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Model-Free Reconstruction of Excitatory Neuronal Connectivity from Calcium Imaging Signals

Figure 4

TE-based network reconstruction of non-locally clustered topologies.

A ROC curve for a network reconstruction with generalized TE of Markov order , and with fluorescence data conditioned at . The shaded area depicts the 95% confidence intervals based on 6 networks. B Comparison between structural (shown in blue) and reconstructed (red) network properties: clustering coefficients (top), degree distribution (center), and distance of connections (bottom). C Reconstructed clustering coefficients as a function of the structural ones for different reconstruction methods. Non-linear causality measures, namely Mutual Information (MI, red) and generalized Transfer Entropy (TE, yellow), provide the best agreement, while a linear reconstruction method such as cross-correlation (XC, blue) fails, leading invariably to an overestimated level of clustering. The error bars indicate 95% confidence intervals based on 3 networks for each considered clustering level. All network realizations were constructed with a clustering index of 0.5, and simulated with light scattering artifacts in the fluorescence signal.

Figure 4