AUTHOR=Musotto Rosa , Pioggia Giovanni 

TITLE=Neuromechanical network model

JOURNAL=Frontiers in Physics

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2025.1656677

DOI=10.3389/fphy.2025.1656677

ISSN=2296-424X

ABSTRACT=Neuronal oscillations play a crucial role in brain function, regulating processes such as perception, cognition, and motor control. These oscillations are characterized by frequencies that define specific neural states and interactions. This study investigates a neuro mechanical model that emulates brain wave frequencies using a system of five identical masses connected by springs with variable stiffness. The mass-spring arrangement serves as an analog for neuronal oscillations, with each spring's stiffness adjusted to produce frequencies that approximate the characteristic brain wave bands: Delta, Theta, Alpha, Beta, and Gamma. The model leverages coupled oscillations to represent neural interactions, mirroring how groups of neurons may synchronize to generate brain rhythms. Through a three-step optimization process, the spring constants were fine-tuned to align the system’s natural frequencies with target brain wave frequencies. Initial settings ensured a monotonic trend in stiffness, while the Nelder-Mead algorithm minimized the deviations from target frequencies. The resulting model successfully matched Delta, Theta, and Alpha frequencies closely, while Beta and Gamma bands showed moderate deviations, highlighting the need for further refinement or an expanded system. A comparison between this model and neural dynamics suggests that pulse transmission in a mass-spring system resembles neuronal depolarization waves. The analogy draws parallels between oscillatory interactions in physical and biological systems, where each unit influences its neighbor to transmit energy or signals. The study concludes that simplified mechanical systems can effectively approximate brain oscillations, offering a foundation for exploring cognitive states through physical modeling and suggesting potential avenues for neuro engineering and cognitive research.