The DOI matches its Crossref record; 99 of the 101 references it cites were found in a registry or an index.
Dense connectomic reconstruction in layer 4 of the somatosensory cortex
Open accessPublished version of a preprint
Abstract
Brain anatomy revealed in startling detail: The mammalian cerebral cortex is an enormously complex network of neuronal processes that are long and thin, branching, and extremely densely packed. This high packing density has made the reconstruction of cortical neuronal networks challenging. Motta et al. used advanced automated imaging and analysis tools to reconstruct with high spatial resolution the morphological features of 89 neurons and their connections in the mouse barrel cortex. The reconstruction covered an area more than two orders of magnitude larger than earlier neuroanatomical mapping attempts. This approach revealed information about the connectivity of inhibitory and excitatory synapses of corticocortical as well as excitatory thalamocortical connections. Science , this issue p. eaay3134
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“However, this characteristic is often misinterpreted as implying that synapses on dendritic shafts are mostly SS (Motta et al., 2019; Karimi et al., 2020; Loomba et al., 2022).”
From Unambiguous identification of asymmetric and symmetric synapses using volume electron microscopy (2024) · cites this paper as Motta et al., 2019
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884
Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs
Markram, H., Lübke, J., Frotscher, M., & Sakmann, B. (1997) · Science · doi:10.1126/science.275.5297.213 · Cited by 3,832
Structural basis of long-term potentiation in single dendritic spines
Matsuzaki, M., Honkura, N., Ellis-Davies, G. C. R., & Kasai, H. (2004) · Nature · doi:10.1038/nature02617 · Cited by 2,483
Globally optimal stitching of tiled 3D microscopic image acquisitions
Preibisch, S., Saalfeld, S., & Tomancak, P. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp184 · Cited by 2,452
Long-term in vivo imaging of experience-dependent synaptic plasticity in adult cortex
Trachtenberg, J. T., Chen, B. E., Knott, G. W., Feng, G., Sanes, J. R., Welker, E., & Svoboda, K. (2002) · Nature · doi:10.1038/nature01273 · Cited by 1,927
Serial Block-Face Scanning Electron Microscopy to Reconstruct Three-Dimensional Tissue Nanostructure
Denk, W., & Horstmann, H. (2004) · PLoS Biology · doi:10.1371/journal.pbio.0020329 · Cited by 1,644
Reconstruction and Simulation of Neocortical Microcircuitry
Markram, H., Muller, E., Ramaswamy, S., Reimann, M. W., Abdellah, M., Sanchez, C. A., Ailamaki, A., Alonso-Nanclares, L., Antille, N., Arsever, S., et al. (2015) · Cell · doi:10.1016/j.cell.2015.09.029 · Cited by 1,557
Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex
The Petilla Interneuron Nomenclature Group (PING) (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2402 · Cited by 1,491
A place theory of sound localization.
Jeffress, L. A. (1948) · Journal of Comparative and Physiological Psychology · doi:10.1037/h0061495 · Cited by 1,484
Flagged first, then by citations
2 of the 101 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- D. Rolnick N. Shavit Morphological error detection in 3D segmentations. arXiv:1705.10882 [cs.CV] (30 May 2017). · arXiv
Cited by
Sorted by citations
Items 201–300 of 336
Kiritani, T., Pala, A., Gasselin, C., Crochet, S., & Petersen, C. C. H. (2023) · PLoS ONE · doi:10.1371/journal.pone.0287174 · Cited by 5
Organelle landscape analysis using a multiparametric particle-based method
Kurikawa, Y., Koyama‐Honda, I., Igarashi, K., Tamura, N., Koike, S., & Mizushima, N. (2024) · PLoS Biology · doi:10.1371/journal.pbio.3002777 · Cited by 4
Neural network embedding of functional microconnectome
Shirakami, A., Hase, T., Yamaguchi, Y., & Shimono, M. (2024) · Network Neuroscience · doi:10.1162/netn_a_00424 · Cited by 4
