Dataset and code for "Functional specificity of recurrent inhibition in visual cortex"
收藏DataCite Commons2024-01-22 更新2024-07-13 收录
下载链接:
https://crick.figshare.com/articles/dataset/Dataset_and_code_for_Functional_specificity_of_recurrent_inhibition_in_visual_cortex_/23295188
下载链接
链接失效反馈官方服务:
资源简介:
This repository contains preprocessed data and analysis code for "Functional specificity of recurrent inhibition in visual cortex" by P. Znamenskiy, M. Kim, D.R. Muir, M.F. Iacaruso, S. B. Hofer, and T.D. Mrsic-Flogel.
<strong>Data</strong>
<strong>connections.mat</strong>: in vitro connectivity data from multiple patch clamp recordings<br>
<strong>pv_dobs.csv</strong>: dates of birth and in vitro recording for each mouse<br>
<strong>pv_rois.mat</strong>: in vivo calcium imaging data used in Figures 2-3. Directories <strong>PV** </strong>contain data from individual sessions.<br>
<strong>pv_rois_vivo_vitro.mat</strong>: in vivo data for cells recorded in vitro (Figure 4)
For access to raw image data, please contact Petr Znamenskiy (petr.znamenskiy@crick.ac.uk).
<strong>Analysis code</strong>
Required dependencies for generating figures:
<strong>shadedErrorBar</strong> for plotting nice error bars [https://github.com/raacampbell/shadedErrorBar]
Additional dependencies for preprocessing raw imaging data (not required to generate figures):
<strong>twophotonanalysisv2</strong>: Mrsic-Flogel lab pipeline for preprocessing of 2p imaging data. Please contact Thomas Mrsic-Flogel (t.mrsic-flogel@ucl.ac.uk) or Petr Znamenskiy (petr.znamenskiy@crick.ac.uk).<br>
<strong>ast_model</strong>: Asymmetric Student-t model for neuropil decontamination [https://github.com/BaselLaserMouse/ast_model]
Code organization:
<strong>connectivity</strong>: functions for analysing in vitro connectivity
<strong>selectivity</strong>: functions for analysing in vivo responses<br>
<strong>preprocess:</strong> functions used to preprocess calcium imaging data and estimate stimulus tuning<br>
<strong>NetworkSimulations</strong>: network simulation code
To generate figures, run the scripts listed in <strong>all_figures.m</strong>.
<strong>Simulation code</strong>
Required dependencies:<br>
The `NEST` spiking neuron simulator, v2.12 [https://nest-simulator.org]<br>
Matlab python connector<br>
Python 2.7
<strong>SimulateAnalyzeTorusNetworks</strong> performs simulation and analysis of the torus model networks discussed in the paper, and generates the paper figures.
提供机构:
The Francis Crick Institute
创建时间:
2023-06-06



