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Multi-scale approaches for high-speed imaging and analysis of large neural populations
Johannes Friedrich, Weijian Yang, Daniel Soudry, Yu Mu, Misha B. Ahrens, Rafael Yuste, Darcy S. Peterka, Liam Paninski
doi: https://doi.org/10.1101/091132
Johannes Friedrich
1Department of Statistics, Grossman Center for the Statistics of Mind, and Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
2Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA, USA
Weijian Yang
3NeuroTechnology Center, Department of Biological Sciences, Columbia University, New York, NY, USA
Daniel Soudry
1Department of Statistics, Grossman Center for the Statistics of Mind, and Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
Yu Mu
2Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA, USA
Misha B. Ahrens
2Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA, USA
Rafael Yuste
3NeuroTechnology Center, Department of Biological Sciences, Columbia University, New York, NY, USA
4Department of Neuroscience and Kavli Institute of Brain Science, Columbia University, New York, NY, USA
Darcy S. Peterka
3NeuroTechnology Center, Department of Biological Sciences, Columbia University, New York, NY, USA
5Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
Liam Paninski
1Department of Statistics, Grossman Center for the Statistics of Mind, and Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
3NeuroTechnology Center, Department of Biological Sciences, Columbia University, New York, NY, USA
4Department of Neuroscience and Kavli Institute of Brain Science, Columbia University, New York, NY, USA
5Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
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Posted December 02, 2016.
Multi-scale approaches for high-speed imaging and analysis of large neural populations
Johannes Friedrich, Weijian Yang, Daniel Soudry, Yu Mu, Misha B. Ahrens, Rafael Yuste, Darcy S. Peterka, Liam Paninski
bioRxiv 091132; doi: https://doi.org/10.1101/091132
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