Complexity control in living matter

We introduce a dynamic model for complexity control (CC) between systems, represented by time series characterized by different temporal complexity measures, as indicated by their respective inverse power law (IPL) indices. Given the apparent straight forward character of the model and the generality of the result, we formulate a hypothesis based on the closeness of the scaling measures of the model to the empirical complexity measures of the human brain. The main differences between the empirical Complexity Matching and Management Effect (CMME) model and the current CC model are: 1) CC is based on reinforcement learning (RL), while CMME is a quantum mechanical effect based on linear response theory. 2) CC depends on the interaction's nature, while CMME is independent of the strength of the perturbation 3) In CC, the complexity of the interacting networks changes over time whereas in CMMEs, they remain unchanged during the perturbation. 4) CC is manifest on single organ-network (ON) time series (ONTS), while CMMEs only appear at the level of large ensemble averages and at the asymptotic regime. Consequently, CC is a proper model for describing the recent experimental results, such as the rehabilitation in walking arm-in-arm and the complexity synchronization (CS) effect between the ONTS. The CC effect can lead to the design of mutual-adaptive signals to restore the misaligned complexity of maladjusted ONs or, on the other hand, to disrupt the complexity of a malicious system and lower its intelligence.