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In addition, for both this patch-clamp method and our effective connectivity method, connection probability increased almost linearly with the number of common neighbors.
The approach we adopted, by considering the connection strength proportional to the value of the connectivity method, does not allowed us to identify inhibitory connections.
For each pair of neurons, the connectivity method provides an estimation of the connection strength (one for each direction).
For a given connectivity method, what effectively matters, is its capacity to properly detect a connection, so that mainly TPs (a connection properly detected) and FPs (a connection wrongly detected) have to be taken into account.
The PPC slope, in this case, provides information about the performances of the connectivity method as well as the right order in the identification of the connections.
A new species reduction method called the Simulation Error Minimization Connectivity Method (SEM-CM) was developed.
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Therefore, PPC adds further information regarding how the connectivity methods identify the connections.
In addition to the coarse AUC parameter, PPC allows to evaluate not only the absolute number of the existing/non-existing connections identified by the connectivity methods but also to recognize the synaptic weights following the right ordering.
On the other hand, PPCs not only allow evaluating the absolute number of the existing/non-existing connections correctly identified by the connectivity methods, but also provide further information regarding how the connectivity methods identify them.
Similarly to the network models, we expect to maximize the performance of the connectivity methods by selecting a number of connections near the number of direct connections of a hypothetic SWM of the culture.
Therefore, by considering the information contained in the PPCs shown in Figure 4, we could expect to maximize the performance of the connectivity methods by choosing about 600 700 connections also on the experimental data.
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