SynfireRings

Turing computation with neural networks composed of synfire rings

This little GiHub repository contains the code to reproduce the results of the paper submitted to IJCNN 2022.

The program that implements neural networks composed of synfire rings is located in the /core/ folder. This program can be executed with python 3.7or higher.

In oder to reproduce the results and figures of the paper, execute the following steps:

Compile the file simulate.py (with python 3.7 or higher). This will simulate a boolean neural network composed of synfire rings that mimics the behavior of a 2-tape Turing machine recognizing the language ${ 0^n1^n0^n : n \geq 0 }$. This step generates the following files:
- data/node.csv
- data/edges.csv
- data/raster.csv
Execute the notebook raster_plot.ipynb to create the pdf figure raster.pdf that corresponds to the raster plot of the whole network.
Compile the file simulate_ring.py (with python 3.7 or higher). This will generate the following file:
- data/raster_ring.csv
Execute the notebook raster_plot.ipynb to create the pdf figure raster_ring.pdf that corresponds to the raster plot of a single ring.
Run movie_snapshot.R to create the set of images that constitute the movie. This process takes a long time. It will generate 300 png files in the folder /data/snapshots/
Go to the folder /data/snapshots/ and execute the command below to create the movie. Here the movie movie.m4vhas already been created and is located in the parent folder: /SynfireRings/movie.m4v.
```
 ffmpeg -r 3 -start_number 1 -i plot%d.png -s 1080x1080 -ar 44100 -async 44100 -r 29.970 -ac 2 movie.m4v
```

JeremCab/SynfireRings

SynfireRings