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Implementing Relational-Algebraic Operators for Improving Cognitive Abilities in Networks of Neural Cliques

Associative memories are devices capable of retrieving previously stored messages from parts of their content. They are used in a variety of applications including CPU caches, routers, intrusion detection systems, etc. They are also considered a good model for human memory, motivating the use of neural-based techniques. When it comes to cognition, it is important to provide such devices with the ability to perform complex requests, such as union, intersection, difference, projection and selection. In this paper, we extend a recently introduced associative memory model to perform relational algebra operations. We introduce new algorithms and discuss their performance which provides an insight on how the brain performs some high-level information processing tasks.

@inproceedings{AboGriTes20153,
  author = {Ala Aboudib and Vincent Gripon and
Baptiste Tessiau},
  title = {Implementing Relational-Algebraic Operators
for Improving Cognitive Abilities in Networks of
Neural Cliques},
  booktitle = {Proceedings of Cognitive},
  year = {2015},
  pages = {36--41},
  month = {March},
}