What question did this study set out to answer?

This research aims to demonstrate a new method for synchronizing stochastic neural dynamics using a physics-informed approach.

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April 10, 2026Open Access

Physics-Informed Koopman Learning Approach for Fixed-Time Synchronization of Stochastic Neural Dynamics

Key Points

This research aims to demonstrate a new method for synchronizing stochastic neural dynamics using a physics-informed approach.
Developed a MATLAB simulation suite for testing the method
Applied the Hindmarsh-Rose neuron model under deterministic and stochastic conditions
Incorporated Brownian noise and compound Poisson jumps
Achieved fixed-time synchronization of stochastic dynamics
Demonstrated robustness against stochastic perturbations
Highlighted the effectiveness of the Koopman approach in achieving synchronization

Abstract

# Physics-Informed Koopman Learning Approach for Fixed-Time Synchronization of Stochastic Neural Dynamics A MATLAB simulation suite that demonstrates **Physics-Informed Koopman learning approach for fixed-time synchronization of stochastic neural dynamics** of the Hindmarsh-Rose (HR) neuron model under both deterministic dynamics and stochastic perturbations (Brownian noise + compound Poisson jumps).

Physics-Informed Koopman Learning Approach for Fixed-Time Synchronization of Stochastic Neural Dynamics

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Abstract

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