Implementing Holographic Reduced Representations for Spiking Neural Networks

Neuromorphic Computing surpasses conventional von Neumann architectures in terms of energy efficiency, parallelisation, scalability, and stochasticity. Given the inherent structure of neurons and synapses, neuromorphic computers can be directly implemented as spiking neural networks. Despite these advantages, neuromorphic computing applications are hitherto limited to benchmark datasets and empirical demonstrations. This is primarily due to the lack of a unifying computing framework that designates a middle-layer abstraction between the actual neuromorphic computing and the required application functionality. Drawing on the distributed vector representation of symbolic and numerical data structures and robust dual interface with diverse operational primitives, Vector Symbolic Architectures (VSA) have been positioned as a suitable candidate to address this middle-layer void. In this paper, we explore the potential of VSA as an intermediary abstraction layer to advance practical neuromorphic computing applications. We introduce a novel vectorised framework that efficiently processes parallel streams of spiking data by combining and computing them through VSA for real-time downstream learning tasks, leveraging spike latency encoding. Our implementation utilises containerised methods within Lava, an open-source framework for neuromorphic computing.