Sentence examples for performance of connectivity from inspiring English sources

Pearson correlation and receiver operating characteristic (ROC) analyses were applied to evaluate the performance of connectivity strengths in predicting/classifying treatment responses.

These bridges greatly degrade the performance of connectivity-based clustering algorithms, such as single-linkage clustering algorithm and complete-linkage clustering algorithm [46].

However, in the context of functional connectivity, the performance of a connectivity method can be stated differently from the context of classifiers.

However, in more realistic networks and under increased connectivity conditions, the performance of the connectivity methods rapidly decreases.

This paper compares the statistical performance of linear connectivity detection [1] using four popular neural connectivity estimators.

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.

In the comparisons, we used Monte Carlo simulations of three widely used toy models from the literature and verified the performance of null connectivity hypothesis rejection as a function of data record length, K. To complement the study, we also computed false positive (FP) and false negative (FN) test rates for each estimator alternative.

This dataset was used to test the performance of the connectivity methods detailed in the next sections.

Considering the PPCs of the model networks (Figure 4C and 4D), they suggest to select about 600 links to maximize the performance of the connectivity methods.

The purpose of this work is to address this issue by applying standard and ad hoc procedures to evaluate the performance of the connectivity methods on realistic models of neuronal networks.