Data from: Exponential history integration with diverse temporal scales in retrosplenial cortex supports hyperbolic behavior
收藏资源简介：
Analysis Code Code written in Python available in the linked Zenodo repository. Behavior data Data was recorded during mouse in vivo imaging or inactivation sessions. Behavior data is saved in Python pickle files in the pandas dataframe format.a: choice type for each trial (Right choice, Left choice, Alarm, Miss) = (1,2,3,4)R: reward outcome type for each trial. (Reward delivered, Reward not delivered) = (1,0) Imaging data Processed two-photon calcium imaging data, aligned to trial ‘ready’ period or ‘choice’ period. Saved as a netcdf file in the xarray dataset format, bundling neural activity for all cells recorded in a session with that session’s behavior metrics. spks: deconvolved estimate spike rate, aligned to ready cue onset. First data point corresponds to 2sec before ready cue onset. Last data point corresponds to 10sec after ready cue onset.z_spks: zscore normalized spks, each cell zscored over the entire session. Inactivation data Behavior data from the inactivation sessions includes variable ‘a’ and ‘R’ as well as the inactivation condition ‘Opto’ and metadata associated with the inactivation parameters. a: choice type for each trial (Right choice, Left choice, Alarm, Miss) = (1,2,3,4)R: reward outcome type for each trial. (Reward delivered, Reward not delivered) = (1,0)Opto: inactivation status for the trial. (cortical inactivation, control inactivation) = (1,0)Inac Type: trial-aligned timing of inactivation, either aligned to the 'ready-answer' periods, the 'intertrial interval', or the both. String = ('ra', 'iti', '0i1ra')Area: the area targeted for inactivation in that session Analysis outputs All of the outputs associated with the analysis are located in the respective directories. Reference the Jupyter notebook files for the specific analysis performed and the generation of the figures from the output data.
分析代码：采用Python编写，可从关联的Zenodo知识库中获取。 行为数据：在小鼠在体成像或失活实验会话中记录得到，以Python pickle格式存储为pandas数据框（pandas dataframe）。其中字段`a`表示每一试次的选择类型，对应编码关系为：右选择、左选择、告警、错失分别编码为1、2、3、4；字段`R`表示每一试次的奖赏结果类型，对应编码关系为：奖赏递送、奖赏未递送分别编码为1、0。 成像数据：为经过预处理的双光子钙成像（two-photon calcium imaging）数据，已对齐至试次「准备就绪」时期或「选择」时期，以netCDF格式存储为xarray数据集（xarray dataset），整合了单会话中所有记录神经元的神经活动与该会话的行为指标。其中：`spks`为反卷积得到的尖峰放电率估计值，对齐至准备就绪提示信号的起始时刻，首个数据点对应准备就绪提示信号起始前2秒，末个数据点对应准备就绪提示信号起始后10秒；`z_spks`为经z分数标准化处理的`spks`数据，每个神经元在整个会话周期内完成独立标准化。 失活实验数据：来自失活实验的行为数据包含字段`a`、`R`，以及失活条件`Opto`和与失活参数相关的元数据。其中字段`a`、`R`的定义与前述行为数据一致；`Opto`表示试次的失活状态，对应编码关系为：皮层失活、对照失活分别编码为1、0；`Inac Type`为试次对齐的失活计时类型，可对齐至「准备-应答」时期、「试次间间隔」时期，或同时对齐两者，字符串取值为('ra', 'iti', '0i1ra')；`Area`表示该会话中靶向失活的脑区。 分析输出：所有与分析相关的输出文件均存储于对应目录中，具体分析流程及基于输出数据生成图表的步骤可参考配套的Jupyter笔记本文件。
