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pro vyhledávání: '"Turaga, Srinivas C."'
Autor:
Deb, Diptodip, Jiao, Zhenfei, Sims, Ruth, Chen, Alex B., Broxton, Michael, Ahrens, Misha B., Podgorski, Kaspar, Turaga, Srinivas C.
Differentiable simulations of optical systems can be combined with deep learning-based reconstruction networks to enable high performance computational imaging via end-to-end (E2E) optimization of both the optical encoder and the deep decoder. This h
Externí odkaz:
http://arxiv.org/abs/2104.10611
Autor:
Mi, Lu, Wang, Hao, Meirovitch, Yaron, Schalek, Richard, Turaga, Srinivas C., Lichtman, Jeff W., Samuel, Aravinthan D. T., Shavit, Nir
Single-beam scanning electron microscopes (SEM) are widely used to acquire massive data sets for biomedical study, material analysis, and fabrication inspection. Datasets are typically acquired with uniform acquisition: applying the electron beam wit
Externí odkaz:
http://arxiv.org/abs/2101.02746
Akademický článek
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Autor:
Speiser, Artur, Müller, Lucas-Raphael, Matti, Ulf, Obara, Christopher J., Legant, Wesley R., Ries, Jonas, Macke, Jakob H., Turaga, Srinivas C.
Single-molecule localization fluorescence microscopy constructs super-resolution images by sequential imaging and computational localization of sparsely activated fluorophores. Accurate and efficient fluorophore localization algorithms are key to the
Externí odkaz:
http://arxiv.org/abs/1907.00770
What can we learn from a connectome? We constructed a simplified model of the first two stages of the fly visual system, the lamina and medulla. The resulting hexagonal lattice convolutional network was trained using backpropagation through time to p
Externí odkaz:
http://arxiv.org/abs/1806.04793
Each training step for a variational autoencoder (VAE) requires us to sample from the approximate posterior, so we usually choose simple (e.g. factorised) approximate posteriors in which sampling is an efficient computation that fully exploits GPU pa
Externí odkaz:
http://arxiv.org/abs/1805.10958
Autor:
Speiser, Artur, Yan, Jinyao, Archer, Evan, Buesing, Lars, Turaga, Srinivas C., Macke, Jakob H.
Calcium imaging permits optical measurement of neural activity. Since intracellular calcium concentration is an indirect measurement of neural activity, computational tools are necessary to infer the true underlying spiking activity from fluorescence
Externí odkaz:
http://arxiv.org/abs/1711.01846
A powerful approach for understanding neural population dynamics is to extract low-dimensional trajectories from population recordings using dimensionality reduction methods. Current approaches for dimensionality reduction on neural data are limited
Externí odkaz:
http://arxiv.org/abs/1711.01847
Autor:
Funke, Jan, Tschopp, Fabian David, Grisaitis, William, Sheridan, Arlo, Singh, Chandan, Saalfeld, Stephan, Turaga, Srinivas C.
We present a method combining affinity prediction with region agglomeration, which improves significantly upon the state of the art of neuron segmentation from electron microscopy (EM) in accuracy and scalability. Our method consists of a 3D U-NET, t
Externí odkaz:
http://arxiv.org/abs/1709.02974
Autor:
Parag, Toufiq, Tschopp, Fabian, Grisaitis, William, Turaga, Srinivas C, Zhang, Xuewen, Matejek, Brian, Kamentsky, Lee, Lichtman, Jeff W., Pfister, Hanspeter
The field of connectomics has recently produced neuron wiring diagrams from relatively large brain regions from multiple animals. Most of these neural reconstructions were computed from isotropic (e.g., FIBSEM) or near isotropic (e.g., SBEM) data. In
Externí odkaz:
http://arxiv.org/abs/1707.08935