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Related Experiment Video

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Simultaneous Electrophysiological Recording and Calcium Imaging of Suprachiasmatic Nucleus Neurons
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Computational processing of neural recordings from calcium imaging data.

Carsen Stringer1, Marius Pachitariu1

  • 1HHMI Janelia Research Campus, Ashburn, VA 20147, USA.

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|December 12, 2018
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Calcium imaging offers a powerful alternative to electrophysiology for neuroscience research. This review details computational methods to improve calcium imaging data quality by estimating neuron spike times.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biophysics

Background:

  • Electrophysiology traditionally records neuron action potentials with millisecond precision.
  • Calcium imaging using fluorescent indicators is an emerging neuroscience technique.
  • Calcium imaging presents unique data processing challenges and interpretation confounds.

Purpose of the Study:

  • To review computational methods for processing calcium imaging data.
  • To address weaknesses in calcium imaging through computational approaches.
  • To improve the quality and reliability of calcium imaging data.

Main Methods:

  • Review of computational algorithms for converting raw calcium movies to neuron spike times.
  • Focus on methods enabling minimal human supervision.
  • Introduction of a novel metric for evaluating processing pipeline output.

Main Results:

  • Computational methods can convert calcium imaging data into estimates of single neuron spike times.
  • These methods aim to mitigate the inherent weaknesses of calcium imaging.
  • A new metric, inspired by electrophysiology's cluster isolation distance, is proposed for evaluation.

Conclusions:

  • Computational processing significantly enhances the utility of calcium imaging data.
  • Addressing data processing challenges improves the accuracy of neuron activity estimation.
  • The proposed metric aids in assessing the quality of spike time estimations from calcium imaging.