Multi-modal Associative Storage and Retrieval Using Hopfield Auto-associative Memory Network

2019 ◽  
pp. 57-75
Author(s):  
Rachna Shriwas ◽  
Prasun Joshi ◽  
Vandana M. Ladwani ◽  
V. Ramasubramanian
Keyword(s):  
Associative Memory ◽  
Storage And Retrieval ◽  
Memory Network

A Reconfigurable Bidirectional Associative Memory Network with Memristor Bridge

2021 ◽  
Author(s):  
Yingying Li ◽  
Junrui Li ◽  
Jie Li ◽  
Shukai Duan ◽  
Lidan Wang ◽  
...  
Keyword(s):  
Associative Memory ◽  
Bidirectional Associative Memory ◽  
Memory Network

On Neural Associative Memory Structures: Storage and Retrieval of Sequences in a Chain of Tournaments

2021 ◽  
pp. 1-30
Author(s):  
Asieh Abolpour Mofrad ◽  
Samaneh Abolpour Mofrad ◽  
Anis Yazidi ◽  
Matthew Geoffrey Parker
Keyword(s):  
Neural Networks ◽  
Associative Memory ◽  
Large Set ◽  
Memory Architecture ◽  
Retrieval Performance ◽  
Storage And Retrieval ◽  
Memory Efficiency ◽  
Robustness To Noise ◽  
A Chain ◽  
Sequence Storage

Abstract 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 backward—in 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.

Oscillatory associative memory network with perfect retrieval

2004 ◽  
Vol 197 (1-2) ◽  
pp. 134-148 ◽  
Author(s):  
Takashi Nishikawa ◽  
Frank C. Hoppensteadt ◽  
Ying-Cheng Lai
Keyword(s):  
Associative Memory ◽  
Memory Network

Associative Memory Network with Dynamic Synapses

2014 ◽  
pp. 479-483 ◽  
Author(s):  
Yuichi Katori ◽  
Yosuke Otsubo ◽  
Masato Okada ◽  
Kazuyuki Aihara
Keyword(s):  
Associative Memory ◽  
Dynamic Synapses ◽  
Memory Network

A Proposal of Multi-Module Network for Association of Patterns and Symbols

1996 ◽  
Vol 8 (4) ◽  
pp. 345-350 ◽  
Author(s):  
Yoichiro Hattori ◽  
◽  
Takeshi Furuhashi ◽  
Yoshiki Uchikawa
Keyword(s):  
Associative Memory ◽  
Construction Method ◽  
System Construction ◽  
Module Network ◽  
Brain System ◽  
Brain Functions ◽  
Construction Methods ◽  
Memory Network ◽  
Pattern Layer ◽  
The Brain

Our brain is constructed of modules, which share brain functions and work through stimulus from outside by cooperating and competing with each other. By analogizing the brain system, construction methods of artificial multi-module networks have been proposed. The authors have proposed a 2-layer associative memory network consisting of a pattern layer and a symbol layer. This paper presents a construction method for a multimodule network using the 2-layer models. We examine the association of facial patterns by the network.

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