Clapp Matthew, Bahuguna Jyotika, Giossi Cristina, Rubin Jonathan E, Verstynen Timothy, Vich Catalina
Department of Psychology & Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
Departament de Ciències Matemàtiques i Informàtica, Universitat de les Illes Balears, Palma, Spain.
bioRxiv. 2024 Aug 4:2023.09.05.556301. doi: 10.1101/2023.09.05.556301.
Here we introduce CBGTPy, a virtual environment for designing and testing goal-directed agents with internal dynamics that are modeled on the cortico-basal-ganglia-thalamic (CBGT) pathways in the mammalian brain. CBGTPy enables researchers to investigate the internal dynamics of the CBGT system during a variety of tasks, allowing for the formation of testable predictions about animal behavior and neural activity. The framework has been designed around the principle of flexibility, such that many experimental parameters in a decision making paradigm can be easily defined and modified. Here we demonstrate the capabilities of CBGTPy across a range of single and multi-choice tasks, highlighting the ease of set up and the biologically realistic behavior that it produces. We show that CBGTPy is extensible enough to apply to a range of experimental protocols and to allow for the implementation of model extensions with minimal developmental effort.
在此，我们介绍CBGTPy，这是一个用于设计和测试具有内部动力学的目标导向智能体的虚拟环境，其内部动力学是基于哺乳动物大脑中的皮质-基底神经节-丘脑（CBGT）通路建模的。CBGTPy使研究人员能够在各种任务中研究CBGT系统的内部动力学，从而形成关于动物行为和神经活动的可测试预测。该框架围绕灵活性原则进行设计，以便在决策范式中可以轻松定义和修改许多实验参数。在此，我们展示了CBGTPy在一系列单选项和多选项任务中的能力，突出了其设置的简便性以及所产生的生物学上逼真的行为。我们表明，CBGTPy具有足够的扩展性，可应用于一系列实验方案，并能以最小的开发工作量实现模型扩展。
