Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models describe complex systems using latent variables that evolve according to simple locally linear dynamics. However, existing methods for latent variable estimation are not robust to dynamical noise and system nonlinearity due to noise-sensitive inference procedures and limited model formulations. This can lead to inconsistent results on signals with similar dynamics, limiting the model's ability to provide scientific insight. In this work, we address these limitations and propose a probabilistic approach to latent variable estimation in decomposed models that improves robustness against dynamical noise. Additionally, we introduce an extended latent dynamics model to improve robustness against system nonlinearities. We evaluate our approach on several synthetic dynamical systems, including an empirically-derived brain-computer interface experiment, and demonstrate more accurate latent variable inference in nonlinear systems with diverse noise conditions. Furthermore, we apply our method to a real-world clinical neurophysiology dataset, illustrating the ability to identify interpretable and coherent structure where previous models cannot.

该研究解决了现有潜在变量估计方法在动态噪声和系统非线性方面的鲁棒性不足的问题。作者提出了一种概率方法来改进分解模型中潜在变量的估计，并引入扩展的潜在动态模型以提高对非线性的鲁棒性。研究表明，该方法在多种噪声条件下的非线性系统中能够更准确地推断潜在变量，并成功应用于实际临床神经生理数据集。

鲁棒发现潜在神经动态的概率分解线性动态系统