Sanes, D. H. et al. Development of the Nervous System (Academic Press, 2011).
Smith, J. L. & Schoenwolf, G. C. Neurulation: coming to closure. Trends Neurosci. 20, 510–517 (1997).
Randlett, O. et al. Whole-brain activity mapping onto a zebrafish brain atlas. Nat. Methods 12, 1039–1046 (2015).
Keller, P. J. & Ahrens, M. B. Visualizing whole-brain activity and development at the single-cell level using light-sheet microscopy. Neuron 85, 462–483 (2015).
Alivisatos, A. P. et al. The brain activity map. Science 339, 1284–1285 (2013).
Stringer, C. et al. Spontaneous behaviors drive multidimensional, brainwide activity. Science 364, 255 (2019).
Fox, M. D. & Raichle, M. E. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700–711 (2007).
Ahrens, M. B., Orger, M. B., Robson, D. N., Li, J. M. & Keller, P. J. Whole-brain functional imaging at cellular resolution using light-sheet microscopy. Nat. Methods 10, 413–420 (2013).
Hong, G. & Lieber, C. M. Novel electrode technologies for neural recordings. Nat. Rev. Neurosci. 20, 330–345 (2019).
Jun, J. J. et al. Fully integrated silicon probes for high-density recording of neural activity. Nature 551, 232–236 (2017).
Abbott, J. et al. A nanoelectrode array for obtaining intracellular recordings from thousands of connected neurons. Nat. Biomed. Eng. 4, 232–241 (2020).
Chiang, C. H. et al. Development of a neural interface for high-definition, long-term recording in rodents and nonhuman primates. Sci. Transl. Med. 12, eaay4682 (2020).
Musk, E. An integrated brain–machine interface platform with thousands of channels. J. Med. Internet Res. 21, e16194 (2019).
McDole, K. et al. In toto imaging and reconstruction of post-implantation mouse development at the single-cell level. Cell 175, 859–876 (2018).
Kasthuri, N. et al. Saturated reconstruction of a volume of neocortex. Cell 162, 648–661 (2015).
Pijuan-Sala, B. et al. A single-cell molecular map of mouse gastrulation and early organogenesis. Nature 566, 490–495 (2019).
Liu, J. et al. Syringe-injectable electronics. Nat. Nanotechnol. 10, 629–636 (2015).
Tian, B. et al. Macroporous nanowire nanoelectronic scaffolds for synthetic tissues. Nat. Mater. 11, 986–994 (2012).
Xie, C. et al. Three-dimensional macroporous nanoelectronic networks as minimally invasive brain probes. Nat. Mater. 14, 1286–1292 (2015).
Dai, X., Zhou, W., Gao, T., Liu, J. & Lieber, C. M. Three-dimensional mapping and regulation of action potential propagation in nanoelectronics-innervated tissues. Nat. Nanotechnol. 11, 776–782 (2016).
Fu, T. M. et al. Stable long-term chronic brain mapping at the single-neuron level. Nat. Methods 13, 875–882 (2016).
Li, Q. et al. Cyborg organoids: implantation of nanoelectronics via organogenesis for tissue-wide electrophysiology. Nano Lett. 19, 5781–5789 (2019).
Le Floch, P. et al. Stretchable mesh nanoelectronics for 3D single-cell chronic electrophysiology from developing brain organoids. Adv. Mater. 34, 2106829 (2022).
Xu, T. et al. Characterization of the mechanical behavior of SU-8 at microscale by viscoelastic analysis. J. Micromech. Microeng. 26, 105001 (2016).
Gupta, P., Bera, M. & Maji, P. K. Nanotailoring of sepiolite clay with poly [styrene‐b‐(ethylene‐co‐butylene)‐b‐styrene]: structure–property correlation. Polym. Adv. Technol. 28, 1428–1437 (2017).
Chanthasopeephan, T., Desai, J. P. & Lau, A. C. W. Study of soft tissue cutting forces and cutting speeds. Stud. Health Technol. Inform. 98, 56–62 (2004).
Spruiell Eldridge, S. L. et al. A focal impact model of traumatic brain injury in Xenopus tadpoles reveals behavioral alterations, neuroinflammation, and an astroglial response. Int. J. Mol. Sci. 23, 7578 (2022).
Yoshino, J. & Tochinai, S. Successful reconstitution of the non‐regenerating adult telencephalon by cell transplantation in Xenopus laevis. Dev. Growth Differ. 46, 523–534 (2004).
Khodagholy, D. et al. NeuroGrid: recording action potentials from the surface of the brain. Nat. Neurosci. 18, 310–315 (2015).
Yang, X. et al. Bioinspired neuron-like electronics. Nat. Mater. 18, 510–517 (2019).
Liu, Y. et al. Soft and elastic hydrogel-based microelectronics for localized low-voltage neuromodulation. Nat. Biomed. Eng. 3, 58–68 (2019).
Minev, I. R. et al. Electronic dura mater for long-term multimodal neural interfaces. Science 347, 156–163 (2015).
Kozai, T. D. Y. et al. Ultrasmall implantable composite microelectrodes with bioactive surfaces for chronic neural interfaces. Nat. Mater. 11, 1065–1073 (2012).
Manita, S. & Ross, W. N. Synaptic activation and membrane potential changes modulate the frequency of spontaneous elementary Ca2+ release events in the dendrites of pyramidal neurons. J. Neurosci. 29, 7833–7845 (2009).
