Abstract:Biological function arises through the dynamical interactions of multiple subsystems, including those between brain areas, within gene regulatory networks, and more. A common approach to understanding these systems is to model the dynamics of each subsystem and characterize communication between them. An alternative approach is through the lens of control theory: how the subsystems control one another. This approach involves inferring the directionality, strength, and contextual modulation of control between subsystems. However, methods for understanding subsystem control are typically linear and cannot adequately describe the rich contextual effects enabled by nonlinear complex systems. To bridge this gap, we devise a data-driven nonlinear control-theoretic framework to characterize subsystem interactions via the Jacobian of the dynamics. We address the challenge of learning Jacobians from time-series data by proposing the JacobianODE, a deep learning method that leverages properties of the Jacobian to directly estimate it for arbitrary dynamical systems from data alone. We show that JacobianODEs outperform existing Jacobian estimation methods on challenging systems, including high-dimensional chaos. Applying our approach to a multi-area recurrent neural network (RNN) trained on a working memory selection task, we show that the "sensory" area gains greater control over the "cognitive" area over learning. Furthermore, we leverage the JacobianODE to directly control the trained RNN, enabling precise manipulation of its behavior. Our work lays the foundation for a theoretically grounded and data-driven understanding of interactions among biological subsystems.
| Comments: | 10 pages, 6 figures |
| Subjects: | Quantitative Methods (q-bio.QM); Machine Learning (cs.LG); Dynamical Systems (math.DS); Neurons and Cognition (q-bio.NC) |
| Cite as: | arXiv:2507.01946 [q-bio.QM] |
| (or arXiv:2507.01946v2 [q-bio.QM] for this version) | |
| https://doi.org/10.48550/arXiv.2507.01946 arXiv-issued DOI via DataCite |
|
| Journal reference: | Advances in Neural Information Processing Systems 38 (NeurIPS 2025) |
From: Adam J. Eisen [view email]
[v1]
Wed, 2 Jul 2025 17:55:53 UTC (3,535 KB)
[v2]
Fri, 1 May 2026 05:07:50 UTC (3,346 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。