Skip to main content

Table 3 Parameters for the hidden-node network model. The noise values on the edges \(\eta _{y_{k}}\) and the additional edges \(\eta _{y_{\mathrm{{out}}}}\) and \(\eta _{y_{\mathrm{{in}}}}\) for the model simulation to fit the twelve transition probabilities of the data shown in Fig. 6. Here \({\mathcal{S}} = 0.17\) and \({\mathcal{C}} = 1.268\)

From: Noisy network attractor models for transitions between EEG microstates

\(\eta _{y_{\mathrm{{out}}}}\)

\(\eta _{y_{\mathrm{{in}}}}\)

    

0.032

0.052

    

\(\eta _{y_{1}}\)

\(\eta _{y_{2}}\)

\(\eta _{y_{3}}\)

\(\eta _{y_{4}}\)

\(\eta _{y_{5}}\)

\(\eta _{y_{6}}\)

0.0501

0.0533

0.0489

0.0521

0.0567

0.0494

\(\eta _{y_{7}}\)

\(\eta _{y_{8}}\)

\(\eta _{y_{9}}\)

\(\eta _{y_{10}}\)

\(\eta _{y_{11}}\)

\(\eta _{y_{12}}\)

0.0477

0.0492

0.0468

0.0503

0.0501

0.0548