five

Neural mechanisms to incorporate visual counterevidence in self movement estimation

收藏
DataONE2023-10-30 更新2024-06-08 收录
下载链接:
https://search.dataone.org/view/sha256:bb7ba9f39032cf2565887126079011b224232422db101037f06258ac80fd5610
下载链接
链接失效反馈
官方服务:
资源简介:
In selecting appropriate behaviors, animals should weigh sensory evidence both for and against specific beliefs about the world. For instance, animals measure optic flow to estimate and control their own rotation. However, existing models of flow detection can be spuriously triggered by visual motion from external movements. Here, we show that stationary patterns on the retina, which constitute evidence against observer rotation, suppress inappropriate stabilizing rotational behavior in the fruit fly Drosophila. In silico experiments show that artificial neural networks that are optimized to distinguish observer movement from object motion similarly detect stationarity and incorporate negative evidence. Employing neural measurements and genetic manipulations, we identified components of the circuitry for stationary pattern detection, which runs parallel to the fly’s local motion- and optic flow-detectors. Our results show how the fly brain incorporates negative evidence to improve headi..., This dataset contains all experimental data necessary to create figures in Tanaka et al. (2023), as well as scripts to analyze them. The scripts are written in Matlab 2019b, and uses some functions from Statistics and Machine Learning Toolbox., , # Data for: Neural mechanisms to incorporate visual counterevidence in self movement estimation ## Folder structure * `scripts` The easiest way to explore the data is to run the .m scripts under the `scripts` folder. Each script loads .mat files in the `data` holder and replicates plots in the paper showing behavioral and physiological data. The folder contains following scripts, with corresponding figure panels: * `fig1_01_basic.m` : Figure 1G-L * `fig1_02_bar.m` : Figure 1PQ * `fig1_03_orientation.m` : Figure 1ST, S1BC * `fig1_04_movingwindow.m` : Figure S1EF * `fig1_05_onset.m` : Figure 1VW, S1G-J * `fig3_sweeps.m` : Figure 3 (all panels) * `fig4_01_T4T5.m` : Figure 4EF, S3AB * `fig4_02_LPTC.m` : Figure 4GH, S3CD * `fig5_transfer.m` : Figure 5 (all panels) * `fig6_01_screen_distplot.m` : Figure 6A, S4A * `fig6_02_replication.m` : Figure 6B, S4B * `fig6_03_NTsplit.m` : Figure S4D * `fig7_mi4.m` : Figure 7A-D The generated ...
创建时间:
2023-11-29
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