Lendület Laboratory of Systems Neuroscience, Institute of Experimental Medicine, Budapest, Hungary.
János Szentágothai Doctoral School of Neurosciences, Semmelweis University, Budapest, Hungary.
STAR Protoc. 2021 Sep 3;2(3):100795. doi: 10.1016/j.xpro.2021.100795. eCollection 2021 Sep 17.
High throughput, temporally controlled, reproducible quantitative behavioral assays are important for understanding the neural mechanisms underlying behavior. Here, we provide a step-by-step training protocol for a probabilistic Pavlovian conditioning task, where two auditory cues predict probabilistic outcomes with different contingencies. This protocol allows us to study the differential behavioral and neuronal correlates of expected and surprising outcomes. It has been tested in combination with chronic electrophysiological recordings and optogenetic manipulations in ChAT-Cre and PV-Cre mouse lines. For complete details on the use and execution of this protocol, please refer to Hegedüs et al. (2021).
高通量、时间控制、可重复的定量行为分析对于理解行为的神经机制非常重要。在这里，我们提供了一个逐步的训练方案，用于概率性巴甫洛夫条件反射任务，其中两个听觉线索以不同的关联度预测概率性结果。该方案允许我们研究预期和意外结果的差异行为和神经元相关性。它已与慢性电生理记录和 ChAT-Cre 和 PV-Cre 小鼠系中的光遗传学操作结合使用。有关该方案的使用和执行的完整详细信息，请参阅 Hegedüs 等人。(2021)。
