Vector Symbolic Architectures (VSA) were first proposed as connectionist models for symbolic reasoning, leveraging parallel and in-memory computing in brains and neuromorphic hardware that enable low-power, low-latency applications. Symbols are defined in VSAs as points/vectors in a high-dimensional neural state-space. For spiking neuromorphic hardware (and brains), particularly sparse representations are of interest, as they minimize the number of costly spikes. Furthermore, sparse

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... ons can be efficiently stored in simple Hebbian auto-associative memories, which provide error correction in VSAs. However, the binding of spatially sparse representations is computationally expensive because it is not local to corresponding pairs of neurons as in VSAs with dense vectors. Here, we present the first implementation of a sparse VSA on spiking neuromorphic hardware, specifically Intel's neuromorphic research chip Loihi. To reduce the cost of binding, a delay line and coincidence detection are used, trading off space with time. We show as proof of principle that our network on Loihi can perform the binding operation of a classical analogical reasoning task and discuss the cost of different sparse binding operations. The proposed binding mechanism can be used as a building block for VSA-based architectures on neuromorphic hardware.