Predictive coding graphs (PCGs) are a recently introduced generalization to predictive coding networks, a neuroscience-inspired probabilistic latent variable model. Here, we prove how PCGs define a mathematical superset of feedforward artificial neural networks (multilayer perceptrons). This positions PCNs more strongly within contemporary machine learning (ML), and reinforces earlier proposals to study the use of non-hierarchical neural networks for ML tasks, and more generally the notion of topology in neural networks.

翻译：预测编码图（PCGs）是近期对预测编码网络的一种推广，后者是一种受神经科学启发的概率潜变量模型。本文证明了PCGs如何定义前馈人工神经网络（多层感知机）的数学超集。这一结论将预测编码网络更牢固地定位于当代机器学习（ML）领域，并强化了先前关于研究非层级神经网络在ML任务中应用的提议，更广泛地强化了神经网络拓扑结构的概念。