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Review of data processing of functional optical microscopy for neuroscience.
Benisty, Hadas; Song, Alexander; Mishne, Gal; Charles, Adam S.
Afiliação
  • Benisty H; Yale Neuroscience, New Haven, Connecticut, United States.
  • Song A; Max Planck Institute for Intelligent Systems, Stuttgart, Germany.
  • Mishne G; UC San Diego, Haliciglu Data Science Institute, Department of Electrical and Computer Engineering and the Neurosciences Graduate Program, La Jolla, California, United States.
  • Charles AS; Johns Hopkins University, Kavli Neuroscience Discovery Institute, Center for Imaging Science, Department of Biomedical Engineering, Department of Neuroscience, and Mathematical Institute for Data Science, Baltimore, Maryland, United States.
Neurophotonics ; 9(4): 041402, 2022 Oct.
Article em En | MEDLINE | ID: mdl-35937186
Functional optical imaging in neuroscience is rapidly growing with the development of optical systems and fluorescence indicators. To realize the potential of these massive spatiotemporal datasets for relating neuronal activity to behavior and stimuli and uncovering local circuits in the brain, accurate automated processing is increasingly essential. We cover recent computational developments in the full data processing pipeline of functional optical microscopy for neuroscience data and discuss ongoing and emerging challenges.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Neurophotonics Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Neurophotonics Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos
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