Spike-based primitives for graph algorithms

2019/03/25 by Kathleen E. Hamilton, Hamilton, Kathleen E., Tiffany M. Mintz +3 · 1 citation
Engineering · Neuroscience · #Advanced Memory and Neural Computing #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Computer and information sciences #FOS: Physical sciences #Ferroelectric and Negative Capacitance Devices #Neural and Evolutionary Computing (cs.NE) #Neural dynamics and brain function

paper · pdf · doi:10.48550/arxiv.1903.10574

openalex publication_date 2019/03/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• A Survey of Neuromorphic Computing and Neural Networks in Hardware

Cited by

• Pipelining Kruskal's: A Neuromorphic Approach for Minimum Spanning Tree

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