A probabilistic framework for task-aligned intra- and inter-area neural manifold estimation
A probabilistic framework for task-aligned intra- and inter-area neural manifold estimation
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发表时间：
2022-09
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通讯作者：
Edoardo Balzani;Jean-Paul Noel;Pedro Herrero-Vidal;D. Angelaki;Cristina Savin
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作者：
Edoardo Balzani;Jean-Paul Noel;Pedro Herrero-Vidal;D. Angelaki;Cristina Savin
潜在的流形提供了一个紧凑的表征神经群体的活动和共享的共同变异跨脑区。尽管如此，现有的用于提取神经流形的统计工具在任务变量的潜伏期的可解释性方面面临限制，并且很难应用于没有重复试验的数据集。在这里，我们提出了一个新的概率框架，允许解释分区内和跨地区的自然行为的背景下的人口变异。我们的任务对齐流形估计（TAME-GP）方法通过（1）将可变性明确划分为私有和共享源，（2）使用泊松噪声模型，以及（3）以高斯过程先验的形式引入潜在轨迹的时间平滑，扩展了分层PCA的概率变体。该TAME-GP图形模型允许对局部人群响应中的任务相关变异性以及脑区之间的共享共变性进行稳健估计。我们证明了我们的估计模型和生物动机的模拟数据内的效率。我们还将其应用于猴子闭环虚拟导航任务中的神经记录，证明TAME-GP能够以单次试验分辨率捕获有意义的区域内和区域间神经变异性。
Latent manifolds provide a compact characterization of neural population activity and of shared co-variability across brain areas. Nonetheless, existing statistical tools for extracting neural manifolds face limitations in terms of interpretability of latents with respect to task variables, and can be hard to apply to datasets with no trial repeats. Here we propose a novel probabilistic framework that allows for interpretable partitioning of population variability within and across areas in the context of naturalistic behavior. Our approach for task aligned manifold estimation (TAME-GP) extends a probabilistic variant of demixed PCA by (1) explicitly partitioning variability into private and shared sources, (2) using a Poisson noise model, and (3) introducing temporal smoothing of latent trajectories in the form of a Gaussian Process prior. This TAME-GP graphical model allows for robust estimation of task-relevant variability in local population responses, and of shared co-variability between brain areas. We demonstrate the efficiency of our estimator on within model and biologically motivated simulated data. We also apply it to neural recordings in a closed-loop virtual navigation task in monkeys, demonstrating the capacity of TAME-GP to capture meaningful intra- and inter-area neural variability with single trial resolution.