Python Version: 3.10.13
Package Version:
numpy==1.26.4
pandas==2.2.3
scikit-learn==1.6.1
scipy==1.14.1
tqdm==4.66.2
matplotlib==3.9.3
neuron==9.0a-451-g925a22567+


File structure
./analysis_code
├── analysis.py (analysis code)
├── data 
│   ├── placeholder.pkl 
│   └── preprocess
│       ├── fit_g_syn_FR.pkl (prefit to g_syn and mu_F)
│       └── summary.pkl (summary of all data)
├── data_vis.ipynb (notebook for data visualization and demo)
└── paper_fig
    └── all_k (precomputed optimal path for all kappa)


./simulation_code
├── ch_carter_subtchan.ses
├── stch_carter_subtchan.ses
├── g_syn_on.pkl (the presearched g_syn when neuron just starts firing)
├── g_syn_saturation.pkl (the presearched g_syn when neuron firing starts saturating)
├── seeds.pkl (the seeds for the random number generator)
├── main.py (main simulation file)
├── simulation
│   ├── experiment.py (define how to run the simulation)
│   ├── helper.py
│   └── model.py (define the neuron modeling)
├── test.ipynb (you can execute this file to test the simulation)
└── test.sh (this is the main script to run the simulation, you can implement distributed simulation based on this file)

./population_code_demo
├── data
│   └── orientation distribution.csv (this file defines the prior)
└── population_code_vis.ipynb (this file demo the population code)
