A Wavelet Based Method for Detecting Multiple Encoding Rhythms in Neural Networks

In this work we propose the use of the discrete wavelet transform for the detection of multiple encoding rhythms that appear, for example, in spatio-temporal patterns generated by neuronal activity in a set of coupled neurons. The method here presented allows a quantitative characterization of spatio-temporal patterns and is based on the behavior of a compression-like scheme. The wavelet-based method is faster than the two-dimensional spectral methods for finding different rhythms on spatio-temporal patterns, as it has a computational complexity O (width ×height ) for each 2D-frame of the spatio-temporal pattern. The method also provides a easy method for classifying different qualitative behaviors of the patterns.