Rtrack

Rtrack analyzes spatial exploration data from behavioral experiments, classifying search strategies and computing path metrics to support studies of animal navigation and cognition.

Key Features:

• Parameter-free classifier: Implements a parameter-free machine learning model for rapid and reproducible classification of spatial search strategies.
• Strategy assignment: Assigns one of nine distinct behavioral strategies to each path.
• Path metrics calculation: Computes quantitative metrics from path coordinates to quantify movement and search patterns.
• Data import and visualization: Provides capabilities for importing experimental paths and visualizing trajectories and classification results.
• Standard export format: Supports a proposed standardized export format to enable cross-platform data sharing and reproducibility.

Scientific Applications:

• Morris water maze analysis: Analyzes trajectories and classifies search strategies from Morris water maze behavioral experiments.
• Animal navigation and cognition studies: Quantifies navigational tactics and movement efficiency to support research on spatial learning and cognitive mapping.
• Comparative behavioral analysis: Facilitates cross-platform comparison and reproducible sharing of behavioral path data via the standard export format.

Methodology:

Uses a parameter-free machine learning classifier to assign one of nine behavioral strategies to individual paths and computes quantitative metrics from path coordinates, with support for exporting data in a standardized format.