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Impact du bruit synaptique sur les performances des réseaux de cliques neurales

E. Coyac, V. Gripon and C. Langlais, "Impact du bruit synaptique sur les performances des réseaux de cliques neurales," in Proceedings of the GRETSI conference, 2015.

Artificial neural networks are inspired by biological neural networks present in the brain, and biological plausibility is often used as an argument to validate or criticize a neural network proposal. However, the brain is a system with a lot of interferences and the behaviour of neural networks with respect to this noise has not often been studied. This paper introduces a model to represent noise inside the brain, and studies how neural clique networks respond to that noise. It is shown that the noise can improve the neural clique network performance by avoiding local minima. We also show the impact of this noise on the widely-known Hopfield networks.

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Bibtex
@inproceedings{CoyGriLan2015,
  author = {Eliott Coyac and Vincent Gripon and
Charlotte Langlais},
  title = {Impact du bruit synaptique sur les
performances des réseaux de cliques neurales},
  booktitle = {Proceedings of the GRETSI conference},
  year = {2015},
}




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