Spike-based primitives for graph algorithms

03/25/2019

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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.

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Spike-based primitives for graph algorithms

Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning

Connecting Spiking Neurons to a Spiking Memristor Network Changes the Memristor Dynamics

Efficient Neuromorphic Signal Processing with Loihi 2

Community detection with spiking neural networks for neuromorphic hardware

NeuSort: An Automatic Adaptive Spike Sorting Approach with Neuromorphic Models

SpikE: spike-based embeddings for multi-relational graph data

Simplified Klinokinesis using Spiking Neural Networks for Resource-Constrained Navigation on the Neuromorphic Processor Loihi

Spike-based primitives for graph algorithms

Related Research

Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning

Connecting Spiking Neurons to a Spiking Memristor Network Changes the Memristor Dynamics

Efficient Neuromorphic Signal Processing with Loihi 2

Community detection with spiking neural networks for neuromorphic hardware

NeuSort: An Automatic Adaptive Spike Sorting Approach with Neuromorphic Models

SpikE: spike-based embeddings for multi-relational graph data

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