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

Summary

Here, we present a protocol to detect and quantify predatory pursuit behavior in a mouse model. This platform provides a new research paradigm for studying the dynamics and neural mechanisms of predatory pursuit behavior in mice and will provide a standardized platform for studying pursuit behavior.

Abstract

Predatory pursuit behavior involves a series of important physiological processes, such as locomotion, learning, and decision-making that are critical to the success of an animal in capturing prey. However, there are few methods and systems for studying predatory pursuit behavior in the laboratory, especially in mice, a commonly used mammalian model. The main factors limiting this research are the uncontrollability of live prey (e.g., crickets) and the challenge of harmonizing experimental standards. The goal of this study was to develop an interactive platform to detect and quantify predatory pursuit behaviors in mice on a robotic bait. The platform uses computer vision to monitor the relative positions of the mouse and robotic bait in real time to program the motion patterns of the robotic bait, and the interactive two-dimensional sliders magnetically control the movement of the robotic bait to achieve a closed-loop system. The robotic bait is able to evade approaching hungry mice in real-time, and its escape speed and direction can be adjusted to mimic the predatory pursuit process in different contexts. After a short period of unsupervised training (less than two weeks), the mice were able to perform the predation task with a relatively high efficiency (less than 15 s). By recording kinematic parameters such as speed and trajectories of the robotic bait and the mice, we were able to quantify the pursuit process under different conditions. In conclusion, this method provides a new paradigm for the study of predatory behavior and can be used to further investigate the dynamics and neural mechanisms of predatory pursuit behavior.

Introduction

The pursuit of prey by predators is not only a vivid demonstration of the struggle for survival but also a key driver of species evolution, maintaining the ecological balance and energy flow in nature^1,2. For predators, the activity of pursuing prey is a sophisticated endeavor that involves a variety of physiological processes. These processes include the motivational states that drive the predator to hunt³, the perceptual abilities that allow it to detect and track prey^4,5,6, the decision-making abilities that dictate the course of the hunt⁷, the locomotor function that enables the physical pursuit^8,9 and the learning mechanisms that refine hunting strategies over time^10,11. Therefore, predatory pursuit has received much attention in recent years as an important and complex behavioral model.

As a widely used mammalian model in the laboratory, mice have been documented to hunt crickets both in their natural habitat and in laboratory studies¹². However, the diversity and the uncontrollability of live prey in quantifying predatory pursuit behavior limits the reproducibility of experiments as well as the exchange of comparisons between different laboratories¹³. First, cricket strains may be different among laboratories, resulting in differences in prey characteristics that could influence pursuit behavior. Second, individual crickets have unique characteristics that may affect the outcome of predatory interactions¹⁴. For example, the escape speed of each cricket may be different, leading to variability in the pursuit dynamics. Additionally, some crickets may have a short warning distance, which could lead to a lack of pursuit process, as the predator may not have the opportunity to engage in pursuit. Finally, some crickets may exhibit defensive, aggressive behavior when stressed, which complicates the interpretation of experimental data¹⁵. It is difficult to determine whether changes in predator behavior are due to the defensive strategies of the prey or are inherent to the predator's behavioral patterns. This blurred line between prey defense and predator strategies adds another layer of complexity to the study of predatory pursuit.

Recognizing these limitations, researchers have turned to artificial prey as a means of controlling and standardizing experimental conditions^16,17. Seven rodent species, including mice, have been shown to exhibit significant predatory behavior toward artificial prey¹³. Therefore, a controllable robotic bait may be feasible in the study of predatory pursuit behavior. By designing different artificial prey, researchers can exert a level of control over experimental conditions, which is not possible with live prey^18,19. In addition, a small number of previous studies have used artificially controlled robotic fish or prey to study schooling behavior and predation in fish^15,17,19. These studies have highlighted the value of robotic systems in providing consistent, repeatable, and manipulable stimuli for experimental research, but despite these advances, the field of rodent behavior, particularly in mice, lacks a dedicated platform for detecting and quantifying predatory chasing behavior using robotic bait.

