用变分潜高斯过程恢复单次试验动态
Variational Latent Gaussian Process for Recovering Single-Trial Dynamics from Population Spike Trains
摘要：
当受底层低维动力学支配时，同时记录的神经元群体的相互依赖性可以由少量共享因子或低维轨迹来解释。恢复这些潜在的轨迹，特别是从单次试验群体记录中恢复，可能有助于我们理解驱动神经计算的动力学。然而，由于生物物理的限制和噪声的尖峰列车，推断轨迹的数据是一个具有挑战性的统计问题。在这里，我们提出了一个实用和有效的推理方法，称为变分潜在高斯过程（vLGP）。该vLGP结合了生成模型与历史相关的点过程观察连同平滑之前的潜在轨迹。vLGP改进了早期的方法，用于恢复潜在的轨迹，假设观测模型不适合点过程或线性动力学。我们比较和验证vLGP的模拟数据集和人口记录从初级视觉皮层。在V1数据集中，我们发现vLGP实现了比以前的方法更高的性能，用于预测省略的尖峰序列，以及捕获视觉刺激空间的环形拓扑结构和噪声相关性。这些结果表明，vLGP是一个强大的方法，有可能揭示隐藏的神经动力学从大规模的神经记录。
英文摘要：
When governed by underlying low-dimensional dynamics, the interdependence of simultaneously recorded population of neurons can be explained by a small number of shared factors, or a low-dimensional trajectory. Recovering these latent trajectories, particularly from single-trial population recordings, may help us understand the dynamics that drive neural computation. However, due to the biophysical constraints and noise in the spike trains, inferring trajectories from data is a challenging statistical problem in general. Here, we propose a practical and efficient inference method, called the variational latent Gaussian process (vLGP). The vLGP combines a generative model with a history-dependent point process observation together with a smoothness prior on the latent trajectories. The vLGP improves upon earlier methods for recovering latent trajectories, which assume either observation models inappropriate for point processes or linear dynamics. We compare and validate vLGP on both simulated datasets and population recordings from the primary visual cortex. In the V1 dataset, we find that vLGP achieves substantially higher performance than previous methods for predicting omitted spike trains, as well as capturing both the toroidal topology of visual stimuli space, and the noise-correlation. These results show that vLGP is a robust method with a potential to reveal hidden neural dynamics from large-scale neural recordings.
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