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      Bayesian nonparametric analysis for the detection of spikes in noisy calcium imaging data

      1 , 2 , 3 , 3
      Biometrics
      Wiley

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          Comparing partitions

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            Objective Criteria for the Evaluation of Clustering Methods

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              High-performance calcium sensors for imaging activity in neuronal populations and microcompartments

              Calcium imaging with genetically encoded calcium indicators (GECIs) is routinely used to measure neural activity in intact nervous systems. GECIs are frequently used in one of two different modes: to track activity in large populations of neuronal cell bodies, or to follow dynamics in subcellular compartments such as axons, dendrites and individual synaptic compartments. Despite major advances, calcium imaging is still limited by the biophysical properties of existing GECIs, including affinity, signal-to-noise ratio, rise and decay kinetics and dynamic range. Using structure-guided mutagenesis and neuron-based screening, we optimized the green fluorescent protein-based GECI GCaMP6 for different modes of in vivo imaging. The resulting jGCaMP7 sensors provide improved detection of individual spikes (jGCaMP7s,f), imaging in neurites and neuropil (jGCaMP7b), and may allow tracking larger populations of neurons using two-photon (jGCaMP7s,f) or wide-field (jGCaMP7c) imaging.
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                Author and article information

                Contributors
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                Journal
                Biometrics
                Biometrics
                Wiley
                0006-341X
                1541-0420
                March 28 2022
                Affiliations
                [1 ]Department of Economics Management and Statistics University of Milano‐Bicocca Milan Italy
                [2 ]Department of Statistical Sciences University of Padova Padova Italy
                [3 ]Department of Statistics University of California Irvine Irvine California USA
                Article
                10.1111/biom.13626
                4560a192-6aa5-4122-80e9-506416e94a48
                © 2022

                http://creativecommons.org/licenses/by/4.0/

                http://doi.wiley.com/10.1002/tdm_license_1.1

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