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的强大工具。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“协同平滑”预测框架的局限性，并提出了一种改进的少样本预测方法，以促进更准确的潜动态建模。通过采用隐马尔可夫模型的师生设置，我们证明了高协同平滑的模型空间可能包含那些在潜表示中具有任意无关动态的模型。为解决此问题，我们引入了一种辅助度量——协同平滑的少样本版本。该方法使用更少的试次，从潜变量对数据中留出的通道进行回归。结果表明，在具有接近最优协同平滑的模型中，那些含有无关动态的模型在少样本协同平滑上的表现，要逊于没有此类动态的“最小”模型。我们还对这一现象的起源提供了分析性见解。我们进一步使用两种前沿方法——LFADS和STNDT——在真实神经数据上验证了我们的发现。在缺乏真实数据的情况下，我们提出了一种代理度量来量化无关动态。通过对所有高协同平滑模型对的潜变量进行交叉解码，我们识别出具有最少无关动态的模型。我们发现少样本协同平滑性能与这一新度量之间存在相关性。总之，我们提出了一种新颖的预测度量，旨在产生能更准确反映真实情况的潜变量，为潜动态推断提供了重要改进。