amvjakob / cppcourse-brunel

Dynamics of Sparsely Connected Networks of Excitatory and Inhibitory Spiking Neurons

cppcourse-brunel

Aim

This program simulates a spiking neural network of 12500 neurons described in Nicolas Brunel's paper "Dynamics of Sparsely Connected Networks of Excitatory and Inhibitory Spiking Neurons", and aims to reproduce figure 8 of the mentioned paper.

Running

The default values for ETA and G are, respectively, 2 and 5. These can be modified in the src/Constants.hpp file to reproduce the different graphs. The simulation takes about 20s to run from start to finish.

To run the program, follow these steps:

1. Download or clone repository
2. Navigate to the cloned / downloaded folder (cppcourse-brunel)
3. cd build to enter the build folder
4. cmake .. to run CMake and generate the makefiles
5. make to make both the simlation as well as the tests. Alternatively, make NeuroSimulation to generate the simulation only or make NeuroSimulation_UnitTest to generate the unit tests only
6. ./NeuroSimulation to run the simulation, ./NeuroSimulation_UnitTest to run the tests
7. The result file is created under results/, with the name "spikes_eta[eta_val]_g[g_val].gdf", and contains the times and ids of the neurons that spiked.

Documentation

To create the documentation from scratch, simply type make doc from the build folder to generate it in the doc folder.

Plot

In order to plot the data, I wrote a script (src/graph.py) to produce both a scatterplot and a histogram.

• The scatterplot shows neuron ID vs spiking time. Every dot corresponds to one spike.

• The histogram shows the number of spikes for a given time interval (500 bins).

Alternatively, the web application can also be used to generate the plots.