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Physiological noise: a comprehensive review on informative randomness in neural systems
Andrea Scarciglia1, Claudio Bonanno2, Gaetano Valenza1
1Department of Information Engineering, School of Engineering, University of Pisa, Italy; Bioengineering and Robotics Research Center E. Piaggio, School of Engineering, University of Pisa, Italy.
Physics of Life Reviews
|April 17, 2025
Summary
Physiological noise, often dismissed as interference, actually provides critical information in biological systems. Understanding this informative randomness is key to analyzing neurocardiovascular and neural dynamics.
Area of Science:
- Neuroscience
- Physiology
- Biomedical Engineering
Background:
- Noise is traditionally viewed as signal interference in biomedical analysis.
- Stochasticity plays a crucial, informative role in complex physiological systems, especially neurocardiovascular and neural networks.
Purpose of the Study:
- To comprehensively explore the role and identification of informative randomness in physiological contexts.
- To review noise research evolution from Brownian motion to neural system applications.
- To highlight physiological noise as a potential clinical biomarker.
Main Methods:
- Review of noise research evolution and applications in neural systems.
- Distinction between output (measurement) noise and dynamic (intrinsic) noise.
- Evaluation of physiological noise identification techniques (stochastic differential equations, Bayesian methods, Kalman filters).
Main Results:
- Physiological noise significantly influences system behaviors at multiple levels.
- Noise is integral to multiscale neural systems, shaping brain dynamics, neuronal communication, and heart-brain interactions.
- Noise characteristics can serve as biomarkers for neural system structure and health.
Conclusions:
- Informative randomness is fundamental to understanding complex physiological dynamics.
- Noise identification techniques are crucial for analyzing neurocardiovascular and neural functions.
- Future research should focus on multivariate noise estimation for causality and interaction insights.
Keywords:
Cardiovascular systemDynamic noiseKalman filterNeural activityNoise estimationOutput noiseStochastic differential equationsStochastic processesMore Related Videos
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