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ThinkMind // COGNITIVE 2017, The Ninth International Conference on Advanced Cognitive Technologies and Applications // View article cognitive_2017_1_20_40031


Performance of Neural Clique Networks Subject to Synaptic Noise

Authors:
Eliott Coyac
Vincent Gripon
Charlotte Langlais
Claude Berrou

Keywords: associative memories; neural clique networks; synaptic noise

Abstract:
Artificial neural networks are so-called because they are supposed to be inspired from the brain and from the ways the neurons work. While some networks are used purely for computational purpose and do not endeavor to be a plausible representation of what happens in the brain, such as deep learning neural networks, others do. However, the question of the noise in the brain and its impact on the functioning of those networks has been little-studied. For example, it is widely known that synapses misfire with a significant probability. We model this noise and study its impact on associative memories powered by neural networks: neural clique networks and Hopfield networks as a reference point. We show that synaptic noise can in fact slightly improve the performance of the decoding p

Pages: 4 to 9

Copyright: Copyright (c) IARIA, 2017

Publication date: February 19, 2017

Published in: conference

ISSN: 2308-4197

ISBN: 978-1-61208-531-9

Location: Athens, Greece

Dates: from February 19, 2017 to February 23, 2017

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