ReptiLearn: A Smart Home Cage for Behavioral Experiments in Reptiles

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ReptiLearn: A Smart Home Cage for Behavioral Experiments in Reptiles


Eisenberg, T.; Shein-Idelson, M.


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 and under minimally constrained conditions in which actions are selected through bi-directional interactions with the environment and without human intervention. Recent technological advancements have opened up new possibilities for more natural experimental designs 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 which 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 bi-directional 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 non-conventional 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.

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