Eisenberg Tal, Shein-Idelson Mark
School of Neurobiology, Biochemistry, and Biophysics, The George S. Wise Faculty of Life Science, Tel Aviv University, Tel Aviv, Israel.
Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
PLoS Biol. 2024 Feb 29;22(2):e3002411. doi: 10.1371/journal.pbio.3002411. eCollection 2024 Feb.
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.
理解行为及其进化基础对于揭示脑功能的复杂性至关重要。传统方法试图通过设计短期、高度受限的行为任务来降低行为复杂性，这些任务具有二分选择，动物在其中对明确的外部扰动做出反应。相比之下，自然行为在多个时间尺度上进化，在此期间，行为是通过与环境的双向相互作用且无需人为干预来选择的。最近的技术进步为实验设计开辟了新的可能性，通过用精确的多维行为分析取代严格的实验控制，从而更紧密地模拟自然行为。然而，这些方法仅适用于少数物种。这种特异性限制了物种多样性所提供的实验机会。此外，它阻碍了比较分析，而比较分析对于提取总体行为原则以及从进化角度研究行为至关重要。为了解决这一限制，我们开发了ReptiLearn——一种通用的、低成本的、基于Python的解决方案，针对在爬行动物的饲养笼中进行自动长期实验进行了优化，无需人为干预。此外，该系统还具有独特的功能，如精确的温度测量和控制、活体猎物奖励分配器、与触摸屏的交互以及通过用户友好的网络界面进行远程控制。最后，ReptiLearn集成了低延迟闭环反馈，允许动物与其环境之间进行双向交互。因此，ReptiLearn为研究变温动物及其他动物行为的研究人员提供了一个全面的解决方案，弥合了受限实验室环境与非传统模型系统中自然行为之间的差距。我们通过在一个需要联想学习、移位奖励学习和逆向学习的复杂空间学习任务上自动训练鬃狮蜥（Pogona vitticeps）来展示ReptiLearn的能力。
