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Syntalos: a software for precise synchronization of simultaneous multi-modal data acquisition and closed-loop

Matthias Klumpp¹, Lee Embray¹, Filippo Heimburg¹

¹Institute of Physiology and Pathophysiology, Medical Faculty, Heidelberg University, Heidelberg, Germany.

Nature Communications

|January 15, 2025

Summary

This summary is machine-generated.

Syntalos is an open-source software for synchronized multi-modal data acquisition and real-time interventions. It ensures precise timing across diverse experimental devices, facilitating data sharing and complex research.

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Area of Science:

Neuroscience
Computational Biology
Biotechnology

Background:

Complex experimental protocols demand synchronized multi-modal data acquisition and real-time interventions.
Existing solutions are often setup-specific, lacking flexibility and interoperability.

Purpose of the Study:

To present Syntalos, an open-source, integrated software solution for synchronized data acquisition and closed-loop interventions.
To enable flexible and interoperable solutions for complex experimental setups.

Main Methods:

Developed a Linux-based software solution for simultaneous data acquisition from multiple sources (electrophysiology, imaging).
Implemented real-time, closed-loop interventions with various actuators.
Utilized continuous statistical analysis for precise timestamp synchronization across devices.
Designed a structured data format for easy data pooling and sharing.

Main Results:

Syntalos successfully synchronizes data from arbitrary sources, including electrophysiology and live imaging.
The software allows for closed-loop, real-time interventions.
Timestamp correction ensures precise data alignment.
New data sources can be integrated with minimal programming effort.
Preliminary neuroscientific experiments in mice demonstrated successful performance and ease of use.

Conclusions:

Syntalos provides a flexible, interoperable solution for synchronized multi-modal recordings and closed-loop interventions.
The software facilitates complex experimental designs and data sharing across laboratories.
Its user-friendly structure supports diverse neuroscientific research applications.