Based on the above reasons, we designed an open-source real-time interactive platform to study predatory pursuit behavior in mice. The robotic bait in the platform can escape from the mice in real-time, and the robotic bait is highly controllable, so we can set different escape directions or speeds to simulate different predation scenarios. A Python program on the computer was used to generate the motion parameters of the robotic prey, which was combined with an STM32 microcontroller to drive the servo motors and control the motion of the robotic decoy. The modular hardware system can be adapted to the specific laboratory environment in real-time, and the software system can adjust the difficulty of the system as well as the indicators to better serve the research purpose according to the experimental needs. The lightweight system allows for a significant reduction in computer processing time, which is essential for the effectiveness of the system and improves its portability. The platform supports the following technical features: flexible and controllable artificial prey for easy repetition and modeling; maximum simulation of the hunting process in a natural environment; real-time interaction and low system latency; the scalability of hardware and software as well as scalability; cost-effectiveness and ease of use. Using this platform, we have successfully trained mice to perform predatory tasks under various conditions and have been able to quantify parameters such as trajectory, speed, and relative distance during predatory pursuit. The platform provides a rapid method for establishing a predatory pursuit paradigm to further investigate the neural mechanisms behind predatory pursuit.

Protocol

Adult C57BL/6J mice (male, 6-8 weeks old) are provided by the Army Medical University Laboratory Animal Center. All experimental procedures are performed in accordance with institutional animal welfare guidelines and are approved by the Animal Care and Use Committee of the Army Medical University (No. AMUWEC20210251). Mice are housed under temperature-controlled conditions (22-25°C) with a 12-h reverse light/dark cycle (lights on 20:00-8:00) and free access to food and water.

1. Hardware preparation for real-time interactive platform

1. Mount a webcam on a crossbar above the entire platform to monitor the positions of the mouse and robotic bait in the arena below in real-time and transmit the images to the computer (Figure 1A).
2. Design a large circular arena (800 mm diameter, 300 mm height) consisting of a square acrylic panel at the bottom and an acrylic tube as a border. Place evenly spaced four icons (square, triangle, circle, and cross) on the interior walls of the arena to serve as visual cues (Figure 1B).
   NOTE: The inside wall of the acrylic tube and the surface of the square acrylic plate need to be covered with an opaque sticker.
3. Use a round neodymium magnet (40 mm diameter, 10 mm height, crickets are about 2-4 cm in length^4,22) with food pellets (5 × 0.2 mg) as a robotic bait. Attach a blue sticker to the surface of the magnet for identification and location.
4. Mount a two-dimensional slider (with an effective travel of 1000 mm) under the arena. Install another neodymium magnet on its carrier as a pulling magnet to magnetically and remotely pull the robotic bait.
5. Drive the puller magnet by servo motors controlled by an STM32 board and a switching circuit. Using the speed-direction mode to drive a servo motor (75 mm per 16,000 pulses), the frequency of the PWM wave at the output port on the STM32 board encodes the speed, and the high or low levels (3.3 V or 0 V) encode the forward or reverse direction (Figure 1C).
   NOTE: The switching circuit is shown in Supplementary Figure 1, which only shows the speed and direction signal connection diagram required for the movement of one slide and the control of the other slide in the same way. The ground wires in the two slide control circuits can be connected together.

