VolPy: Automated and scalable analysis pipelines for voltage imaging datasets.

Area of Science:

• Neuroscience
• Biophysics
• Computational Biology

Background:

• Voltage imaging offers high spatio-temporal resolution for neural activity studies.
• High data rates and low signal-to-noise ratios pose significant analysis challenges.

Purpose of the Study:

• To introduce VolPy, an automated and scalable pipeline for pre-processing voltage imaging data.
• To address bottlenecks in analyzing large-scale neural activity datasets.

Main Methods:

• Developed a highly parallelizable, modular, and extensible framework for data processing.
• Implemented motion correction, memory mapping, automated segmentation, denoising, and spike extraction.
• Created a corpus of 24 manually annotated datasets for training and validation.

Main Results:

• VolPy demonstrated state-of-the-art performance in spike extraction.
• The pipeline is highly scalable, handling large voltage imaging datasets efficiently.
• Benchmarking confirmed VolPy's accuracy against ground truth and existing methods.

Conclusions:

• VolPy provides an efficient solution for analyzing complex voltage imaging data.
• The pipeline facilitates advanced research into neural dynamics and network function.
• Automated processing with VolPy enhances the study of subthreshold activity and synchrony.