Probing fine-scale connections in the brain
Landhuis, E. W.-Y. (2020) · Nature · doi:10.1038/d41586-020-02947-5 · Cited by 6
3D Synaptic Organization of the Rat CA1 and Alterations Induced by Cocaine Self-Administration
Blázquez‐Llorca, L., Miguéns, M., Montero‐Crespo, M., Selvas, A., González‐Soriano, J., Ambrosio, E., & DeFelipe, J. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa331 · Cited by 5
Lazar, J. W. (2022) · Journal of the History of the Neurosciences · doi:10.1080/0964704x.2022.2066409 · Cited by 5
Hippocampal and Medial Prefrontal Cortex Fractal Spiking Patterns Encode Episodes and Rules
Srinivasan, A., Srinivasan, A., Goodman, M. R., Riceberg, J. S., Guise, K. G., & Shapiro, M. L. (2023) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2023.113508 · Cited by 5
Targeting operational regimes of interest in recurrent neural networks
Ekelmans, P., Kraynyukova, N., & Tchumatchenko, T. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011097 · Cited by 7
Whole-Brain Evaluation of Cortical Microconnectomes
Matsuda, K., Shirakami, A., Nakajima, R., Akutsu, T., & Shimono, M. (2023) · eNeuro · doi:10.1523/eneuro.0094-23.2023 · Cited by 4
Bosch, C., Ackels, T., Pacureanu, A., Zhang, Y., Peddie, C. J., Berning, M., Rzepka, N., Zdora, M., Whiteley, I., Storm, M., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.13.426503 · Cited by 5
Self-citationBio-instantiated recurrent neural networksPreprint
Goulas, A., Damicelli, F., & Hilgetag, C. C. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.22.427744 · Cited by 5
Beniaguev, D., Shapira, S., Segev, I., & London, M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.28.478132 · Cited by 5
Dissecting origins of wiring specificity in dense reconstructions of neural tissuePreprint
Harth, P., Udvary, D., Boelts, J., Macke, J. H., Baum, D., Hege, H., & Oberlaender, M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.14.628490 · Cited by 6
Unifying the Electron Microscopy Multiverse through a Large-scale Foundation ModelPreprint
He, L., Shi, R., Wang, W., Fang, G., Cai, Y., & Ма, Л. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.13.648639 · Cited by 5
Accelerating Neuron Reconstruction with PATHFINDERPreprint
Januszewski, M., Templier, T., Hayworth, K. J., Peale, D. R., & Hess, H. F. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.16.654254 · Cited by 5
Modeling and simulation of neocortical micro- and mesocircuitry (Part I, anatomy)
Reimann, M., Bolaños‐Puchet, S., Courcol, J.-D., Santander, D. E., Arnaudon, A., Coste, B., Delalondre, F., Delemontex, T., Devresse, A., Dictus, H., et al. (2026) · eLife · doi:10.7554/elife.99688.3 · Cited by 5
What connectomics can learn from genomics
Chen, P. B., & Flint, J. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009692 · Cited by 4
Wilson, A. M., & Babadi, M. (2023) · Patterns · doi:10.1016/j.patter.2023.100693 · Cited by 4
Antao, N. V., Sall, J., Petzold, C. J., Ekiert, D. C., Bhabha, G., & Liang, F. (2024) · PLoS ONE · doi:10.1371/journal.pone.0301284 · Cited by 5
A map of neural signals and circuits traces the logic of brain computation
Petkova, M. D., & Schuhknecht, G. F. P. (2025) · Nature · doi:10.1038/d41586-025-00908-4 · Cited by 4
Rah, J., & Choi, J. H. (2022) · Molecules and Cells · doi:10.14348/molcells.2022.2021 · Cited by 4
Connectivity Matrix Seriation via Relaxation
Borst, A., & Denk, W. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011904 · Cited by 4
Choi, J., Hildebrand, D. G. C., Moon, J., Quan, T. M., Tuấn, T. A., Ko, S., & Jeong, W. (2021) · IEEE Access · doi:10.1109/access.2021.3084066 · Cited by 4
Madany, M., Marcus, K., Peltier, S. T., Ellisman, M. H., & Altıntaş, İ. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378053 · Cited by 4
Regional and LTP-Dependent Variation of Synaptic Information Storage Capacity in Rat HippocampusPreprint