2. Software design for real-time interactive platform

1. Use the main program to detect the relative position of the mouse and the robotic bait and to transmit the motion signal of the robotic bait to the STM32 microcontroller (Figure 1D).
2. When the computer receives images from the webcam, process the frames in the Python environment.
3. Convert each frame from the RGB color model to the HSV color model using the OpenCV²⁰ module (cv2.cvtColor).
   NOTE: Since mice are nocturnal foragers and for the stability of identification, the experiments should be conducted under conditions of low and stable illumination, ensuring consistent lighting conditions in the arena and avoiding partial shadows. The illumination in this experiment was approximately 95 lux.
4. Acquire mask images of the mouse and robotic bait based on their color ranges in the HSV color model, respectively (cv2.inRange).
5. Apply median filtering to the mask image to make the contours of the mouse and robotic bait clearer and more stable (cv2.medianBlur).
6. Obtain the contour information of the mouse and the robotic bait using the contour detection function (cv2.findContours), and then find a minimum rectangle that can cover the contour area. Use the center coordinates of this rectangle as the position of the mouse and the robotic bait, respectively, and save them as TXT files.
7. Based on the position information of the mouse and the robot bait in each frame, calculate the speed of the mouse and the relative distance to the robot bait and save these parameters as TXT files.
   NOTE: Considering the time cost of calculations related to image processing, the actual processing speed of the script is about 41 fps, which means that the system can detect mouse and robotic bait movements in about 24 ms. The system delay was tested between sending a motion command and detecting motion is 59.4 ± 7.3 ms (n = 8), so the delay between the mouse movement and the prey movement triggered by the mouse is about 83 ms (crickets have a reaction time of less than 250 ms²²).
8. Depending on the relative distance between the mouse and the robotic bait and their respective positions in the arena, determine the escape strategy of the robotic bait^21,22,23.
   NOTE: If the mouse is relatively far away from the robotic bait (>250 mm), it is assumed that the robotic bait is in a safe zone. Suppose the mouse is close enough to the robotic bait (<80 mm, since the relative distance in this method is determined based on the center of the mouse and the robotic lure, we adjusted this threshold distance based on the body length of the mouse, approximately 11 cm, and the diameter of the lure, 4 cm). In that case, it is considered to be a successful capture, and the robotic bait will stop, allowing the mouse to consume the food pellets on it (Figure 2A). In the straight-line escape strategy, if the bait is in the arena's center area (distance to boundary >75 mm), indicating sufficient space in all escape directions, it will move in the direction that the mouse is moving toward itself. On the contrary, if the bait is close to the arena's wall as indicated by the surrounding area (distance to boundary ≤75 mm), signifying insufficient space for continuous movement, it will make a 90° turn to the side with a greater distance to the arena boundary (more available space, Figure 2B). In the turn-based escape strategy, the robotic bait will turn 90° to the side with more space to escape, judged at 0.5 s intervals (Figure 2C).
9. After determining the escape strategy, have the Python script encode the speed and direction signals separately and send them to the STM32 via the serial port (ser.port = 'COM3') at a bit rate of 115200.
   NOTE: Scripts in STM32 are compiled in C environment. When the speed signal is received, it is converted into a PWM wave of the corresponding frequency by the crystal oscillator frequency divider and output (STM32 crystal frequency is 72000000). After receiving the direction signal, the digital signal is converted by pulling up or down the voltage of the output port (0 or 3.3 V).
10. Use the serial port burner to burn a compiled C program to the STM32.
11. Use the reset program to move the robotic bait to the center of the arena.
    NOTE: By detecting the position of the robotic bait in the arena, the robotic bait is continuously driven toward the center position (5 cm/s), and when a distance of <20 mm from the center position is detected, it is considered to have reached the center of the arena and stops moving. Run the reset program before running the main program.

3. Habituation (Figure 2D)

1. Fast the mouse for 12 h to reduce the animal's body weight to 90-95% of normal, with free access to water during this period.
2. First, run the reset program to move the robotic bait to the center of the arena, and then isolate the hungry mouse at the edge of the arena with a baffle.
3. Set the save path of the file and the moving speed of the robotic bait (5 cm/s) in the main program. Then click Run, and observe in the pop-up video window whether the mouse and robot decoys are recognized stably.
4. Remove the baffle and start the timer to observe the predatory behavior of the mouse for 20 min, recording the time when the mouse first take a food pellet from the robotic bait. If the mouse fails to do so, stop the bait and place it next to the mouse for feeding.
5. Return the mouse to its cage and allow a 24-h recovery period with free access to food and water. Then, repeat habituation. If there is no significant difference in the time to first food retrieval between two consecutive trials within the same group of animals, consider that the habituation is complete. This period typically takes 3-5 trials.
6. After each habituation, clean the arena properly with 75% alcohol and water.

