Dynamics of Probabilistic Neurons

In this project, I created an array of 8,000 neurons, each connected to their nearest neighbours. I then created an update rule, such that each activated neuron starts with an equal, nonzero probability of activating each of its neighbours. After starting the simulation, paths taken often are slightly strengthened by increasing the activation probability between neurons, and paths rarely taken decay in the opposite manner. The question arises: what stable structures will emerge when the neurons are simulated for an extended time? Watch and find out below!

 Written in C++ using XCode, chosen over Python for the sake of speeding up the interaction loops. Visualization created using the OpenGL graphics engine. View the code on Github.