We consider the problem of how a trusted, but computationally bounded agent (a 'verifier') can learn to interact with one or more powerful but untrusted agents ('provers') in order to solve a given task. More specifically, we study the case in which agents are represented using neural networks and refer to solutions of this problem as neural interactive proofs. First we introduce a unifying framework based on prover-verifier games Anil et al. (2021), which generalises previously proposed interaction protocols. We then describe several new protocols for generating neural interactive proofs, and provide a (theoretical) comparison of both new and existing approaches. In so doing, we aim to create a foundation for future work on neural interactive proofs and their application in building safer AI systems.