We show that nonlinear latent factors and structures in neural population activity can be modelled in a manner that allows for flexible dynamical inference, causally, non-causally and in the presence of missing neural observations. Further, the developed neural network model improves the prediction of neural activity, behaviour and latent neural structures.

中文翻译：

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一种能够根据神经群体活动进行灵活的非线性推理的神经网络

我们表明，神经群体活动中的非线性潜在因素和结构可以以允许灵活动态推理的方式进行建模，无论是因果关系、非因果关系还是在存在缺失神经观察的情况下。此外，开发的神经网络模型改进了对神经活动、行为和潜在神经结构的预测。