This paper presents a novel form of associative content addressable (ACA) memory systems. The canonical model for ACA memory is the Hopfield network, which can only store approximately N patterns of N bits. The authors use developments from error-correcting codes (ECCs) to implement an ACA that can store e^N, N bit patterns. This is accomplished by using a bipartite expander graph, which is essentially a restricted Boltzmann machine (RBM) wherein the hidden nodes are actually clusters of units that are mutually inhibitory. The authors demonstrate that these networks have dynamics that can engage in error correction similar to ECCs, enabling the storage of exponentially many patterns. The reviewers agreed that this work was theoretically interesting and potentially relevant to neuroscience. The connections made between Hopfield nets, RBMs and ECCs are novel and worthy of presentation at NeurIPS. The reviewers had some concerns, but these were largely addressed in the authors' response. Therefore, the reviewers agreed that this paper should be accepted.