论文标题
使用变异自动编码器的大脑集体动力学的生成嵌入
Generative embeddings of brain collective dynamics using variational autoencoders
论文作者
论文摘要
我们考虑了基于少数不同的观察值，在低维潜在空间中编码耦合动力学系统之间的成对相关性的问题。我们使用变异自动编码器（VAE）来嵌入耦合的非线性振荡器之间，这些非线性振荡器模拟大脑在尾流循环中呈现为二维流形。使用使用两个不同参数组合生成的样品训练A VAE导致嵌入，代表了集体动力学的整个曲目，以及基础连接网络的拓扑结构。我们首先遵循这种方法来推断大脑状态的轨迹，从觉醒到深度睡眠，从该轨迹的两个终点。接下来，我们表明相同的体系结构能够代表通过复杂网络拓扑结合的通用Landau-Stuart振荡器的成对相关性
We consider the problem of encoding pairwise correlations between coupled dynamical systems in a low-dimensional latent space based on few distinct observations. We used variational autoencoders (VAE) to embed temporal correlations between coupled nonlinear oscillators that model brain states in the wake-sleep cycle into a two-dimensional manifold. Training a VAE with samples generated using two different parameter combinations resulted in an embedding that represented the whole repertoire of collective dynamics, as well as the topology of the underlying connectivity network. We first followed this approach to infer the trajectory of brain states measured from wakefulness to deep sleep from the two endpoints of this trajectory; next, we showed that the same architecture was capable of representing the pairwise correlations of generic Landau-Stuart oscillators coupled by complex network topology
