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PursuitNet: A deep learning model for predicting competitive pursuit-like behavior in mice
Qiaoqian Wei1, Jincheng Wang2, Guifeng Zhai1
1Guangxi Key Laboratory of Special Biomedicine and Advanced Institute for Brain and Intelligence, School of Medicine, Guangxi University, Nanning 530004, China; Department of Neurobiology, College of Basic Medicine, Army Medical University, Chongqing 400038, China.
Researchers developed PursuitNet, a deep learning model for predator-prey simulations. This AI framework accurately predicts complex pursuit-escape behaviors, advancing robotics and understanding animal intelligence.
Area of Science:
- Artificial Intelligence
- Computational Neuroscience
- Robotics
Background:
- Predator-prey interactions showcase evolved adaptive intelligence, but replicating these complex dynamics in artificial systems is difficult.
- Existing trajectory datasets often lack the real-time mutual adaptation seen in pursuit-escape scenarios.
Purpose of the Study:
- To introduce PursuitNet, a novel deep learning framework for modeling competitive, real-time pursuit-escape dynamics.
- To leverage the unique Pursuit-Escape Confrontation (PEC) dataset for training and evaluating the model.
- To enhance the design of interactive robotics and autonomous systems by understanding adaptive decision-making.
Main Methods:
- Developed PursuitNet, a lightweight deep learning architecture integrating Graph Convolutional Networks for spatial-temporal dynamics and Temporal Convolutional Networks for velocity/acceleration fusion.
- Utilized the Pursuit-Escape Confrontation (PEC) dataset, capturing detailed mouse-and-robotic-bait interactions with abrupt maneuvers.
- Conducted empirical evaluations and ablation experiments to validate the model's performance and the importance of integrated features.
Main Results:
- PursuitNet significantly outperformed standard models like Social GAN and TUTR, demonstrating lower displacement errors on the PEC dataset.
- Ablation studies confirmed the critical role of combined spatial and temporal features in predicting erratic movements and speed changes.
- Simulations generated pursuit events closely mirroring real-world mouse-and-bait interactions.
Conclusions:
- The biologically inspired PursuitNet framework accurately predicts complex pursuit-escape trajectories, offering insights into innate decision-making processes.
- This approach deepens the understanding of predator-prey dynamics and provides a foundation for advanced robotics and autonomous systems.
- The model's ability to capture rapid trajectory shifts highlights its potential for real-world applications requiring adaptive interaction.
