ReptiLearn: An automated home cage system for behavioral experiments in reptiles without human intervention

Abstract

Understanding behavior and its evolutionary underpinnings is crucial for unraveling the complexities of brain function. Traditional approaches strive to reduce behavioral complexity by designing short-term, highly constrained behavioral tasks with dichotomous choices in which animals respond to defined external perturbation. In contrast, natural behaviors evolve over multiple time scales during which actions are selected through bidirectional interactions with the environment and without human intervention. Recent technological advancements have opened up new possibilities for experimental designs that more closely mirror natural behaviors by replacing stringent experimental control with accurate multidimensional behavioral analysis. However, these approaches have been tailored to fit only a small number of species. This specificity limits the experimental opportunities offered by species diversity. Further, it hampers comparative analyses that are essential for extracting overarching behavioral principles and for examining behavior from an evolutionary perspective. To address this limitation, we developed ReptiLearn—a versatile, low-cost, Python-based solution, optimized for conducting automated long-term experiments in the home cage of reptiles, without human intervention. In addition, this system offers unique features such as precise temperature measurement and control, live prey reward dispensers, engagement with touch screens, and remote control through a user-friendly web interface. Finally, ReptiLearn incorporates low-latency closed-loop feedback allowing bidirectional interactions between animals and their environments. Thus, ReptiLearn provides a comprehensive solution for researchers studying behavior in ectotherms and beyond, bridging the gap between constrained laboratory settings and natural behavior in nonconventional model systems. We demonstrate the capabilities of ReptiLearn by automatically training the lizard Pogona vitticeps on a complex spatial learning task requiring association learning, displaced reward learning, and reversal learning.

PreyTouch: An Automated System for Prey Capture Experiments Using a Touch Screen

“…Behavioral experiments with reptiles tend to last longer and require more repetitions, possibly due to their slower metabolism which diminishes the effectiveness of food as a motivator, lower curiosity or diminished play behavior relative to mammals and birds (Burghardt, 1977(Burghardt, , 2013Wilkinson and Huber, 2012). Following previous success with automated approaches (Eisenberg and Shein-Idelson, 2024), we addressed these caveats by integrating the PreyTouch scheduler agent allowing automatic and remote monitoring of animal progress as well as optimizing experiment schedules. This strategy enabled the collection of large prey catch datasets with minimal effort while animals remain engaged for prolonged periods of time (Figure 4).…”

“…All peripheral devices, including the reward dispenser, LED lights, temperature sensors, and camera triggers, are interfaced using an Arduino (Nano Every). The complete code for both the Arduino and the service facilitating communication through MQTT were sourced from the ReptiLearn project (Eisenberg and Shein-Idelson, 2024) .…”

“…For rewarding animals with live prey we utilized a readily available aquarium feeder (EVNICE EV200GW), which was attached to the arena’s structure using its built-in clamp. The feeder was modified to decrease its response time and driven by an Arduino board (Eisenberg and Shein-Idelson, 2024) . Feeders were stocked with worms, each housed in individual compartments (15 in total) with sufficient food for gut-loading.…”

“…The arena is built from a frame of connected aluminum profiles with dimensions of 70x100x45cm (WxLxH) (Figure 1I). Arena walls and floor were made from 3 mm thick aluminum composite panels (Eisenberg and Shein-Idelson, 2024). The choice of conductive wall is optimized for experiments with electrophysiological measurement requiring a faraday cage.…”

“…We designed a GUI for configuring all experiment parameters, for example, the quantity or frequency of sessions, the prey types and their movements, the rewards or the light/dark cycles (see Methods, Figure S4F). Finally, we added a system for automatically delivering food rewards (including live prey, (Eisenberg and Shein-Idelson, 2024)) upon successful performance and an alert system for notifying the user in case rewards needed to be replenished or upon system failures. Measuring strike dynamics (Schaerlaeken et al, 2007) can provide valuable information about the animal's prey-capture strategies.…”