a-real-time-interactive-system-for-studying-confrontational-pursuit

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Predatory pursuit behavior involves a series of important physiological processes, such as locomotion, learning, and decision-making that are critical to ...

Army Medical University

Escape is one of the most essential behaviors for an animal's survival because it could be a matter of life and death. Much of our current understanding of the neural mechanisms underlying escape is derived from the looming paradigm, which mimics a diving aerial predator. Yet, the idea of the looming paradigm does not account for all types of threats like lions hunting antelopes or cats stalking mice. Escape responses to such terrestrial threats may require different strategies and neural mechanisms.

A method for studying escape behavior to terrestrial threats in rodents.

Confrontations between predator and prey, driven by the innate survival instincts in both predator and prey, constitute the most significant form of competition in evolution. Yet, understanding how survival skills can benefit from such confrontations remains limited, despite its critical importance for animal survival. We have developed an interactive platform to investigate confrontations between a hungry mouse and an escaping bait. This robotic bait is controlled magnetically through a closed-loop system to continually evade the approaching mouse. Meanwhile, the mouse must capture the escaping bait to receive a food reward. Through analysis of angles, speeds and other kinematic parameters of both the mouse and the bait, we observed that confrontation experiences can notably enhance mice performance. Compared with novice mice, veteran mice enhanced predation efficiency primarily by optimizing the pursuit phase, significantly reducing time costs, mainly by minimizing pauses in movement. Additionally, experience strengthened the navigation strategies used by mice to better track evading bait. Finally, we validated the impact of empirically induced changes in speed distribution and pursuit methods on predation efficiency through modeling of the pursuit phase. In conclusion, this study reveals that confrontation experience could improve pursuit strategy in mice by altering the speed control and pursuit method, providing new insights into these crucial behavioral interactions in nature.

Confrontation experience against escaping bait improves pursuit strategy in mice.

Sound-evoked wakefulness from sleep is crucial in daily life, yet its neural mechanisms remain poorly understood. It is found that CaMKIIα+ neurons in the temporal association cortex (TeA) of mice are not essential for natural awakening from sleep. However, optogenetic activation of these neurons reliably induces wakefulness from non-rapid eye movement (NREM) sleep but not from rapid eye movement (REM) sleep. In vivo electrophysiological and calcium recordings further demonstrated that TeA neurons are monotonically tuned to sound intensity but not frequency. More importantly, it is found that the activity of CaMKIIα+ neurons in TeA can gate sound-evoked arousal from NREM sleep, which is further confirmed by optogenetic manipulations. Further investigation reveals that the baseline excitability of TeA CaMKIIα+ neurons and the delta oscillations in the electroencephalogram are particularly important in regulating the evoked activity of TeA neurons. Anatomical and functional screening of downstream targets of TeA reveals that excitatory projections from TeA glutamatergic neurons to glutamatergic neurons in the basolateral/lateral amygdala are critical for modulating sound-evoked arousal from NREM sleep. These findings uncover a top-down regulatory circuit that selectively governs sound-evoked arousal from NREM sleep, with the TeA functioning as a key connecting cortex to subcortical regions.

Temporal Association Cortex Gates Sound-Evoked Arousal from NREM Sleep.

Predator-prey interactions exemplify adaptive intelligence refined by evolution, yet replicating these behaviors in artificial systems remains challenging. Here, we introduce PursuitNet, a deep learning framework specifically designed to model the competitive, real-time dynamics of pursuit-escape scenarios. Our approach is anchored by the Pursuit-Escape Confrontation (PEC) dataset, which records laboratory mice chasing a magnetically controlled robotic bait programmed to evade capture. Unlike conventional trajectory datasets, PEC emphasizes abrupt speed changes, evasive maneuvers, and continuous mutual adaptation. PursuitNet integrates a lightweight architecture that explicitly models dynamic interactions and spatial relationships using Graph Convolutional Networks, and fuses velocity and acceleration data to predict change using Temporal Convolutional Networks. In empirical evaluations, it outperforms standard models such as Social GAN and TUTR, exhibiting substantially lower displacement errors on the PEC dataset. Ablation experiments confirm that integrating spatial and temporal features is crucial for predicting the erratic turns and speed modulations inherent to pursuit-escape behavior. Beyond accurate trajectory prediction, PursuitNet simulates pursuit events that closely mirror real mouse-and-bait interactions, shedding light on how innate drives, rather than external instructions, guide adaptive decision-making. Although the framework is specialized for rapidly shifting trajectories, our findings suggest that this biologically inspired perspective can deepen understanding of predator-prey dynamics and inform the design of interactive robotics and autonomous systems.

Article	Year
A method for studying escape behavior to terrestrial threats in rodents. Journal of neuroscience methods\| PubMed ID: 38417713	2024
Confrontation experience against escaping bait improves pursuit strategy in mice. The Journal of experimental biology\| PubMed ID: 39606910	2025
Temporal Association Cortex Gates Sound-Evoked Arousal from NREM Sleep. Advanced science (Weinheim, Baden-Wurttemberg, Germany)\| PubMed ID: 39887927	2025
PursuitNet: A deep learning model for predicting competitive pursuit-like behavior in mice. Brain research\| PubMed ID: 40210144	2025

Jincheng Wang

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