Neural networks as fuzzy logic formulas

Abstract

Neural networks are a fundamental aspect of modern artificial intelligence, playing a key role in various important machine learning architectures including transformers and graph neural networks. Recently, logical characterisations have been used to study the expressive power of many machine learning architectures, but logical characterisations of plain neural networks have received less attention. In this paper, we provide fuzzy logic characterisations of rational-weight ReLU-activated neural networks via Rational Pavelka logic ($\mathrm{RPL}$) and an extension of $\mathrm{RPL}$ called $\mathrm{RPL}(\odot {)}_{\le 1}$, as well as two fragments of $L\hat{I}\frac{1}{2}$ called $L\hat{I}\frac{1}{2}({\to }_P^{-}{)}_{\le 1}$ and $L\hat{I}\frac{1}{2}({\odot }^{-},{\to }_P^{-})$. The activation values of the neural networks are allowed to be arbitrary real numbers. We also provide fuzzy logic characterisations of a generalised polynomial ring over $\mathbb{Q}$ in countably many variables where the use of the ReLU-function is permitted via the fuzzy logic $\mathrm{RPL}(\odot )$ and a fragment of $L\hat{I}\frac{1}{2}$ called $L\hat{I}\frac{1}{2}({\to }_P^{-})$.