Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Philipp J Schubert"'
Publikováno v:
Nature Communications, Vol 10, Iss 1, Pp 1-12 (2019)
Volume electron microscopy data of brain tissue can tell us much about neural circuits, but increasingly large data sets demand automation of analysis. Here, the authors introduce cellular morphology neural networks and successfully automate a range
Externí odkaz:
https://doaj.org/article/38c48d21d4914eeab082cca5e0f1ad3b
Autor:
Fabian Svara, Dominique Förster, Fumi Kubo, Michał Januszewski, Marco dal Maschio, Philipp J. Schubert, Jörgen Kornfeld, Adrian A. Wanner, Eva Laurell, Winfried Denk, Herwig Baier
Publikováno v:
Nature Methods
This Resource presents a serial block-face EM dataset of the whole larval zebrafish brain, including automated segmentation of neurons, detection of synapses and reconstruction of circuitry for visual motion processing. Dense reconstruction of synapt
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e242bd6f13c99321ac80e3c690ee79c4
https://doi.org/10.1038/s41592-022-01621-0
https://doi.org/10.1038/s41592-022-01621-0
Autor:
Philipp J. Schubert, Sven Dorkenwald, Michał Januszewski, Jonathan Klimesch, Fabian Svara, Andrei Mancu, Hashir Ahmad, Michale S. Fee, Viren Jain, Joergen Kornfeld
Publikováno v:
Nature Methods
The ability to acquire ever larger datasets of brain tissue using volume electron microscopy leads to an increasing demand for the automated extraction of connectomic information. We introduce SyConn2, an open-source connectome analysis toolkit, whic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::076a15b19620e602fed0a29a13bf4d37
https://hdl.handle.net/21.11116/0000-000C-0072-321.11116/0000-000C-0074-1
https://hdl.handle.net/21.11116/0000-000C-0072-321.11116/0000-000C-0074-1
Autor:
Jörgen Kornfeld, Viren Jain, Winfried Denk, Michale S. Fee, Philipp J Schubert, Michał Januszewski
Learning turns experience into better decisions. A key problem in learning is credit assignment—knowing how to change parameters, such as synaptic weights deep within a neural network, in order to improve behavioral performance. Artificial intellig
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c4ce59feba0f30b75773b9247dea0145
https://doi.org/10.1101/2020.02.18.954354
https://doi.org/10.1101/2020.02.18.954354
Autor:
Shawn Mikula, Fabian Svara, Sven Dorkenwald, Joergen Kornfeld, Gregor Urban, Philipp J Schubert, Marius F Killinger
Publikováno v:
Nature Methods. 14:435-442
Teravoxel volume electron microscopy data sets from neural tissue can now be acquired in weeks, but data analysis requires years of manual labor. We developed the SyConn framework, which uses deep convolutional neural networks and random forest class
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::15d8891bc118765ceca4ec0d41c64b5f
https://doi.org/10.1038/nmeth.4206
https://doi.org/10.1038/nmeth.4206
Publikováno v:
Nature Communications
Nature Communications, Vol 10, Iss 1, Pp 1-12 (2019)
Nature Communications, Vol 10, Iss 1, Pp 1-12 (2019)
Reconstruction and annotation of volume electron microscopy data sets of brain tissue is challenging but can reveal invaluable information about neuronal circuits. Significant progress has recently been made in automated neuron reconstruction as well
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::016b0d6982a3bf4513a5f60c453a9b93
https://doi.org/10.1038/s41467-019-10836-3
https://doi.org/10.1038/s41467-019-10836-3