five

Normative behavioral data from the novel One Trail Trace escape reaction task (OTTER)

收藏
NIAID Data Ecosystem2026-03-14 收录
下载链接:
http://datadryad.org/dataset/doi%253A10.5061%252Fdryad.q83bk3jn2
下载链接
链接失效反馈
官方服务:
资源简介:
We designed a behavioral task where rats learn to associate two temporally distinct but close-in-time events by means of incidental one-trial learning. The task takes advantage of two competing motivations - to avoid light places and to avoid painful stimuli (foot shock) predicted by an acoustic signal. Motivation to avoid a bright light is constant throughout the experiment and is manifested by rats preferring a dark compartment in a light/dark shuttle box. Motivation to stay in the dark is overridden (1) during the training session: when foot-shock (US) (preceded by the acoustic stimulus (CS)) is presented; and (2) during the testing session: when CS, that predicts US, is presented. The latter case occurs when rats successfully form an association between CS and US in the training session and the rat escapes into the light compartment ('responders'). The rats that do not escape ('non-responders' appear to lack this memory as even non-specific fear response (freezing) was absent. Generally, >50% of rats escape into the lit compartments following CS on the testing day. Importantly, rats do not show a contextual association measured by freezing behavior and preference for a light compartment on the testing day. Moreover, we observed that several putative details in the setup affect the probability of the successful formation of a one-trial trace association. Compared to similar tasks, the OTTER task tests incidental learning with no requirement for pre-training. Moreover, the memory formed in the OTTER task consists of two events that do not overlap in time: a characteristic of many episodic memories. The OTTER task presents high-throughput means to study neuronal substrates of incidental temporal binding.
创建时间:
2022-12-08
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作