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

Ala Aboudib (1, 2) , Vincent Gripon (1, 2) , Baptiste Tessiau (2)

1 Lab-STICC_TB_CACS_IAS
2 ELEC - Département Electronique

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.

Mots clés: Cognitive modeling; Artificial neural networks; Relational algebra; Associative memory; Simulated annealing

Domaines: Intelligence artificielle [cs.AI]; Informatique; Electronique; Traitement du signal et de l'image [eess.SP]

Ala Aboudib, Vincent Gripon, Baptiste Tessiau. Implementing Relational-Algebraic Operators for Improving Cognitive Abilities in Networks of Neural Cliques. COGNITIVE 2015 : the 7th International Conference on Advanced Cognitive Technologies and Applications, Mar 2015, Nice, France. pp.36 - 41. ⟨hal-01216082⟩