Ciarleglio, C. M. et al. Multivariate analysis of electrophysiological diversity of Xenopus visual neurons during development and plasticity. Elife 4, e11351 (2015).
Maden, M. Salamanders as key models for development and regeneration research. Methods Mol. Biol. 2562, 1 (2023).
Steinmetz, N. A. et al. Neuropixels 2.0: a miniaturized high-density probe for stable, long-term brain recordings. Science 372, eabf4588 (2021).
Schoonover, C. E., Ohashi, S. N., Axel, R. & Fink, A. J. P. Representational drift in primary olfactory cortex. Nature 594, 541–546 (2021).
Denker, M., Yegenoglu, A. & Grün, S. Collaborative HPC-enabled workflows on the HBP Collaboratory using the Elephant framework. Neuroinformatics 19, FZJ-2018-04114 (2018).
Cowley, B. et al. DataHigh: graphical user interface for visualizing and interacting with high-dimensional neural activity. J. Neural Eng. 10, 066012–066019 (2013).
Wei, X. et al. Single-cell stereo-seq reveals induced progenitor cells involved in axolotl brain regeneration. Science 377, eabp9444 (2022).
Lust, K. et al. Single-cell analyses of axolotl telencephalon organization, neurogenesis, and regeneration. Science 377, eabp9262 (2022).
Takahashi, M. & Osumi, N. The method of rodent whole embryo culture using the rotator-type bottle culture system. J. Vis. Exp. 42, 2170 (2010).
Huang, Q. et al. Intravital imaging of mouse embryos. Science 368, 181–186 (2020).
Jouary, A., Haudrechy, M., Candelier, R. & Sumbre, G. A 2D virtual reality system for visual goal-driven navigation in zebrafish larvae. Sci. Rep. 6, 34015 (2016).
Le Floch, P. et al. 3D spatiotemporally scalable in vivo neural probes based on fluorinated elastomers. Nat. Nanotechnol. 19, 319–329 (2024).
Fowkes, F. M. Attractive forces at interfaces. Ind. Eng. Chem. 56, 40–52 (1964).
Karimi, K. et al. Xenbase: a genomic, epigenomic and transcriptomic model organism database. Nucleic Acids Res. 46, D861–D868 (2018).
Aguilera-Castrejon, A. et al. Ex utero mouse embryogenesis from pre-gastrulation to late organogenesis. Nature 593, 119–124 (2021).
Huang, Y., Wilkie, R. & Wilson, V. Methods for precisely localized transfer of cells or DNA into early postimplantation mouse embryos. J. Vis. Exp. 106, e53295 (2015).
Lohmiller, J. J., Swing, S. P. & Hanson, M. M. in The Laboratory Rat 3rd edn (eds Suckow, M. A. et al.) 157–179 (Elsevier, 2020).
Vczian, A. S. & Zuber, M. E. A simple behavioral assay for testing visual function in Xenopus laevis. J. Vis. Exp. 88, 51726 (2014).
Dong, W. et al. Visual avoidance in Xenopus tadpoles is correlated with the maturation of visual responses in the optic tectum. J. Neurophysiol. 101, 803–815 (2009).
Pratt, K. G. & Khakhalin, A. S. Modeling human neurodevelopmental disorders in the Xenopus tadpole: from mechanisms to therapeutic targets. Dis. Models Mech. 6, 1057–1065 (2013).
Klymkowsky, M. W. Whole-mount immunocytochemistry in Xenopus. Cold Spring Harb. Protoc. 2018, pdb-prot097295 (2018).
Hamilton, P. W. & Henry, J. J. Prolonged in vivo imaging of Xenopus laevis. Dev. Dyn. 243, 1011–1019 (2014).
Kamei, M. & Weinstein, B. M. Long-term time-lapse fluorescence imaging of developing zebrafish. Zebrafish 2, 113–123 (2005).
Kamei, M., Isogai, S., Pan, W. & Weinstein, B. M. Imaging blood vessels in the zebrafish. Methods Cell. Biol. 100, 27–54 (2010).
Chaure, F. J. et al. A novel and fully automatic spike-sorting implementation with variable number of features. J. Neurophysiol. 120, 1859–1871 (2018).
Chung, J. E. et al. A fully automated approach to spike sorting. Neuron 95, 1381–1394 (2017).
Buccino, A. P. et al. SpikeInterface, a unified framework for spike sorting. eLife 9, e61834 (2020).
Bartolo, R., Saunders, R. C., Mitz, A. R. & Averbeck, B. B. Information-limiting correlations in large neural populations. J. Neurosci. 40, 1668–1678 (2020).
Insel, N. & Barnes, C. A. Differential activation of fast-spiking and regular-firing neuron populations during movement and reward in the dorsal medial frontal cortex. Cereb. Cortex 25, 2631–2647 (2015).
Ogden, R. W. Non-linear Elastic Deformations (Courier, 1997).
Amar, M. B. & Goriely, A. Growth and instability in elastic tissues. J. Mech. Phys. Solids 53, 2284–2319 (2005).
Garaschuk, O., Hanse, E. & Konnerth, A. Developmental profile and synaptic origin of early network oscillations in the CA1 region of rat neonatal hippocampus. J. Physiol. 507, 219–236 (1998).