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pro vyhledávání: '"Plaza, Stephen M"'
We propose a method to facilitate exploration and analysis of new large data sets. In particular, we give an unsupervised deep learning approach to learning a latent representation that captures semantic similarity in the data set. The core idea is t
Externí odkaz:
http://arxiv.org/abs/2012.12175
Autor:
Shinomiya, Kazunori, Nern, Aljoscha, Meinertzhagen, Ian A., Plaza, Stephen M., Reiser, Michael B.
Publikováno v:
In Current Biology 22 August 2022 32(16):3529-3544
Extracting a connectome from an electron microscopy (EM) data set requires identification of neurons and determination of synapses between neurons. As manual extraction of this information is very time-consuming, there has been extensive research eff
Externí odkaz:
http://arxiv.org/abs/1604.03075
Autor:
Plaza, Stephen M., Berg, Stuart E.
The emerging field of connectomics aims to unlock the mysteries of the brain by understanding the connectivity between neurons. To map this connectivity, we acquire thousands of electron microscopy (EM) images with nanometer-scale resolution. After a
Externí odkaz:
http://arxiv.org/abs/1604.00385
Autor:
Zhao, Ting, Takemura, Shin-ya, Huang, Gary B., Horne, Jane Anne, Katz, William T., Shinomiya, Kazunori, Scheffer, Louis K., Meinertzhagen, Ian A., Rivlin, Patricia K., Plaza, Stephen M.
The promise of extracting connectomes and performing useful analysis on large electron microscopy (EM) datasets has been an elusive dream for many years. Tracing in even the smallest portions of neuropil requires copious human annotation, the rate-li
Externí odkaz:
http://arxiv.org/abs/1508.06232
Autor:
Zhao, Ting, Plaza, Stephen M
Mapping the connectivity of neurons in the brain (i.e., connectomics) is a challenging problem due to both the number of connections in even the smallest organisms and the nanometer resolution required to resolve them. Because of this, previous conne
Externí odkaz:
http://arxiv.org/abs/1409.1892
Autor:
Plaza, Stephen M., Parag, Toufiq, Huang, Gary B., Olbris, Donald J., Saunders, Mathew A., Rivlin, Patricia K.
Reconstructing neuronal circuits at the level of synapses is a central problem in neuroscience and becoming a focus of the emerging field of connectomics. To date, electron microscopy (EM) is the most proven technique for identifying and quantifying
Externí odkaz:
http://arxiv.org/abs/1409.1801
Autor:
Plaza, Stephen M.
Identifying complex neural circuitry from electron microscopic (EM) images may help unlock the mysteries of the brain. However, identifying this circuitry requires time-consuming, manual tracing (proofreading) due to the size and intricacy of these i
Externí odkaz:
http://arxiv.org/abs/1409.1199
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