Automatic deep learning-driven label-free image-guided patch clamp system
Patch clamp recording of neurons is slow and labor-intensive. Here the authors present a method for automated deep learning driven label-free image guided patch clamp physiology to perform measurements on hundreds of human and rodent neurons.
Guardado en:
Autores principales: | Krisztian Koos, Gáspár Oláh, Tamas Balassa, Norbert Mihut, Márton Rózsa, Attila Ozsvár, Ervin Tasnadi, Pál Barzó, Nóra Faragó, László Puskás, Gábor Molnár, József Molnár, Gábor Tamás, Peter Horvath |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/752426adf88042c3b1fc84b42821906d |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Complex events initiated by individual spikes in the human cerebral cortex.
por: Gábor Molnár, et al.
Publicado: (2008) -
Intelligent image-based in situ single-cell isolation
por: Csilla Brasko, et al.
Publicado: (2018) -
Patch clamp-assisted single neuron lipidomics
por: Collin B. Merrill, et al.
Publicado: (2017) -
Computer modeling of whole-cell voltage-clamp analyses to delineate guidelines for good practice of manual and automated patch-clamp
por: Jérôme Montnach, et al.
Publicado: (2021) -
Combination of High-density Microelectrode Array and Patch Clamp Recordings to Enable Studies of Multisynaptic Integration
por: David Jäckel, et al.
Publicado: (2017)