Extended Poisson Gaussian-Process Latent Variable Model for Unsupervised Neural Decoding.
Extended Poisson Gaussian-Process Latent Variable Model for Unsupervised Neural Decoding.
复制标题
用于无监督神经解码的扩展泊松高斯过程潜变量模型。
DOI：
10.1101/2024.03.04.583340
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Kemere,Caleb
中科院分区：
文献类型：
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作者：
Luo,DellaDaiyi;Giri,Bapun;Diba,Kamran;Kemere,Caleb
神经活动的降维为无监督的神经解码铺平了道路，通过将内部神经模式再激活的测量从外部变量调谐的测量中分离出来。仅假设潜在动态和内部调谐曲线的平滑性，泊松高斯过程潜在变量模型（P-GPLVM; Wu等人，）是发现高维spike序列低维潜在结构的有力工具。然而，当给定新的神经数据时，原始模型缺乏在学习的潜在空间中推断其潜在轨迹的方法，限制了其估计神经再激活的能力。在这里，我们扩展了P-GPLVM，以支持对受先前学习的平滑度和映射信息约束的新数据进行潜在变量推断。我们还描述了一个原则性的方法，用于约束潜变量推理的时间压缩模式的活动，如那些发现在人口突发事件在海马尖波波纹，以及评估神经模式激活的有效性和推断的编码经验的指标。将这些方法应用于主动迷宫探索期间的海马系综记录，我们复制了P-GPLVM学习编码动物位置的潜在空间的结果。我们进一步证明，这个潜在的空间可以区分一个迷宫上下文从另一个。通过在运行过程中推断新神经数据的潜在变量，根据训练数据流形中其附近神经轨迹编码的经验的相似性，观察到某些神经模式重新激活。最后，神经模式的再激活也可以针对群体突发事件期间的神经活动进行估计，从而允许识别多功能行为和更一般经验的重放事件。因此，我们对P-GPLVM框架的扩展可以用于神经活动的无监督分析，以回答与科学发现相关的关键问题。
Dimension reduction on neural activity paves a way for unsupervised neural decoding by dissociating the measurement of internal neural pattern reactivation from the measurement of external variable tuning. With assumptions only on the smoothness of latent dynamics and of internal tuning curves, the Poisson gaussian-process latent variable model (P-GPLVM; Wu et al., ) is a powerful tool to discover the low-dimensional latent structure for high-dimensional spike trains. However, when given novel neural data, the original model lacks a method to infer their latent trajectories in the learned latent space, limiting its ability for estimating the neural reactivation. Here, we extend the P-GPLVM to enable the latent variable inference of new data constrained by previously learned smoothness and mapping information. We also describe a principled approach for the constrained latent variable inference for temporally compressed patterns of activity, such as those found in population burst events during hippocampal sharp-wave ripples, as well as metrics for assessing the validity of neural pattern reactivation and inferring the encoded experience. Applying these approaches to hippocampal ensemble recordings during active maze exploration, we replicate the result that P-GPLVM learns a latent space encoding the animal’s position. We further demonstrate that this latent space can differentiate one maze context from another. By inferring the latent variables of new neural data during running, certain neural patterns are observed to reactivate, in accordance with the similarity of experiences encoded by its nearby neural trajectories in the training data manifold. Finally, reactivation of neural patterns can be estimated for neural activity during population burst events as well, allowing the identification for replay events of versatile behaviors and more general experiences. Thus, our extension of the P-GPLVM framework for unsupervised analysis of neural activity can be used to answer critical questions related to scientific discovery.