4. Predation task (Figure 2D)

1. Begin the predation task immediately after the habituation.
2. Fast the mouse for 12 h to reduce the animal's body weight to 90-95% of normal, with free access to water during this period.
3. First, run a reset program to move the robotic bait to the center of the arena, and then isolate the hungry mouse at the edge of the arena with a baffle.
4. Set the save path of the file and the moving speed of the robotic bait (0-60 cm/s, different speeds can be set according to experimental needs) in the main program. Then click Run, and observe in the pop-up video window whether the mouse and robotic bait are detected stably.
5. Remove the baffle and start the timer to observe the predatory behavior of the mouse for 60 s.
   1. If the mouse successfully captures the robotic bait within 60 s, close the main program and allow the mouse to eat all the food pellets before being returned to the cage.
   2. If the mouse does not capture the robotic bait within 60 s, release the mouse directly back into the cage without any reward or punishment.
6. Conduct a second round of predation tasks 4 h later, repeating steps 4.3-4.5.
7. Allow the mouse a 24-h recovery period with free access to food and water. Then, repeat the predation task. If more than 80% of the mice spend less than 15 s in two consecutive trials, the mice are considered skilled predators.
8. After each predation task, clean the arena properly with 75% alcohol and water.

Results

To escape from a predator, prey often employs flexible and variable escape strategies, such as changing escape speeds or fleeing in unpredictable directions^21,22,23. In this study, the movement pattern of the robotic bait is flexibly controlled in speed and direction so that we can change the escape direction as well as the speed of the robotic bait to simulate the predation task under different conditions, respectively.

Discussion

In this protocol, to achieve real-time control with low system latency, we use OpnenCV, a lightweight and efficient computer vision library, and a color model to identify the positions of the mice and the robot decoy. This requires that the lighting in the arena is relatively stable and that the shadows in the arena are avoided as much as possible to avoid interfering with the detection of the black mice. To obtain relatively stable contour detection, we capture the color ranges of the mice with the robotic decoys at sev...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Acrylic cylinder	SENTAI	PMMA	Diameter 800 mm Height 300 mm Thickness 8 mm
Anti-vibration table	VEOO	Custom made	Length 1500 mm Width 1500 mm Height 750 mm
Camera	JIERUIWEITONG	HF868SS	Pixel Size 3 µm ´ 3 µm 480P: 120 fps
Camera support frame	RUITU	Custom made	Maximum width 3300 mm Maximum height 2600 mm
Circuit board	WXRKDZ	Custom made	Length 60 mm Width 40 mm Hole spacing 2.54 mm
Computer	DELL	Precision 5820 Tower	Inter(R) Xeon(R) W-2155 CPU NVIDIA GeForce RTX 2080Ti
DuPont Line	TELESKY	Custom made	30 cm
Food pellets	Bio-serve	F07595	20 mg
Platform support frame	HENGDONG	OB3030	Length 1600 mm Height 900 mm Width 800 mm
Regulated power supply	ZHAOXIN	PS-3005D	Output voltage: 0-30 V Output current:0-3 A
Round magnetic block	YPE	YPE-230213-5	Diameter 40 mm Thickness 10 mm
Servo Motor Driver	FEREME	FCS860P	0.1 kw-5.5 kw SVPWM 220 VAC+10% ~-15% RS-485
Slide rail	JUXIANG	JX45	Length 1000 mm Width 1000 mm
Square acrylic plate	SENTAI	PMMA	Length 800 mm Width 800 mm Thickness 10 mm
Square Magnetic Block	RUITONG	N35	Length 100 mm Width 50 mm Thickness 20 mm
Stm32	ZHENGDIANYUANZI	F103	STM32F103ZET6 72 MHz clock
Transistor	Semtech	C118536	2N2222A, NPN