Samavat, M., Bartol, T. M., Bromer, C., Bowden, J. B., Hubbard, D. D., Hanka, D. C., Kuwajima, M., Mendenhall, J. M., Parker, P. H., Abraham, W. C., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.29.505464 · Cited by 3
Lu, C., Chen, K., Qiu, H., Chen, X., Chen, G., Qi, X., & Jiang, H. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.12.548636 · Cited by 4
A Novel Semi-automated Proofreading and Mesh Error Detection Pipeline for Neuron ExtensionPreprint
Joyce, J., Chalavadi, R., Chan, J., Tanna, S., Xenes, D., Kuo, N. P., Rose, V. A., Matelsky, J., Kitchell, L. M., Bishop, C. A., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.20.563359 · Cited by 4
Samavat, M., Bartol, T. M., Bromer, C., Hubbard, D. D., Hanka, D. C., Kuwajima, M., Mendenhall, J. M., Parker, P. H., Bowden, J. B., Abraham, W. C., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.12.574766 · Cited by 4
Connectomic traces of Hebbian plasticity in the entorhinal-hippocampal systemPreprint
Grasso, E., Corteze, M., & Schmidt, H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.07.647631 · Cited by 3
Luján, R., Turégano-López, M., Aguado, C., & Merchán-Pérez, Á. (2024) · Advanced technology in neuroscience . · doi:10.4103/atn.atn-d-24-00009 · Cited by 3
Model-agnostic linear-memory online learning in spiking neural networks
Wang, C., Dong, X., Ji, Z., Xiao, M., Jiang, J., Liu, X., Huan, Y., & Wu, S. (2026) · Nature Communications · doi:10.1038/s41467-026-68453-w · Cited by 3
Mathieu, R., Draia-Nicolau, T., Corbières, L., Govindan, A. D., Bensa, V., Pallesi‐Pocachard, E., Silvagnoli, L., Represa, A., Cardoso, C., Telley, L., et al. (2026) · Nature Communications · doi:10.1038/s41467-025-68059-8 · Cited by 6
Comparative prospects of imaging methods for whole-brain mammalian connectomics
Collins, L. T., Huffman, T., & Koene, R. A. (2025) · Cell Reports Methods · doi:10.1016/j.crmeth.2025.100988 · Cited by 5
Bayesian reconstruction of memories stored in neural networks from their connectivity
Goldt, S., Krząkała, F., Zdeborová, L., & Brunel, N. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010813 · Cited by 4
Rothman, J. S., Borges-Merjane, C., Holderith, N. B., Jónás, P., & Silver, R. A. (2023) · PLoS ONE · doi:10.1371/journal.pone.0277148 · Cited by 4
Fast Compressed Segmentation Volumes for Scientific Visualization
Piochowiak, M., & Dachsbacher, C. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2023.3326573 · Cited by 3
A milestone map of mouse-brain connectivity reveals challenging new terrain for scientists
Eisenstein, M. S. (2024) · Nature · doi:10.1038/d41586-024-01096-3 · Cited by 3
Geometrical properties of three-dimensional crossed nanowire networks
Gomes, T. D. C. S. C., Marchal, N., Moureaux, A., de Wergifosse, S., Chopin, C., Piraux, L., Medina, J. D. L. T., & Araujo, F. A. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.023211 · Cited by 3
Cell nuclei segmentation in mm-scale x-ray holographic nanotomography images of mouse brain tissue
Nathansen, A., Clausen, M., Berning, M., MacKenzie, E., Zhang, Y., Pacureanu, A., Schaefer, A. T., Rzepka, N., & Bosch, C. (2024) · Developments in X-Ray Tomography · doi:10.1117/12.3028309 · Cited by 3
Self-citationA detailed theory of thalamic and cortical microcircuits for predictive visual inference
George, D., Lázaro-Gredilla, M., Lehrach, W. P., Dedieu, A., Zhou, G., & Marino, J. (2025) · Science Advances · doi:10.1126/sciadv.adr6698 · Cited by 3
nAdder: A scale-space approach for the 3D analysis of neuronal traces
Phan, M.-S., Matho, K. S., Beaurepaire, E., Livet, J., & Chessel, A. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010211 · Cited by 3
Dervinis, M., & Major, G. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.20.485046 · Cited by 3
Morphology and ultrastructure of external sense organs of Drosophila larvaePreprint
Richter, V., Rist, A., Kislinger, G., Laumann, M., Schoofs, A., Miroschnikow, A., Pankratz, M. J., Cardona, A., & Thum, A. S. (2023) · eLife · doi:10.7554/elife.91155.1 · Cited by 2
