Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Adam S. Shai"'
Publikováno v:
eNeuro. 9(3)
The dendrites of cortical pyramidal neurons receive synaptic inputs from different pathways that are organized according to their laminar target. This architectural scheme provides cortical neurons with a spatial mechanism to separate information, wh
Autor:
Oscar F. Hernández, Mark J. Schnitzer, Masatoshi Inoue, Radosław Chrapkiewicz, Mark J. Wagner, Haruhiko Bito, Tong Zhang, Adam S. Shai, Hongkui Zeng, Cheng-Hsun Wu, Biafra Ahanonu, Jin Zhong Li, Yanping Zhang, Yiyang Gong
Publikováno v:
Nature Methods. 16:1119-1122
Two-photon microscopy is a mainstay technique for imaging in scattering media and normally provides frame-acquisition rates of ~10–30 Hz. To track high-speed phenomena, we created a two-photon microscope with 400 illumination beams that collectivel
Publikováno v:
PLoS Computational Biology, Vol 11, Iss 3, p e1004090 (2015)
L5 pyramidal neurons are the only neocortical cell type with dendrites reaching all six layers of cortex, casting them as one of the main integrators in the cortical column. What is the nature and mode of computation performed in mouse primary visual
Externí odkaz:
https://doaj.org/article/aa8190210f1c43dca3c536a909c76542
Ultrafast two-photon microscopy for high-speed brain imaging in awake mice (Conference Presentation)
Autor:
Yiyang Gong, Masatoshi Inoue, Oscar F. Hernández, Mark J. Wagner, Yanping Zhang, Radosław Chrapkiewicz, Mark J. Schnitzer, Cheng-Hsun Wu, Jin Zhong Li, Tong Zhang, Haruhiko Bito, Adam S. Shai
Publikováno v:
Unconventional Optical Imaging.
Autor:
Matthew E. Larkum, Adam S. Shai
Publikováno v:
eLife, Vol 6 (2017)
eLife
eLife
Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning alg
Autor:
Costas A. Anastassiou, Adam S. Shai
Publikováno v:
Research and Perspectives in Neurosciences ISBN: 9783319288017
For a century or so, the multidisciplinary nature of neuroscience has left the field fractured into distinct areas of research. In particular, the subjects of consciousness and perception present unique challenges in the attempt to build a unifying u
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5f101d313491f212af379414eb37f313
https://doi.org/10.1007/978-3-319-28802-4_9
https://doi.org/10.1007/978-3-319-28802-4_9
Publikováno v:
PLoS Computational Biology
PLoS Computational Biology, Vol 11, Iss 3, p e1004090 (2015)
PLoS Computational Biology, Vol 11, Iss 3, p e1004090 (2015)
L5 pyramidal neurons are the only neocortical cell type with dendrites reaching all six layers of cortex, casting them as one of the main integrators in the cortical column. What is the nature and mode of computation performed in mouse primary visual
Publikováno v:
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience, Vol 8 (2014)
Frontiers in Computational Neuroscience, Vol 8 (2014)
Apical and tuft dendrites of pyramidal neurons support regenerative electrical potentials, giving rise to long-lasting (approximately hundreds of milliseconds) and strong (~50 mV from rest) depolarizations. Such plateau events rely on clustered gluta
Autor:
Ole Paulsen, Matthew E. Larkum, Lucy M. Palmer, Harry L. Anderson, James E. Reeve, Adam S. Shai
Publikováno v:
Nature Neuroscience
Recent evidence in vitro suggests that the tuft dendrites of pyramidal neurons are capable of evoking local NMDA receptor-dependent electrogenesis, so-called NMDA spikes. However, it has so far proved difficult to demonstrate their existence in vivo.
Structural changes and stability of DNA nanoarchitectures including Y-shaped DNA (see picture), dendrimer-like DNA, and DNA hydrogels are investigated. The results demonstrate the feasibility and flexibility of FRET and NSET (Forster resonance/ nanom
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7f1bbb267aa2dd11b37ed89cf387cbb7
https://resolver.caltech.edu/CaltechAUTHORS:20100817-091631784
https://resolver.caltech.edu/CaltechAUTHORS:20100817-091631784