# Spike-based primitives for graph algorithms

arXiv: https://arxiv.org/abs/1903.10574 · HTML: https://mlsanity.com/paper/1903.10574/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2019.

## Built on by (most cited first, 3 of 3)

- [SpikE: spike-based embeddings for multi-relational graph data](https://mlsanity.com/paper/2104.13398/) (2021)
- [Neuro-symbolic computing with spiking neural networks](https://mlsanity.com/paper/2208.02576/) (2022)
- [Neuromorphic Programming: Emerging Directions for Brain-Inspired Hardware](https://mlsanity.com/paper/2410.22352/) (2024)

## Abstract (arXiv)

In this paper we consider graph algorithms and graphical analysis as a new application for neuromorphic computing platforms. We demonstrate how the nonlinear dynamics of spiking neurons can be used to implement low-level graph operations. Our results are hardware agnostic, and we present multiple versions of routines that can utilize static synapses or require synapse plasticity.