"Species-specific wiring of cortical circuits for small-world networks in the primary visual cortex"
Seungdae Baek, Youngjin Park, and Se-Bum Paik*
*Contact: sbpaik@kaist.ac.kr
- MATLAB 2021a or later version
- Installation of the Deep Learning Toolbox (https://www.mathworks.com/products/deep-learning.html)
- No non-standard hardware is required.
- Uploaded codes were tested using MATLAB 2021a.
- Download "LRC_code.zip" and unzip the file.
- Choose the proper subfolder of 'fun_modifed_toolbox' which is matched to your MATLAB version and Move each file to the proper directory
- Download the MNIST dataset (Y. LeCun et al., 2010), when you train the network model. Move each file to 'fun_stimulus\MNIST'
- Download 'Data.zip' from below link and unzip files in the same directory
- [Data URL] : https://doi.org/10.5281/zenodo.8081752
- Expected Installation time is about 45 minutes, but may vary by system conditions.
- The result of the figure can be regenerated through the code of the folders below:
- i) Fig1: Species-specific existence of long-range horizontal connectivity in V1 (Fig.1)
- ii) Fig2_3: Integration of global information by LRCs in a large network (Fig.2) Local connections are required to integrate local information in a large network (Fig.3)
- iii) Fig4_5: LRCs organize a small-world network to enable the recognition of various visual features (Fig.4) Small-world coefficient of the network predicts the size-dependent effect of LRCs for visual encoding (Fig.5)
- iv) Fig6: Emergence of LRCs for size-dependent optimization of the performance and wiring cost (Fig.6)
- Raw data of the figure can be found in the "Data.zip"