# Hyperdimensional Computing with Spiking-Phasor Neurons

arXiv: https://arxiv.org/abs/2303.00066 · HTML: https://mlsanity.com/paper/2303.00066/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2023.

## Built on by (most cited first, 4 of 4)

- [Learning from Hypervectors: A Survey on Hypervector Encoding](https://mlsanity.com/paper/2308.00685/) (2023)
- [Analogical Reasoning Within a Conceptual Hyperspace](https://mlsanity.com/paper/2411.08684/) (2024)
- [Improved Cleanup and Decoding of Fractional Power Encodings](https://mlsanity.com/paper/2412.00488/) (2024)
- [Developing a Foundation of Vector Symbolic Architectures Using Category Theory](https://mlsanity.com/paper/2501.05368/) (2025)

## Abstract (arXiv)

Vector Symbolic Architectures (VSAs) are a powerful framework for representing compositional reasoning. They lend themselves to neural-network implementations, allowing us to create neural networks that can perform cognitive functions, like spatial reasoning, arithmetic, symbol binding, and logic. But the vectors involved can be quite large, hence the alternative label Hyperdimensional (HD) computing. Advances in neuromorphic hardware hold the promise of reducing the running time and energy footprint of neural networks by orders of magnitude. In this paper, we extend some pioneering work to run VSA algorithms on a substrate of spiking neurons that could be run efficiently on neuromorphic hardware.