Masanori Shimono, 20/05 wrote Motoki Kajiwara, 20/11 translated
To run the code in Matlab, type like the following code at the home directory saving this code: data_index = 1; run_and_save0 Here, you can change the value of data_index to any value.
run_and_save0.m is the main code to be ran, which cites two other m-files, model_ver2020_simple_ver7.m and Post2Conmat_rev.m.
At the beginning of the simulation, the movement is slow and the activity status will seem to be strange. However the figure keeps appearing and gradually shifts to the activity closer to the behavior of real neuronal networks. Finally, a folder called "data" is produced saving the results of recording the second half of the simulation. Also the data of the connection matrix called conmat will be saved.
In the upper left of the figure, the horizontal axis is the time and the vertical axis is the time series of the cells' index. A blue dot is pointed where you are active In the upper right of the figure, cells that are active in a certain time cross section are represented by yellow markers. The connection matrix (binary) is displayed at the bottom left of the figure. The bottom right panel shows distributions of connectivity weights.
If you use this code, cite this following article:
Kajiwara, M., Nomura, R., Goetze, F., Kawabata, M., Isomura, Y., Akutsu, T., & Shimono, M. (2021). Inhibitory neurons exhibit high controlling ability in the cortical microconnectome. PLOS Computational Biology, 17(4), e1008846.