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Spike rate inference from mouse spinal cord calcium imaging data.

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  • 1Laboratory of Neural Circuit Dynamics, Brain Research Institute, University of Zurich, Switzerland.

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|January 20, 2025
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Summary

Spike inference algorithms for calcium imaging generalize well to spinal cord neurons. Re-training models with specific ground truth data further improved accuracy for neuronal activity analysis.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience

Background:

  • Calcium imaging is crucial for monitoring neuronal activity but provides indirect signals requiring spike inference algorithms.
  • Optimizing these algorithms with ground truth data is essential, yet their performance in new cell types and brain regions remains unclear.

Purpose of the Study:

  • To evaluate the generalization of established spike inference algorithms (CASCADE and OASIS) to spinal cord neurons.
  • To improve spike inference accuracy in the spinal cord dorsal horn by re-training models with novel ground truth data.

Main Methods:

  • Recorded ground truth electrophysiological and calcium imaging data from mouse spinal cord dorsal horn neurons (glutamatergic and GABAergic).
  • Applied supervised (CASCADE) and non-supervised (OASIS) deep learning algorithms for spike rate inference.
  • Re-trained CASCADE models using the newly acquired spinal cord ground truth data.

Main Results:

  • Both CASCADE and OASIS algorithms showed good generalization performance for spinal cord neurons, despite being developed for cortical neurons.
  • Re-training CASCADE models with spinal cord ground truth significantly enhanced spike inference accuracy.
  • Provided re-trained models for improved analysis of spinal cord calcium imaging data across various noise levels and frame rates.

Conclusions:

  • Spike inference algorithms can generalize across different neural regions, but region-specific optimization enhances performance.
  • This study establishes a foundation for interpreting calcium imaging data from the spinal cord dorsal horn.
  • Openly shared resources facilitate more accurate neuronal activity analysis in the spinal cord.