Connectomic reconstruction of the inner plexiform layer in the mouse retina

By Moritz Helmstaedter, Kevin L. Briggman, Srinivas C. Turaga, Viren Jain, H. Sebastian Seung, Winfried Denk. Nature, 2013

981 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/322384d2e32e306bcbbbbf372ca524ddb7fc78f6

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In Cellular and Molecular Neuroscience

In Molecular Biology

Citations in the corpus

Review of Artificial Intelligence Adversarial Attack and Defense Technologies (2019, methodology): “Due to high performance, high availability and high intelligence, artificial intelligence technologies have been applied in image classification, object detection, voice control, machine translation and more advanced fields, such as drug composition analysis [1], brain circuit reconstruction [2], pa”
Advances in adversarial attacks and defenses in computer vision: A survey (2021, background): “From analysing mutations in DNA [2] to reconstruction of brain circuits [3] and exploring cell data [4]; deep learning methods are currently advancing our knowledge formany cutting-edge scientific problems.”
Introduction to MAchine Learning & Knowledge Extraction (MAKE) (2017, background): “, in computer vision [47], natural language understanding [48], connectomics (study of brain circuits) [49], bioinformatics [50], health informatics [51–53], or in physics [54], to point only to a few examples.”
Dense connectomic reconstruction in layer 4 of the somatosensory cortex (2018, background): “2, I and J), ~20 times faster than the previous dense reconstruction in the mammalian retina (18), and ~25 times faster than the RESEARCH”
DLFuzz: differential fuzzing testing of deep learning systems (2018, background): “In the past few years, deep learning (DL) systems have demonstrated its competitiveness on a wide range of applications, such as image classification [5, 8], natural language processing [15] and even reconstruction of brain circuits [6].”
Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records (2016, neutral): “Deep learning was recently applied to medicine and genomics to reconstruct brain circuits 35 and to predict the activity of potential drug molecules 36 , the effects of mutations in non-coding DNA on gene expressions 37,38 , and the sequence specificities of DNA and RNA-binding proteins 39 .”
DeepEM3D: approaching human‐level performance on 3D anisotropic EM image segmentation (2017, background): “A breakthrough for automatic dense EM reconstruction is in urgent need, and better machine learning algorithms are considered the most likely approach toward this goal (Helmstaedter et al., 2013; Jain et al., 2010).”
Multi-scale brain networks (2016, positive): “For example, optical imaging has delineated cellular-level networks of mouse retina (Helmstaedter et al., 2013; Lee et al., 2016) as well as of model organisms like the nematode, C.”
Cellular-resolution connectomics: challenges of dense neural circuit reconstruction (2013, methodology): “The most recent mammalian dense-circuit reconstruction in mouse retina 36 took ~30,000 hours, by hundreds of part-time undergraduate annotators.”
Dimensionality in recurrent spiking networks: Global trends in activity and local origins in connectivity (2018, background): “A fundamental step toward understanding neural circuits is relating the structure of 2 their dynamics to the structure of their connectivity [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14].”
Adversarial Attacks and Defences: A Survey (2018, background): “e traditional fields of image classification, speech recognition, language translation along with more advanced areas like analysing potential of drug molecules [37], reconstruction of brain circuits [49], analysing particle accelerator data [71] [39], effects of mutations in DNA [32]. Deep learning”
Structured Connectivity in Cerebellar Inhibitory Networks (2014, background): “Many brain areas reveal signs of structured connectivity, in particular, in relation to their functional representation (Briggman et al., 2011; Helmstaedter et al., 2013; Ko et al., 2011; Maisak et al., 2013; Takemura et al., 2013).”
Deep Convolutional Neural Networks for Multi-Modality Isointense Infant Brain Image Segmentation (2015, methodology): “In Jain et al. (2007), Jain and Seung (2009), Turaga et al. (2010), and Helmstaedter et al. (2013), CNNs were applied to restore and segment the volumetric electron microscopy images.”