short-paper

Neuro-symbolic computing with spiking neural networks

Authors:

Josep Soler Garrido,

Victor Caceres Chian,

Marcel Hildebrandt,

Thomas RunklerAuthors Info & Claims

ICONS '22: Proceedings of the International Conference on Neuromorphic Systems 2022

Article No.: 30, Pages 1 - 4

https://doi.org/10.1145/3546790.3546824

Published: 07 September 2022 Publication History

Abstract

Knowledge graphs are an expressive and widely used data structure due to their ability to integrate data from different domains in a sensible and machine-readable way. Thus, they can be used to model a variety of systems such as molecules and social networks. However, it still remains an open question how symbolic reasoning could be realized in spiking systems and, therefore, how spiking neural networks could be applied to such graph data. Here, we extend previous work on spike-based graph algorithms by demonstrating how symbolic and multi-relational information can be encoded using spiking neurons, allowing reasoning over symbolic structures like knowledge graphs with spiking neural networks. The introduced framework is enabled by combining the graph embedding paradigm and the recent progress in training spiking neural networks using error backpropagation. The presented methods are applicable to a variety of spiking neuron models and can be trained end-to-end in combination with other differentiable network architectures, which we demonstrate by implementing a spiking relational graph neural network.

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Cited By

Bhuyan BRamdane-Cherif ATomar RSingh T(2024)Neuro-symbolic artificial intelligence: a surveyNeural Computing and Applications10.1007/s00521-024-09960-z36:21(12809-12844)Online publication date: 6-Jun-2024
https://doi.org/10.1007/s00521-024-09960-z
Bhuyan BRamdane-Cherif ASingh TTomar RBhuyan BRamdane-Cherif ASingh TTomar R(2024)The Emergence of Neuro-Symbolic Artificial IntelligenceNeuro-Symbolic Artificial Intelligence10.1007/978-981-97-8171-3_1(3-15)Online publication date: 23-Dec-2024
https://doi.org/10.1007/978-981-97-8171-3_1
Nilsson MSchelén OLindgren ABodin UPaniagua CDelsing JSandin F(2023)Integration of neuromorphic AI in event-driven distributed digitized systems: Concepts and research directionsFrontiers in Neuroscience10.3389/fnins.2023.107443917Online publication date: 17-Feb-2023
https://doi.org/10.3389/fnins.2023.1074439

Index Terms

Neuro-symbolic computing with spiking neural networks
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
    1. Learning paradigms

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cover image ACM Other conferences

ICONS '22: Proceedings of the International Conference on Neuromorphic Systems 2022

July 2022

213 pages

ISBN:9781450397896

DOI:10.1145/3546790

Editors:
Thomas E. Potok
Oak Ridge National Laboratory
,
Catherine Schuman
University of Tennessee Knoxville
,
Melika Payvand
University of Zurich
,
Prasanna Date
Oak Ridge National Laboratory
,
Shruti Kulkarni
Oak Ridge National Laboratory
,
Yiran Chen
Duke University
,
Robinson Pino
U.S. Department of Energy
,
Brad Aimone
Sandia National Laboratories
,
Mutsumi Kimura
Ryukoku University
,
Gregory Cohen
Western Sydney University
,
David Whittaker
Elm Street Ventures
,
Gordon Hirsch Wilson
Rain Neuromorphics

Copyright © 2022 ACM.

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Published: 07 September 2022

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ICONS

ICONS: International Conference on Neuromorphic Systems

July 27 - 29, 2022

TN, Knoxville, USA

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Cited By

Bhuyan BRamdane-Cherif ATomar RSingh T(2024)Neuro-symbolic artificial intelligence: a surveyNeural Computing and Applications10.1007/s00521-024-09960-z36:21(12809-12844)Online publication date: 6-Jun-2024
https://doi.org/10.1007/s00521-024-09960-z
Bhuyan BRamdane-Cherif ASingh TTomar RBhuyan BRamdane-Cherif ASingh TTomar R(2024)The Emergence of Neuro-Symbolic Artificial IntelligenceNeuro-Symbolic Artificial Intelligence10.1007/978-981-97-8171-3_1(3-15)Online publication date: 23-Dec-2024
https://doi.org/10.1007/978-981-97-8171-3_1
Nilsson MSchelén OLindgren ABodin UPaniagua CDelsing JSandin F(2023)Integration of neuromorphic AI in event-driven distributed digitized systems: Concepts and research directionsFrontiers in Neuroscience10.3389/fnins.2023.107443917Online publication date: 17-Feb-2023
https://doi.org/10.3389/fnins.2023.1074439

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