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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, sparsedoi:10.5167/uzh-219676 fatcat:bc43j2vjtzgcdn7zprid6vkawi