Neuro-symbolic computing with spiking neural networks
- URL: http://arxiv.org/abs/2208.02576v1
- Date: Thu, 4 Aug 2022 10:49:34 GMT
- Title: Neuro-symbolic computing with spiking neural networks
- Authors: Dominik Dold, Josep Soler Garrido, Victor Caceres Chian, Marcel
Hildebrandt, Thomas Runkler
- Abstract summary: We extend previous work on spike-based graph algorithms by demonstrating how symbolic and multi-relational information can be encoded using spiking neurons.
The introduced framework is enabled by combining the graph embedding paradigm and the recent progress in training spiking neural networks using error backpropagation.
- Score: 0.6035125735474387
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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