We study the expressive power and computational properties of first-order logic and its extensions under the semiring semantics originating from the seminal work of Green, Karvounarakis, and Tannen. While semiring semantics is currently extensively used, e.g., in the study of provenance in database theory and description logic, a comprehensive computational analysis of these logics acting over general semirings is still lacking. We analyse expressivity, and complexity of model-checking of first-order formulas in this framework, providing characterizations in terms of generalized Blum–Shub–Smale machines over semirings. We also show a variant of Fagin's theorem, i.e., a logical characterization of nondeterministic polynomial time over semirings using a version of existential second-order logic. We further generalize Cook's theorem for the semiring framework and show that propositional satisfiability in the semiring semantics is complete for this notion of NP, and that the true existential first-order theory of the semiring is complete for its Boolean fragment.

The training problem of neural networks (NNs) is known to be ER-complete with respect to ReLU and linear activation functions. We show that the training problem for NNs equipped with arbitrary activation functions is polynomial-time bireducible to the existential theory of the reals extended with the corresponding activation functions. For effectively continuous activation functions (e.g., the sigmoid function), we obtain an inclusion to low levels of the arithmetical hierarchy. Consequently, the sigmoid activation function leads to the existential theory of the reals with the exponential function, and hence the decidability of training NNs using the sigmoid activation function is equivalent to the decidability of the existential theory of the reals with the exponential function, a long-standing open problem. In contrast, we obtain that the training problem is undecidable if sinusoidal activation functions are considered.