Systems neuroscience relies on two complementary views of neural data, characterized by single neuron tuning curves and analysis of population activity. These two perspectives combine elegantly in neural latent variable models that constrain the relationship between latent variables and neural activity, modeled by simple tuning curve functions. This has recently been demonstrated using Gaussian processes, with applications to realistic and topologically relevant latent manifolds. Those and previous models, however, missed crucial shared coding properties of neural populations. We propose feature sharing across neural tuning curves which significantly improves performance and helps optimization. We also propose a solution to the ensemble detection problem, where different groups of neurons, i.e., ensembles, can be modulated by different latent manifolds. Achieved through a soft clustering of neurons during training, this allows for the separation of mixed neural populations in an unsupervised manner. These innovations lead to more interpretable models of neural population activity that train well and perform better even on mixtures of complex latent manifolds. Finally, we apply our method on a recently published grid cell dataset, and recover distinct ensembles, infer toroidal latents and predict neural tuning curves in a single integrated modeling framework.

翻译：系统神经科学依赖于对神经数据的两种互补视角：单个神经元调谐曲线分析与群体活动分析。这两种视角在神经潜在变量模型中得以优雅结合——该模型通过简单的调谐曲线函数约束潜在变量与神经活动之间的关系。近来，高斯过程已成功应用于此类建模，并扩展到具有现实意义与拓扑相关性的潜在流形。然而，这些模型及先前研究均未囊括神经群体关键性的共享编码特性。我们提出跨神经调谐曲线的特征共享机制，显著提升模型性能并优化过程。同时，我们针对集群检测问题提出解决方案：不同神经元组（即集群）可受不同潜在流形调制。通过在训练过程中对神经元进行软聚类，该方法能够以无监督方式分离混合神经群体。这些创新使神经群体活动模型更具可解释性，且即使在复杂潜在流形混合场景下仍能高效训练并获得更优性能。最终，我们将该方法应用于最近发布的网格细胞数据集，在统一建模框架中成功恢复不同集群、推断环形潜在变量并预测神经调谐曲线。