Brief overview of the files and folders in this directory
The EM/ code uses pymaid 0.89, the NEURON/ code uses neuron with python 2.7
pymaid 0.89 is included here. To use:
conda create -n turner-evans python=3.6
conda activate turner-evans
cd pymaid-0.89/
python setup.py install
pip install pandas==1.0.4
pickled_neurons/ - locally saved information from Catmaid for our neurons of interest. This must be downloaded from the online data and decompressed into EM/ before running any of the code.
volume.py - class for working with collections of neurons using pymaid.
buildCX.py - constructs a 'volume' object with our neurons of interest from the data in pickled_data.
connectivity_matrices.py - generates connectivity matrices for the different compartments. (fig2E, fig3D, fig4D, fig5DEH, fig6DE, fig7DEF, figS7B, figS13A)
estimate_synapses_distributions.py - extrapolates connectivity information based on anatomy. Plots histograms. (figS3)
make_PB_EB_extrapolated_matrices.py - uses functions from estimate_synapses_distributions.py to make a simplified extrapolated connectivity matrix. (fig8B)
treeNeuron.py - turns a Catmaid neuron into a graph and writes it to a file for compatibility with the NEURON/ code.
run_all_code.py - runs the above scripts to generate the figures/data in the paper
connectivity_distributions/ - histograms of extrapolated and non-extrapolated connectivity counts (figures and .txt files)
connectivity_matrices/ - combined and comparment-specific connectivity matrice (figures and .txt files)
extrapolated/ - extrapolated EB/PB connectivity (pickled dict)
connectivity_matrices/extrapolated/ extrapolated EB/PB connectivity matrices (figures and .txt files)
neurons/ - neuron structures generated by EM/treeNeuron.py
neuron_simulator.py - class for generating NEURON objects and running simulations based on files generated by EM/treeNeuron.py
experiment_functions.py - function for simulating input to and recording from PEN2 neurons to generate the figure/data in the paper (figS11LM)
results/ - results of the calculations in run by experiment_functions.py