Fig. 2

Performance of different methods for N=10 for the Hopf model. Pearson correlation coefficients between EStC matrices estimated using different methods and the GT matrix of the given model parameters for a Hopf model. The number of neurons is set to N=10, while the connection probability p increases from 0.1 to 1.0. For small p (p≤0.5), LCC in conjunction with the DDC algorithm yielded the highest correlation out of all the methods used (row 7). At higher values of p correlation-based methods (rows 1 and 2) and cut LCC (row 6) perform best. LCC and thresholded LCC (rows 4 and 5) perform equally well. All LCC and correlation-based methods perform better than DDC-based methods (rows 8 and 9). NTE and Partial Correlation (rows 10 and 3) show fewer changes in performance as p is changed, and perform worse than LCC- based methods at low p and equally well at higher p, where they also perform better than DDC-based methods. The value per cell is calculated by taking the mean of the correlation in 100 simulations rounded to 2 decimal places.