Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Michael W. Reimann"'
Autor:
András Ecker, Daniela Egas Santander, Sirio Bolaños-Puchet, James B. Isbister, Michael W. Reimann
Publikováno v:
PLoS Computational Biology, Vol 20, Iss 3 (2024)
Externí odkaz:
https://doaj.org/article/c6699259f0d64a69a8c8715626784eda
Autor:
Taylor H. Newton, Michael W. Reimann, Marwan Abdellah, Grigori Chevtchenko, Eilif B. Muller, Henry Markram
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-13 (2021)
Voltage-sensitive dye imaging (VSDI) is a powerful technique for measuring membrane potential dynamics of neurons but the effective resolution is limited. Here, the authors developed an in silico model of VSDI to probe activity in a biologically deta
Externí odkaz:
https://doaj.org/article/fb302b6574b2423eac71eada12147df1
Autor:
Michael W. Reimann, Henri Riihimäki, Jason P. Smith, Jānis Lazovskis, Christoph Pokorny, Ran Levi
Publikováno v:
PLoS ONE, Vol 17, Iss 1 (2022)
In motor-related brain regions, movement intention has been successfully decoded from in-vivo spike train by isolating a lower-dimension manifold that the high-dimensional spiking activity is constrained to. The mechanism enforcing this constraint re
Externí odkaz:
https://doaj.org/article/2456a1d2ec72485fbbaf10c542b04d0f
Publikováno v:
Nature Communications, Vol 10, Iss 1, Pp 1-16 (2019)
A combination of large-scale connectomics with cellular and synapse data to generate a first draft statistical model of the neuron-to-neuron micro-connectome of a whole mouse neocortex. This micro-connectome recreates biological trends of targeting o
Externí odkaz:
https://doaj.org/article/77cbc9e4aca94597850b54dc7795b33b
Publikováno v:
Nature Communications, Vol 10, Iss 1, Pp 1-15 (2019)
Whether cortical neurons can fire reliable spikes amid cellular noise and chaotic network dynamics remains debated. Here the authors simulate a detailed neocortical microcircuit model and show that noisy and chaotic cortical network dynamics are comp
Externí odkaz:
https://doaj.org/article/ec4c222da2d84cdd94e170df118ed925
Autor:
Michael W. Reimann, Max Nolte, Martina Scolamiero, Katharine Turner, Rodrigo Perin, Giuseppe Chindemi, Paweł Dłotko, Ran Levi, Kathryn Hess, Henry Markram
Publikováno v:
Frontiers in Computational Neuroscience, Vol 11 (2017)
The lack of a formal link between neural network structure and its emergent function has hampered our understanding of how the brain processes information. We have now come closer to describing such a link by taking the direction of synaptic transmis
Externí odkaz:
https://doaj.org/article/882bfe7ae3dc4835be76de7d32540de4
Community-based Reconstruction and Simulation of a Full-scale Model of Region CA1 of Rat Hippocampus
Autor:
Armando Romani, Alberto Antonietti, Davide Bella, Julian Budd, Elisabetta Giacalone, Kerem Kurban, Sára Sáray, Marwan Abdellah, Alexis Arnaudon, Elvis Boci, Cristina Colangelo, Jean-Denis Courcol, Thomas Delemontex, András Ecker, Joanne Falck, Cyrille Favreau, Michael Gevaert, Juan B. Hernando, Joni Herttuainen, Genrich Ivaska, Lida Kanari, Anna-Kristin Kaufmann, James Gonzalo King, Pramod Kumbhar, Sigrun Lange, Huanxiang Lu, Carmen Alina Lupascu, Rosanna Migliore, Fabien Petitjean, Judit Planas, Pranav Rai, Srikanth Ramaswamy, Michael W. Reimann, Juan Luis Riquelme, Nadir Román Guerrero, Ying Shi, Vishal Sood, Mohameth François Sy, Werner Van Geit, Liesbeth Vanherpe, Tamás F. Freund, Audrey Mercer, Eilif Muller, Felix Schürmann, Alex M. Thomson, Michele Migliore, Szabolcs Káli, Henry Markram
The CA1 region of the hippocampus is one of the most studied regions of the rodent brain, thought to play an important role in cognitive functions such as memory and spatial navigation. Despite a wealth of experimental data on its structure and funct
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dcd3800f48705dad49baff5ad612097b
https://doi.org/10.1101/2023.05.17.541167
https://doi.org/10.1101/2023.05.17.541167
Autor:
András Ecker, Daniela Egas Santander, Sirio Bolaños-Puchet, James B. Isbister, Michael W. Reimann
Recent developments in experimental techniques have enabled simultaneous recordings from thousands of neurons, enabling the study of functional cell assemblies. However, determining the patterns of synaptic connectivity giving rise to these assemblie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4de58eccf786b9bc95433261e8ca90e5
https://doi.org/10.1101/2023.02.24.529863
https://doi.org/10.1101/2023.02.24.529863
The brain is composed of several anatomically clearly separated structures. This parcellation is often extended into the isocortex, based on anatomical, physiological, or functional differences. Here, we derive a parcellation scheme based purely on t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0e29ce75163472a2755dde01ddb8e6e8
https://doi.org/10.1101/2022.08.30.505842
https://doi.org/10.1101/2022.08.30.505842
Autor:
Michael W. Reimann, Sirio Bolaños-Puchet, Jean-Denis Courcol, Daniela Egas Santander, Alexis Arnaudon, Benoît Coste, Thomas Delemontex, Adrien Devresse, Hugo Dictus, Alexander Dietz, András Ecker, Cyrille Favreau, Gianluca Ficarelli, Mike Gevaert, Juan B. Hernando, Joni Herttuainen, James B. Isbister, Lida Kanari, Daniel Keller, James King, Pramod Kumbhar, Samuel Lapere, Jānis Lazovskis, Huanxiang Lu, Nicolas Ninin, Fernando Pereira, Judit Planas, Christoph Pokorny, Juan Luis Riquelme, Armando Romani, Ying Shi, Jason P. Smith, Vishal Sood, Mohit Srivastava, Werner Van Geit, Liesbeth Vanherpe, Matthias Wolf, Ran Levi, Kathryn Hess, Felix Schürmann, Eilif B. Muller, Srikanth Ramaswamy, Henry Markram
The function of the neocortex is fundamentally determined by its repeating microcircuit motif, but also by its rich, hierarchical, interregional structure with a highly specific laminar architecture. The last decade has seen the emergence of extensiv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fe627eb2c78c5eb420f9c4c1179d84b0
https://doi.org/10.1101/2022.08.11.503144
https://doi.org/10.1101/2022.08.11.503144