Dataset of active avoidance in Wistar-Kyoto and Sprague Dawley rats: Experimental data and reinforcement learning model code and output.
Dataset of active avoidance in Wistar-Kyoto and Sprague Dawley rats: Experimental data and reinforcement learning model code and output.
复制标题
Wistar-Kyoto 和 Sprague Dawley 大鼠主动回避的数据集：实验数据和强化学习模型代码和输出。
DOI：
10.1016/j.dib.2020.106074
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发表时间：
2020
期刊：
影响因子：
1.2
通讯作者：
Myers,CatherineE
中科院分区：
文献类型：
--
作者：
Palmieri,John;Spiegler,KevinM;Pang,KevinCH;Myers,CatherineE
在主动逃避-回避实验期间，从40只Wistar-Kyoto（WKY）和40只Sprague道利（SD）大鼠收集数据。在电击发生前的危险期内，按下控制杆可以避免脚震。如果没有发生回避，则给予一系列的足电击，并且大鼠可以按下杠杆以逃避（终止电击）。对于每只动物，数据被简化为每12秒时间范围内是否存在杠杆按压和刺激。使用预处理的数据集，基于演员-评论家架构的强化学习（RL）模型被用来估计几个不同的模型参数，这些参数最能表征实验期间每只大鼠的行为。一旦确定了所有80只大鼠的个体模型参数，就使用RL模型和每只动物的“最佳拟合”参数运行行为恢复模拟;模拟行为产生的回避数据（在给定实验期间避免的试验百分比）可以在模拟大鼠之间进行比较，如通常使用经验数据进行的那样。代表实验数据和模型生成数据的数据集可以以各种方式进行解释，以进一步了解大鼠在回避和逃避学习过程中的行为。此外，可以在各组之间比较每只大鼠的估计参数。因此，可以检测模型参数中可能的应变间差异，这可能提供对学习中的应变差异的了解。实现强化学习模型的软件也可以应用于其他涉及习得学习的实验或作为其他实验的模板。Spiegler，J. Palmieri，K.C.H. Pang，C.E. Myers，主动回避行为的学习模型：Sprague-Dawley和Wistar-Kyoto大鼠之间的差异。行为举止。Brain Res.（2020年6月22日[印刷前的电子版]）doi：10.1016/j.bbr.2020.112784
Data were collected from 40 Wistar-Kyoto (WKY) and 40 Sprague Dawley (SD) rats during an active escape-avoidance experiment. Footshock could be avoided by pressing a lever during a danger period prior to onset of shock. If avoidance did not occur, a series of footshocks was administered, and the rat could press a lever to escape (terminate shocks). For each animal, data were simplified to the presence or absence of lever press and stimuli in each 12-second time frame. Using the pre-processed dataset, a reinforcement learning (RL) model, based on an actor-critic architecture, was utilized to estimate several different model parameters that best characterized each rat's behaviour during the experiment. Once individual model parameters were determined for all 80 rats, behavioural recovery simulations were run using the RL model with each animal's “best-fit” parameters; the simulated behaviour generated avoidance data (percent of trials avoided during a given experimental session) that could be compared across simulated rats, as is customarily done with empirical data. The datasets representing both the experimental data and the model-generated data can be interpreted in various ways to gain further insight into rat behaviour during avoidance and escape learning. Furthermore, the estimated parameters for each individual rat can be compared across groups. Thus, possible between-strain differences in model parameters can be detected, which might provide insights into strain differences in learning. The software implementing the RL model can also be applied to or serve as a template for other experiments involving acquisition learning.Reference for Co-Submission:K.M. Spiegler, J. Palmieri, K.C.H. Pang, C.E. Myers, A reinforcement-learning model of active avoidance behavior: Differences between Sprague-Dawley and Wistar-Kyoto rats. Behav. Brain Res. (2020 Jun 22[epub ahead of print]) doi: 10.1016/j.bbr.2020.112784