Simulation Based Inference of a Simple Neural Network Structure
The method uses simulation-based inference to estimate network structure from sparse spike train statistics, overcoming limitations of traditional methods that rely on many recorded neurons. By numerically modeling a random graph and adjusting its connection probability parameter 'p' to match observed spike train data, the approach provides better results than existing sub-network reconstruction techniques.