Han, X., Li, P. H., Wang, S., Blakely, T. M., Aggarwal, S., Gopalani, B., Sanchez, M., Schalek, R. L., Meirovitch, Y., Lin, Z., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.24.563674 · Cited by 3
Morphology and ultrastructure of external sense organs of Drosophila larvaePreprint
Richter, V., Rist, A., Kislinger, G., Laumann, M., Schoofs, A., Miroschnikow, A., Pankratz, M. J., Cardona, A., & Thum, A. S. (2024) · eLife · doi:10.7554/elife.91155.2 · Cited by 3
An implantable biohybrid nerve model towards synaptic deep brain stimulationPreprint
Sifringer, L., Fratzl, A., Clément, B., Chansoria, P., Mönkemöller, L. S., Duru, J., Ihle, S. J., Steffens, S., Beltraminelli, A., Ceylan, E., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.31.596665 · Cited by 3
BrainScale, Enabling Scalable Online Learning in Spiking Neural NetworksPreprint
Wang, C., Dong, X., Ji, Z., Jiang, J., Liu, X., & Wu, S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.24.614728 · Cited by 3
Connectomic Organization of the Suprachiasmatic NucleusPreprint
Liu, J., Yu, J., Shen, L., Chen, X., Ma, L., Zhai, H., Li, L., Zhang, L., Wang, L., Yuan, J., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.20.619252 · Cited by 3
PIB: Parallel ion beam etching of sections collected on wafer for ultra large-scale connectomicsPreprint
Ma, H., Lv, Y., Wang, M., Zhao, Z., Liu, J., Dong, X., Zhang, Y., Guo, J., Chen, B., Zhang, L., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.25.650569 · Cited by 3
Identifying determinants of synaptic specificity by integrating connectomes and transcriptomes
Yoo, J., Dombrovski, M., Mirshahidi, P., Nern, A., LoCascio, S. A., Zipursky, S. L., & Kurmangaliyev, Y. Z. (2023) · doi:10.1101/2023.04.03.534791 · Cited by 39
“beat-VI[4] (beat-VI null) deleted sequence(19bp): AAGGATACGGAGCCGGCCA”
VSOT: volume-surface optimization for accurate ultrastructure analysis of dendritic spines
Lyu, B., Wang, J., Risher, W. C., & Yu, G. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf220 · Cited by 3
Ultrastructural analysis of synapses after induction of spike-timing-dependent plasticity
Wang, R., Schweizer, M., Anisimova, M., Gee, C. E., & Oertner, T. G. (2025) · Cell Reports Methods · doi:10.1016/j.crmeth.2025.101142 · Cited by 2
Brain Extracellular Space: From an Overlooked Dimension to Catalyst of a Novel Neuroscience Paradigm
Han, H., Dai, H., Lopes, L. S., Ni, R., Combes, B. F., Song, Y., Tan, H., Xu, M., Li, H., Lv, S., et al. (2026) · Cyborg and Bionic Systems · doi:10.34133/cbsystems.0529 · Cited by 2
Tamada, H. (2026) · The Journal of Physiology · doi:10.1113/jp287612 · Cited by 2
STED microscopy reveals dendrite-specificity of spines in turtle cortex
Knobloch, J. A., Laurent, G., & Lauterbach, M. A. (2023) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2023.102541 · Cited by 2
Liu, J., Hong, B., Xiao, C., Zhai, H., Shen, L., Xie, Q., & Han, H. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124562 · Cited by 2
Hanken, J., Bemis, W. E., Cervantes, P., Gage, M., Turney, S. G., & Woodward, J. (2025) · Bulletin of the Museum of Comparative Zoology · doi:10.3099/mcz84 · Cited by 2
Toward Biophysical Mechanisms of Neocortical Computation after 50 Years of Barrel Cortex Research
Petersen, C. C. H., Knott, G. W., Holtmaat, A. J. D. G., & Schürmann, F. (2020) · Function · doi:10.1093/function/zqaa046 · Cited by 2
MULTI-FEATURE FUSION EMOTION RECOGNITION BASED ON RESTING EEG
Zhang, J., Gu, L., CHEN, Y., Zhu, G., OU, L., WANG, L., LI, X., & Zhong, L. (2022) · Journal of Mechanics in Medicine and Biology · doi:10.1142/s0219519422400024 · Cited by 2
Simplex closing probabilities in directed graphs
Unger, F., Krebs, J., & Müller, M. G. (2022) · Computational Geometry · doi:10.1016/j.comgeo.2022.101941 · Cited by 2
From the beginnings to multidimensional light and electron microscopy of virus morphogenesis
