v1.0.1

CaliAli 1.0.1 Release Notes

• Introduced a new app for determining optimal blood vessel (BV) size.
• Removed unnecessary or deprecated functions.
• Several improvements to the documentation.

Full Changelog: v1.0...v1.0.1

v1.0

CaliAli Stable Version 1.0 Release Notes

We are excited to announce the first stable version of CaliAli, featuring significant enhancements and improvements. Below are the key changes in this release:

Changes:

• Full Online Documentation: Access comprehensive documentation to guide you through using CaliAli efficiently.

• Optimized Computational Performance: CaliAli is now optimized for low memory requirements, ensuring smoother and more efficient processing.

• Improved Session Analysis: Analyze individual sessions and perform multisession concatenation seamlessly.

• Enhanced Blood Vessel (BV) Extraction: Minimized vignetting artifacts for improved accuracy in BV extraction.

• New BV Stability Metric: Evaluate tracking performance with a new stability metric integrated into BV extraction.

• Bug Fixes in GUIs: Addressed several bugs in the graphical user interfaces (GUIs) for improved usability.

• Enhanced Inter-Session Alignment: Further improvements to the inter-session alignment module for precise video session alignment.

For a complete list of changes, please refer to the Full Changelog on GitHub.

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This release of CaliAli marks a significant milestone, offering enhanced functionality and improved performance for extracting neural signals from multi-session calcium imaging data.

CaliAli v1-beta

CaliAli Beta Release

The first beta release of CaliAli is now available, offering advanced capabilities for extracting neural signals from one-photon calcium imaging data in free-moving conditions.

For details, refer to the BioRxiv preprint.

Explore CaliAli to analyze calcium imaging data with accuracy and efficiency, shaping the future of neural signal extraction in neuroscience research.