Fig. 2.

Extraction of spacetime motifs. a In order to study the relevant time scale, we create different bin sizes of the milliseconds neural time series. The middle panel shows the data with 10, 200, 1000 ms bin sizes. These time binned time series are binarised using a point-process algorithm (shown on the right). b In order to extract the number of significant spacetime motifs, we compute the eigenvalues above the maximum of the eigenvalues of the null hypothesis distribution based on random matrix theory, following the Marčenko–Pastur distribution²². c We then extract the spacetime motifs using independent component analysis (ICA) and estimate the corresponding activity, where co-activation patterns are found and used to track the activity over time. d The richness of the dynamical repertoire at different timescales can be computed from the spacetime motifs and corresponding probabilities allow using measures of entropy and hierarchy of functional brain organisation (see Methods). We show the four possible different scenarios of how this may vary with timescale whether flat, monotonic decrease or increase or having an optimum