Sanders, S., Jensen, Y., Reimer, R., & Bosse, J. B. (2023) · Advances in virus research · doi:10.1016/bs.aivir.2023.05.001 · Cited by 2
Learning to Correct Sloppy Annotations in Electron Microscopy Volumes
Chen, M., Renuka, M. B., Mi, L., Lichtman, J. W., Shavit, N., & Meirovitch, Y. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00450 · Cited by 2
Morphometric reconstructions atlas shows insult-driven plasticity in cortical VIP/ChAT interneuronsPreprint
Yayon, N., Amsalem, O., Dudai, A., Yakov, O., Adam, G., Tessier‐Lavigne, M., Renier, N., Segev, I., London, M., & Soreq, H. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.25.263178 · Cited by 2
The Impact of Neuron Morphology on Cortical Network ArchitecturePreprint
Udvary, D., Harth, P., Macke, J. H., Hege, H.-C., de Kock, C. P. J., Sakmann, B. J., & Oberlaender, M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.13.381087 · Cited by 2
Excitability and synaptic transmission after vitrification of mouse corticohippocampal slicesPreprint
German, A., & Akdaş, E. Y. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.03.597218 · Cited by 3
Connectomic analysis of astrocyte-synapse interactions in the cerebral cortexPreprint
Yener, Y., Motta, A., & Helmstaedter, M. N. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.20.639274 · Cited by 2
Self-citationFritz, I., Wang, F., Chirif, R., Malakasis, N., Gjorgjieva, J., & Castro, A. F. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.26.666968 · Cited by 3
SAvEM3: Pretrained and Distilled Models for General-purpose 3D Neuron Reconstruction
Zhai, H., Guo, J., Zhang, Y.-C. S. L.-G., Liu, J., & Han, H. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822494 · Cited by 1
Building the connectome of a small brain with a simple stochastic developmental generative model
Richter, O., & Schneidman, E. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2504913122 · Cited by 4
Structure–function relationships across scales: implications for atlases
Constable, R. T. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1750272 · Cited by 1
Generation of Axons and Dendrites
Ma, L. (2022) · Springer eBooks · doi:10.1007/978-3-030-88832-9_188 · Cited by 1
Holtmaat, A. J. D. G., & Lee, K.-S. (2023) · Oxford University Press eBooks · doi:10.1093/med/9780197676158.003.0013 · Cited by 1
Domain Adaptive Synapse Detection with Weak Point Annotations
Chen, Q., Huang, W., Zhang, Y., & Xiong, Z. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635310 · Cited by 1
3D Quantitative Analysis of Synaptic Ultrastructure in Identified Long-Range Projection Axons
Martin-Correa, P. J., Rodriguez-Moreno, J., Rollenhagen, A., Lübke, J. H. R., & Clascá, F. (2024) · Neuromethods · doi:10.1007/978-1-0716-4019-7_11 · Cited by 1
Beyond neurons: computer vision methods for analysis of morphologically complex astrocytes
Syed, T. A., Youssef, M., Schober, A. L., Kubota, Y., Murai, K. K., & Salmon, C. K. (2024) · Frontiers in Computer Science · doi:10.3389/fcomp.2024.1156204 · Cited by 2
Performance analysis in serial-section electron microscopy image registration of neuronal tissue
Chen, B., Tong, X., Han, H., & Chen, X. (2022) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.2611465 · Cited by 1
(2020) · Neuromethods · doi:10.1007/978-1-0716-0691-9 · Cited by 1
Soma and Neurite Density MRI (SANDI) of the in-vivo mouse brainPreprint
Ianuș, A., Carvalho, J., Fernandes, F. F., Cruz, R., Chavarrías, C., Palombo, M., & Shemesh, N. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.11.455923 · Cited by 1
Pliable synaptic fidelity and excitatory inter-synaptic crosstalk in the intact brainPreprint
Reynolds, J. R., Jensen, T. P., Rama, S., Zheng, K., Savtchenko, L. P., & Rusakov, D. A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.04.483016 · Cited by 1
Kirchner, J. H., Euler, L., & Gjorgjieva, J. (2023) · eLife · doi:10.7554/elife.87527.1 · Cited by 1
Reply to Castro et al.: Do connectomes possess markers of activity-dependent synaptic plasticity?
