Latent variable models serve as powerful tools to infer underlying dynamics from observed neural activity. However, due to the absence of ground truth data, prediction benchmarks are often employed as proxies. In this study, we reveal the limitations of the widely-used 'co-smoothing' prediction framework and propose an improved few-shot prediction approach that encourages more accurate latent dynamics. Utilizing a student-teacher setup with Hidden Markov Models, we demonstrate that the high co-smoothing model space can encompass models with arbitrary extraneous dynamics within their latent representations. To address this, we introduce a secondary metric -- a few-shot version of co-smoothing. This involves performing regression from the latent variables to held-out channels in the data using fewer trials. Our results indicate that among models with near-optimal co-smoothing, those with extraneous dynamics underperform in the few-shot co-smoothing compared to 'minimal' models devoid of such dynamics. We also provide analytical insights into the origin of this phenomenon. We further validate our findings on real neural data using two state-of-the-art methods: LFADS and STNDT. In the absence of ground truth, we suggest a proxy measure to quantify extraneous dynamics. By cross-decoding the latent variables of all model pairs with high co-smoothing, we identify models with minimal extraneous dynamics. We find a correlation between few-shot co-smoothing performance and this new measure. In summary, we present a novel prediction metric designed to yield latent variables that more accurately reflect the ground truth, offering a significant improvement for latent dynamics inference.

翻译：潜变量模型是推断观测神经活动背后动态机制的有力工具。然而，由于缺乏真实数据，预测基准常被用作替代指标。本研究揭示了广泛使用的"共平滑"预测框架的局限性，并提出了一种改进的少样本预测方法，以促进更准确的潜变量动态建模。通过采用隐马尔可夫模型的师生框架，我们证明了高共平滑度的模型空间中可能包含那些在潜变量表征中具有任意冗余动态的模型。为解决此问题，我们引入了一项辅助指标——共平滑度的少样本版本。该方法通过使用更少的试次数据，从潜变量向数据中预留的通道进行回归分析。结果表明，在接近最优共平滑度的模型中，具有冗余动态的模型在少样本共平滑度任务上的表现，要逊于那些不包含此类动态的"最小化"模型。我们还从解析角度揭示了该现象的产生机理。我们进一步在真实神经数据上使用两种前沿方法——LFADS和STNDT——验证了研究结论。在缺乏真实数据的情况下，我们提出了一种量化冗余动态的代理指标：通过对所有高共平滑度模型对的潜变量进行交叉解码，识别出具有最小冗余动态的模型。我们发现少样本共平滑度性能与这一新指标存在相关性。总之，我们提出了一种新颖的预测度量方法，旨在获得能更准确反映真实情况的潜变量，为潜变量动态推断提供了重要改进。