Physiological noise: a comprehensive review on informative randomness in neural systems

Noise is often regarded as mere interference in the analysis of biomedical signals. Nonetheless, stochasticity plays a critical and informative role in the dynamics of complex systems, particularly in neurocardiovascular and neural systems. This review provides a comprehensive exploration on informative randomness in physiological contexts, tracing the evolution of noise research from its foundations on Brownian motion to its applications in neural systems, including the neuroautonomic regulation of cardiovascular dynamics. Key distinctions are made between output (measurement) noise and dynamic (intrinsic) noise, which directly influence the system behaviors at various levels. Several physiological noise identification techniques, such as stochastic differential equations, Bayesian methods, and Kalman filters, are evaluated in real-world scenarios. Special emphasis is placed on the role of physiological noise in multiscale neural systems, such as brain dynamics, neuronal communication, and heart-brain interactions, highlighting how it shapes complex functions. Furthermore, physiological noise is presented as a potential clinical biomarker, offering insights into the underlying structure and health of neural systems. Future research is encouraged to investigate multivariate noise estimation methods and their implications for understanding causality and systemic interactions in neurocardiovascular networks.