Current technologies can record neural avalanches only from a small
portion of the cerebral cortex, leading potentially to systematic
inconsistencies in the values of the measured exponents and in the
scaling relations. Developing robust methods to infer the global
critical exponents from such partial data is therefore essential.
Using two models of avalanche spreading, namely Branching Process and
(2+1)D Directed Percolation, we show that some of the exponents,
namely the ones governing the power spectrum and the detrended
fluctuation analysis of the system activity, are more robust and are
unaffected in some intervals of frequencies by the subsampling.
This robustness derives from the preservation of long-time
correlations in the subsampled signal, even though large avalanches
can be fragmented into smaller ones.
These results don't depend on the specific model and may be used
therefore to extract in a simple and unbiased way the exponents of the
unobserved full system.