Dataset and code for "Functional specificity of recurrent inhibition in visual cortex"

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.

Data

• connections.mat: in vitro connectivity data from multiple patch clamp recordings
  
• pv_dobs.csv: dates of birth and in vitro recording for each mouse
  
• pv_rois.mat: in vivo calcium imaging data used in Figures 2-3. Directories PV** contain data from individual sessions.
  
• pv_rois_vivo_vitro.mat: 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).

Analysis code

Required dependencies for generating figures:

• shadedErrorBar for plotting nice error bars [https://github.com/raacampbell/shadedErrorBar]

Additional dependencies for preprocessing raw imaging data (not required to generate figures):

• twophotonanalysisv2: 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).
  
• ast_model: Asymmetric Student-t model for neuropil decontamination [https://github.com/BaselLaserMouse/ast_model]

Code organization:

• connectivity: functions for analysing in vitro connectivity
• selectivity: functions for analysing in vivo responses
  
• preprocess: functions used to preprocess calcium imaging data and estimate stimulus tuning
  
• NetworkSimulations: network simulation code

To generate figures, run the scripts listed in all_figures.m.

Simulation code

• Required dependencies:
  The `NEST` spiking neuron simulator, v2.12 [https://nest-simulator.org]
  
• Matlab python connector
  
• Python 2.7

SimulateAnalyzeTorusNetworks performs simulation and analysis of the torus model networks discussed in the paper, and generates the paper figures.