  An heuristic signal processing scheme recently introduced shows how brain
signals can be efficiently represented by a sparse spatiotemporal point
process. The approach has been validated already for different relevant
conditions demonstrating that preserves and compress a surprisingly large
fraction of the signal information. In this paper the conditions for such
compression to succeed are investigated as well as the underlying reasons for
such good performance. The results show that the key lies in the correlation
properties of the time series under consideration. It is found that signals
with long range correlations are particularly suitable for this type of
compression, where inflection points contain most of the information. Since
this type of correlation is ubiquitous in signals trough out nature including
music, weather patterns, biological signals, etc., we expect that this type of
approach to be an useful tool for their analysis.
