. Zhang, Z., Bai, L., Cong, L., Yu, P., Zhang, T., Shi, W., Li, F., Du, J., & Wang, K. (2021). Imaging volumetric dynamics at high speed in mouse and zebrafish brain with confocal light field microscopy. Nature Biotechnology, 39(1), 74–83. <https://doi.org/10.1038/s41587-020-0628-7>
502. Zhao, M., Wang, N., Jiang, X., Ma, X., Ma, H., He, G., Du, K., Ma, L., & Huang, T. (2024). An integrative data-driven model simulating c. Elegans brain, body and environment interactions. Nature Computational Science, 4(12), 978–990. <https://doi.org/10.1038/s43588-024-00738-w>
503. Zhao, X., Lenek, D., Dag, U., Dickson, B. J., & Keleman, K. (2018). Persistent activity in a recurrent circuit underlies courtship memory in drosophila. eLife, 7. <https://doi.org/10.7554/eLife.31425>
504. Zheng, Z., Lauritzen, J. S., Perlman, E., Robinson, C. G., Nichols, M., Milkie, D., Torrens, O., Price, J., Fisher, C. B., Sharifi, N., Calle-Schuler, S. A., Kmecova, L., Ali, I. J., Karsh, B., Trautman, E. T., Bogovic, J. A., Hanslovsky, P., Jefferis, G. S. X. E., Kazhdan, M., . . . Bock, D. D. (2018). A complete electron microscopy volume of the brain of adult drosophila melanogaster. Cell, 174(3), 730–743.e22. <https://doi.org/10.1016/j.cell.2018.06.019>
505. Zimmerman, C. A., Lin, Y., Leib, D. E., Guo, L., Huey, E. L., Daly, G. E., Chen, Y., & Knight, Z. A. (2016). Thirst neurons anticipate the homeostatic consequences of eating and drinking. Nature, 537(7622), 680–684. <https://doi.org/10.1038/nature18950>
506. Zocchi, D., Nguyen, M., Marquez-Legorreta, E., Siwanowicz, I., Singh, C., Prober, D. A., Hillman, E. M. C., & Ahrens, M. B. (2025). Days-old zebrafish rapidly learn to recognize threatening agents through noradrenergic and forebrain circuits. Current Biology, 35(1), 163–176.e4. <https://doi.org/10.1016/j.cub.2024.11.057>
507. Zong, W., Obenhaus, H. A., Skytøen, E. R., Eneqvist, H., Jong, N. L. d., Vale, R., Jorge, M. R., Moser, M., & Moser, E. I. (2022). Large-scale two-photon calcium imaging in freely moving mice. Cell, 185(7), 1240–1256.e30. <https://doi.org/10.1016/j.cell.2022.02.017>
508. Zwarts, L., Versteven, M., & Callaerts, P. (2012). Genetics and neurobiology of aggression in drosophila. Fly, 6(1), 35–48. <https://doi.org/10.4161/fly.19249>
