# 新网络complexity

**Repository Path**: fsmyi/complexity

## Basic Information

- **Project Name**: 新网络complexity
- **Description**: 新网络complexity
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-08-16
- **Last Updated**: 2024-08-16

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Network Model with  Internal Complexity Bridges Artificial Intelligence and Neuroscience
## Requirements
````
pip install -r requirements.txt
````

## Time-varied model simulation
To get the results in Fig.3a and Fig.3b, you can __run the main.m__ in "time_varied_simulation/fully-connected" or "time_varied_simulation/xor" folder with MATLAB. These two files run the simulation of HH and tv-LIF2HH models for Supplementary Figures 2.1 and 2.2 .
````
matlab main
````

## Simplified model simulation
To get the results in Fig.3d, you can __run the main.m__ in "simplified_simulation" folder with MATLAB. Different types and amplitudes of input signals can be chosen in main.m. This file run the simulation of HH and s-LIF2HH models for Figure 3.
````
matlab main
````

## Multi-task learning experiment
For model training, you can __run the main.py__ in "multi-task" folder with Python. Different model names, including "LIF_fc", "HH_fc", "LIF_hh_fc", "4LIF_fc", "ANN", "LIF_conv", "HH_conv", "LIF_hh_conv", "4LIF_conv" and "CNN", can be chosen. This file trains each model and prints the accuracy in the multi-task experiments, for Figure 4c. 
````
cd multi-task
python main.py --model_name LIF_fc
````
For robustness test, the amplitude of noise can be modified by changing the value of "A". Different model names, including "LIF_fc", "HH_fc", "LIF_hh_fc", "4LIF_fc", "ANN", "LIF_conv", "HH_conv", "LIF_hh_conv", "4LIF_conv" and "CNN", can be chosen. You can __run the noise_test.py__ in "multi-task" folder with Python. This file adds corresponding noises, trains each model and prints the accuracy in the robustness experiments, for Figure 4h. 
````
python noise_test.py --model_name LIF_fc --A 75
````

## Deep reinforcement learning experiment
For the InvertedPendulum environment, you can __run the main.py__ in "drl_InvertedPendulum" folder with Python. The model name can chosen in "LIF", "HH", "LIF_HH", "4LIF" and "ANN". The results are recorded in the "record/model_name" folder. This file trains each model and records the rewards in the deep reinforcement learning experiment, for Figure 4d (upper one). 
````
cd drl_InvertedPendulum
python main.py --model_name LIF
````
For the InvertedDoublePendulum environment, you can __run the main.py__ in "drl_InvertedDoublePendulum" folder with Python. The model name can chosen in "LIF", "HH", "LIF_HH", "4LIF" and "ANN". The results are recorded in the "record/model_name" folder. You can add state or reward noise and change the amplitude of noise in the main.py. This file trains each model and records the rewards in the deep reinforcement learning experiment, for Figure 4d (bottom one) and 4i. 
````
cd drl_InvertedDoublePendulum
python main.py --model_name LIF
````

## Mutual information analysis
To measure the mutual information of each network, you can __run the main.py__ in "MI" folder with Python. The model name "LIF" can be changed to "HH" or "LIF_HH". This file calculates the mutual information for Figure 5d.
````
cd MI
python main.py --model LIF
````

## FLOPs measurement
To measure the FLOPs in each network, you can __run the cal_flops.ipynb__ in "calculate_flops" folder. This file is for Figure 5b.