330 research outputs found
Implementing Relational-Algebraic Operators for Improving Cognitive Abilities in Networks of Neural Cliques
International audienceAssociative 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
A Comparative Study of Sparse Associative Memories
We study various models of associative memories with sparse information, i.e.
a pattern to be stored is a random string of s and s with about
s, only. We compare different synaptic weights, architectures and retrieval
mechanisms to shed light on the influence of the various parameters on the
storage capacity.Comment: 28 pages, 2 figure
The sparse Blume-Emery-Griffiths model of associative memories
We analyze the Blume-Emery-Griffiths (BEG) associative memory with sparse
patterns and at zero temperature. We give bounds on its storage capacity
provided that we want the stored patterns to be fixed points of the retrieval
dynamics. We compare our results to that of other models of sparse neural
networks and show that the BEG model has a superior performance compared to
them.Comment: 23 p
On Neural Associative Memory Structures: Storage and Retrieval of Sequences in a Chain of Tournaments
Associative memories enjoy many interesting properties in terms of error correction capabilities, robustness to noise, storage capacity, and retrieval performance, and their usage spans over a large set of applications. In this letter, we investigate and extend tournament-based neural networks, originally proposed by Jiang, Gripon, Berrou, and Rabbat (2016), a novel sequence storage associative memory architecture with high memory efficiency and accurate sequence retrieval. We propose a more general method for learning the sequences, which we call feedback tournament-based neural networks. The retrieval process is also extended to both directions: forward and backwardâin other words, any large-enough segment of a sequence can produce the whole sequence. Furthermore, two retrieval algorithms, cache-winner and explore-winner, are introduced to increase the retrieval performance. Through simulation results, we shed light on the strengths and weaknesses of each algorithm.publishedVersio
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