New Algorithm Recovers Hidden Equations from Noisy High-Dimensional Data Using Multi-View Learning
Researchers have introduced DYSCO, a multi-view temporal contrastive learning framework that simultaneously recovers latent trajectories and governing equations from noisy, high-dimensional observations. The method leverages multiple independent noisy views of the same underlying process to separate signal from noise, and provides theoretical guarantees for identification up to an affine transformation. This advance is particularly relevant for neuroscience, where Poisson-distributed spike train data makes recovering underlying neural dynamics especially challenging.
A team led by Mackenzie Mathis has proposed DYSCO (DYnamical System COntrastive learning), a machine learning algorithm designed to identify latent dynamical systems from noisy, high-dimensional measurements. The framework uses multi-view temporal contrastive learning, exploiting multiple independent noisy observations of the same process to disentangle true signal from noise. By parameterizing dynamics in a structured functional basis, DYSCO can symbolically recover governing equations within an affine gauge — meaning the recovered system is unique up to an affine transformation. The authors provide theoretical guarantees of strong identifiability under this affine indeterminacy, extending prior results to the realistic setting of noisy nonlinear observations. Empirically, the method accurately recovers latent trajectories and flow fields across chaotic, oscillatory, and metastable dynamical regimes under both Gaussian and Poisson observation noise. The Poisson noise setting is highlighted as especially important for neural population recordings, suggesting direct applicability to computational neuroscience.
What's missing
As a preprint, DYSCO has not yet undergone formal peer review. Key open questions include how the method scales computationally with the number of views or system dimensionality, how sensitive performance is to the choice of functional basis, and how it compares quantitatively to existing system identification baselines on standardized benchmarks. The degree of affine indeterminacy and its practical impact on downstream scientific interpretation also warrants further clarification.
What different sources said
- arXiv q-bioCenter
Extracting Governing Equations from Latent Dynamics via Multi-View Contrastive Learning
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