Chapochnikov, N. M., Pehlevan, C., & Chklovskii, D. B. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2317056120
Author response: Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part I: Anatomy
Reimann, M., Bolaños‐Puchet, S., Courcol, J.-D., Santander, D. E., Arnaudon, A., Coste, B., Delalondre, F., Delemontex, T., Devresse, A., Dictus, H., et al. (2024) · eLife · doi:10.7554/elife.99688.1.sa4 · Cited by 1
Inferring Ligand-Receptor Interactions between neuronal subtypes during mouse cortical developmentPreprint
Mathieu, R., Corbières, L., Draia-Nicolau, T., Govindan, A. D., Bensa, V., Pallesi‐Pocachard, E., Silvagnoli, L., Represa, A., Cardoso, C., Telley, L., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.02.610245 · Cited by 1
A Computational Perspective on the No-Strong-Loops Principle in Brain NetworksPreprint
Hadaeghi, F., Fakhar, K., Khajehnejad, M. A., & Hilgetag, C. C. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.24.678310 · Cited by 2
Cell-type specific projection patterns promote balanced activity in cortical microcircuitsPreprint
Kurth, A. C., Albers, J., Diesmann, M., & van Albada, S. J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.03.616539 · Cited by 5
Uncovering the Basis of Human Connectome Complexity: The Role of Neuronal MorphologyPreprint
Barros-Zulaica, N., Santander, D. E., Kanari, L., Shi, Y., Perin, R., Pezzoli, M., Benavides‐Piccione, R., DeFelipe, J., de Kock, C. P. J., Segev, I., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.12.705493 · Cited by 1
Oliveira, R., Nguyen‐Duc, J., Brammerloh, M., & Jelescu, I. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.11.705314 · Cited by 1
Protein-guided RNA barcoding links transcriptomes to synaptic architecturePreprint
Urke, A., Dolan, M.-J., Silverman, J., Kim, M. T., Pineda, J., Garcia, S., Luu, J., Buckley, A. R., Kumar, V., Zhao, B., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.26.705527 · Cited by 2
Morphologically constrained modeling of spinous inhibition in the somato-sensory cortexPreprint
Gemin, O., Serna, P., Assendorp, N., Fossati, M., Rostaing, P., Triller, A., & Charrier, C. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.06.230722
Pan, X. (2020) · doi:10.33540/99
Fluorescent Silver-AIE Visualization of Neurons and Fibres in the Cleared BrainPreprint
Xie, S., Wong, A. Y. H., Kam, C., Hermanson, O., Ip, N. Y., Tang, B. Z., & Chen, S. (2021) · ChemRxiv · doi:10.26434/chemrxiv-2021-707mm
Large-scale dendritic spine extraction and analysis through petascale computingPreprint
Wildenberg, G. A., Li, H., Badalamente, G. S., Uram, T. D., Ferrier, N., & Kasthuri, N. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.29.454371
The Brain and Social Development in Childhood
Bigler, E. D. (2022) · Wiley eBooks · doi:10.1002/9781119679028.ch3
Targeting diverse operational regimes in recurrent spiking networksPreprint
Ekelmans, P., Kraynyukova, N., & Tchumatchenko, T. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.22.489005
Uncovering features of synapses in primary visual cortex through contrastive representation learningPreprint
Wilson, A. M., & Babadi, M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.07.495207
Generation of Axons and Dendrites
Ma, L. (2022) · Springer eBooks · doi:10.1007/978-1-4614-6434-1_188-1
Feng, K., Liu, J., Jiang, S., Chen, L., Zeng, X., & Shang, H. (2021) · International Conference on Networking Systems of AI (INSAI) · doi:10.1109/insai54028.2021.00038
Cognitive Mental Processes Realized by Cell Assemblies in the Cortex
Palm, G. (2022) · Springer eBooks · doi:10.1007/978-3-031-00311-0_5
Metadata
- Authors
- Motta, A., Berning, M., Boergens, K. M., Staffler, B., Beining, M., Loomba, S., Hennig, P., Wissler, H., & Helmstaedter, M.
- Affiliations
- Max Planck Institute for Brain Research (DE)
- Max Planck Society (DE)
- Corresponding author
- Motta, Alessandro
- Berning, Manuel
- Boergens, Kevin M.
- Staffler, Benedikt
- Wissler, Heiko
- ORCID
- Venue
- Science
- Published
- 2019-11-29
- Volume
- 366
- Issue
- 6469
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2982268628
- Cited by
- 333
- References
- 101
- Citations by year
- 2026: 23 · 2025: 37 · 2024: 62 · 2023: 55 · 2022: 60 · 2021: 52 · 2020: 40 · 2019: 2
- FWCI
- 18.45
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Science (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Max-Planck-Gesellschaft
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- connectome reconstruction, barrel cortex, layer 4, somatosensory cortex, mice, Hebbian synaptic plasticity, long-term potentiation, neural circuits, 3D electron microscopy
- MeSH terms
- Animals, Axons, Male, Mice, Inbred C57BL, Microscopy, Electron, Neurons, Somatosensory Cortex, Synapses, Neuropil, Imaging, Three-Dimensional, Mice, Connectome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review