SEE: a tool for the visualization and analysis of rodent exploratory behavior

Area of Science:

• Ethology and Animal Behavior Analysis
• Computational Neuroscience
• Bioinformatics and Data Visualization

Background:

• Exploratory behavior in animals is complex, requiring advanced tools for pattern identification and hypothesis generation.
• Existing methods may not adequately capture the nuances of spatial navigation and activity dynamics.
• There is a need for integrated software to visualize and quantify complex behavioral data.

Purpose of the Study:

• To introduce SEE, a Mathematica-based software designed for the exploration and analysis of animal exploratory behavior.
• To provide tools for visualizing animal paths, quantifying activity dynamics, and identifying behavioral patterns.
• To facilitate the generation of new hypotheses regarding the structure of exploratory behavior.

Main Methods:

• Utilizing time-series data of animal coordinates in space to compute speeds and movement dynamics.
• Implementing algorithms for decomposing paths into modes of motion, calculating speeds, spatial spread, and mode proportions.
• Visualizing stopping episodes, home base excursions, phase space dynamics, and place-based connectivity.

Main Results:

• SEE enables visualization of animal paths and activity dynamics, including preferred locations and home base identification.
• The software quantifies modes of motion, typical speeds, spatial spread, and spatio-temporal diversity of stopping episodes.
• Analysis of 'excursions' and phase space visualizations provide insights into behavioral structure and place connections.

Conclusions:

• SEE offers a comprehensive platform for visualizing and analyzing complex animal exploratory behavior.
• The software aids in quantifying behavioral patterns and generating data-driven hypotheses.
• SEE's applicability extends across diverse experimental setups, species, and tracking methodologies.