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Automatic deep learning driven label-free image guided patch clamp system for human and rodent in vitro slice physiology

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
doi: https://doi.org/10.1101/2020.05.05.078162
Krisztian Koos
1Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Szeged, Hungary
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Gáspár Oláh
2MTA-SZTE Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Hungary
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Tamas Balassa
1Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Szeged, Hungary
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Norbert Mihut
2MTA-SZTE Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Hungary
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Márton Rózsa
2MTA-SZTE Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Hungary
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Attila Ozsvár
2MTA-SZTE Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Hungary
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Ervin Tasnadi
1Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Szeged, Hungary
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Pál Barzó
3Department of Neurosurgery, University of Szeged, Szeged, Hungary
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Nóra Faragó
2MTA-SZTE Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Hungary
4Laboratory of Functional Genomics, Institute of Genetics, Biological Research Centre, Szeged, Hungary
5Avidin Ltd, Szeged, Hungary
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László Puskás
4Laboratory of Functional Genomics, Institute of Genetics, Biological Research Centre, Szeged, Hungary
5Avidin Ltd, Szeged, Hungary
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Gábor Molnár
2MTA-SZTE Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Hungary
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József Molnár
1Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Szeged, Hungary
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Gábor Tamás
2MTA-SZTE Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Hungary
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Peter Horvath
1Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Szeged, Hungary
6Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
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  • For correspondence: horvath.peter@brc.hu
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  • Supplementary Information: Cell Detection System[supplements/078162_file02.pdf]
  • Supplementary Information: Cell Tracking System[supplements/078162_file03.pdf]
  • Supplementary Information: Pipette Detection System[supplements/078162_file04.pdf]
  • Supplementary Information: Pressure Regulator[supplements/078162_file05.pdf]
  • Supplementary Information: Software Usage[supplements/078162_file06.pdf]
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Posted May 05, 2020.
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Automatic deep learning driven label-free image guided patch clamp system for human and rodent in vitro slice physiology
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
bioRxiv 2020.05.05.078162; doi: https://doi.org/10.1101/2020.05.05.078162
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Automatic deep learning driven label-free image guided patch clamp system for human and rodent in vitro slice physiology
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
bioRxiv 2020.05.05.078162; doi: https://doi.org/10.1101/2020.05.05.078162

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