Neural generative models can be used to learn complex probability
distributions from data, to sample from them, and to produce probability
density estimates. We propose a computational framework for developing neural
generative models inspired by the theory of predictive processing in the brain.
According to predictive processing theory, the neurons in the brain form a
hierarchy in which neurons in one level form expectations about sensory inputs
from another level. These neurons update their local models based on
differences between their expectations and the observed signals. In a similar
way, artificial neurons in our generative models predict what neighboring
neurons will do, and adjust their parameters based on how well the predictions
matched reality. In this work, we show that the neural generative models
learned within our framework perform well in practice across several benchmark
datasets and metrics and either remain competitive with or significantly
outperform other generative models with similar functionality (such as the
variational auto-encoder).

提出了一种基于预测处理理论的神经生成模型计算框架，通过构建神经元层次结构，预测邻居神经元的工作并根据预测与观测结果的差异更新神经元参数来训练模型，实验结果表明，该模型在多个基准数据集和度量标准上表现出色，并具有与功能类似的其他生成模型相媲美甚至优越的性能。