Cross-correlation analysis at multiple resolutions

The paper proposes a multiresolution cross-correlation analysis (MCCA) combining cross-correlation analysis of complex signals with multiresolution wavelet analysis, which provides decomposition of datasets at several resolution levels and processing of the obtained sets of detail wavelet coefficients. Such an approach can improve the interpretation of changes in the dynamics of the system under study, associating the occurring phenomena with the mechanisms responsible for them. The possibilities of MCCA are illustrated by the example of synchronous and asynchronous dynamics of the model of two interacting nephrons providing oscillations with two different time scales.