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A study of retrieval algorithms of sparse messages in networks of neural cliques

A. Aboudib, V. Gripon and X. Jiang, "A study of retrieval algorithms of sparse messages in networks of neural cliques," in Proceedings of Cognitive 2014, pp. 140--146, May 2014.

Associative memories are data structures addressed using part of the content rather than an index. They offer good fault reliability and biological plausibility. Among different families of associative memories, sparse ones are known to offer the best efficiency (ratio of the amount of bits stored to that of bits used by the network itself). Their retrieval process performance has been shown to benefit from the use of iterations. In this paper, we introduce several algorithms to enhance the performance of the retrieval process in recently proposed sparse associative memories based on binary neural networks. We show that these algorithms provide better performance than existing techniques and discuss their biological plausibility. We also analyze the required number of iterations and derive corresponding curves.

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Bibtex
@inproceedings{AboGriJia20145,
  author = {Ala Aboudib and Vincent Gripon and Xiaoran
Jiang},
  title = {A study of retrieval algorithms of sparse
messages in networks of neural cliques},
  booktitle = {Proceedings of Cognitive 2014},
  year = {2014},
  pages = {140--146},
  month = {May},
}




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