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The ability to encode and manipulate data structures with distributed neural representations could qualitatively enhance the capabilities of traditional neural networks by supporting rule-based symbolic reasoning, a central property of cognition. Here we show how this may be accomplished within the framework of Vector Symbolic Architectures (VSA) (Plate, 1991; Gayler, 1998; Kanerva, 1996), whereby data structures are encoded by combining high-dimensional vectors with operations that together form an algebra on the space of distributed representations. In particular, we propose an efficient solution to a hard combinatorial search problem that arises when decoding elements of a VSA data structure: the factorization of products of multiple code vectors. Our proposed algorithm, called a resonator network, is a new type of recurrent neural network that interleaves VSA multiplication operations and pattern completion. We show in two examples -- parsing of a tree-like data structure and parsing of a visual scene -- how the factorization problem arises and how the resonator network can solve it. More broadly, resonator networks open the possibility to apply VSAs to myriad artificial intelligence problems in real-world domains. A companion paper (Kent et al., 2020) presents a rigorous analysis and evaluation of the performance of resonator networks, showing it out-performs alternative approaches.