Levels of complexity in scale-invariant neural signals

Many physiological systems exhibit complex scale-invariant and nonlinear features characterized long-range power-law correlations, indicating a possibly common control mechanism. It has been suggested that dynamical processes, influenced by inputs and feedback on multiple time scales, may be sufficient to give rise to this complexity. Two examples of physiologic signals that are the output of hierarchical multiscale physiologic systems under neural control are the human heartbeat and human gait. We show that while both cardiac interbeat interval and gait interstride interval time series under healthy conditions have comparable scale-invariant behavior, they still belong to different complexity classes. We compare results from empirical findings and stochastic feedback modeling approaches to cardiac and locomotor dynamics, which provide new insights into the multicomponent neural mechanisms regulating these complex systems.