scholarly journals On a Model of Associative Memory with Huge Storage Capacity

2017 ◽  
Vol 168 (2) ◽  
pp. 288-299 ◽  
Author(s):  
Mete Demircigil ◽  
Judith Heusel ◽  
Matthias Löwe ◽  
Sven Upgang ◽  
Franck Vermet
Keyword(s):  
Associative Memory ◽  
Storage Capacity

scholarly journals Enhancing Associative Memory Recall and Storage Capacity Using Confocal Cavity QED

2021 ◽  
Vol 11 (2) ◽  
Author(s):  
Brendan P. Marsh ◽  
Yudan Guo ◽  
Ronen M. Kroeze ◽  
Sarang Gopalakrishnan ◽  
Surya Ganguli ◽  
...  
Keyword(s):  
Associative Memory ◽  
Cavity Qed ◽  
Storage Capacity ◽  
Memory Recall ◽  
Confocal Cavity ◽  
And Storage

On fuzzy associative memory with multiple-rule storage capacity

1996 ◽  
Vol 4 (3) ◽  
pp. 375-384 ◽  
Author(s):  
Fu-Lai Chung ◽  
Tong Lee
Keyword(s):  
Associative Memory ◽  
Storage Capacity ◽  
Fuzzy Associative Memory

Studies on an Electronic Analog of a Recurrent Neural Network with Retrieval Phase Weight Adaptations

2005 ◽  
Vol 9 (2) ◽  
pp. 127-133
Author(s):  
Vishwanathan Mohan ◽  
◽  
Yashwant V. Joshi ◽  
Anand Itagi ◽  
Garipelli Gangadhar
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Character Recognition ◽  
Storage Capacity ◽  
Hopfield Neural Network ◽  
Learning System ◽  
Adaptive Law ◽  
Stability Issues ◽  
Electronic Implementation ◽  
Retrieval Phase

It is argued that weight adaptations even during retrieval phase can greatly enhance the performance of a neurodynamic associative memory. Our simulations with an electronic implementation of an associative memory showed that extending the Hopfield dynamics with an appropriate adaptive law in retrieval phase could give rise to significant improvements in storage capacity and computational reliability. Weights, which are supposed to encode the information stored in the Hopfield neural network, are usually held constant once training/storage is complete. In our case, weights also change during retrieval, hence losing information in the process, but resulting in much better retrieval of stored patterns. We describe and characterize the functional elements comprising the network, the learning system, and include the experimental results obtained from applying the network for character recognition in various noisy conditions. Stability issues emerging as a consequence of retrieval phase weight adaptation and implications of weights being used as transitory, intermediary variables are briefly discussed.

scholarly journals A bidirectional associative memory based on cortical spiking neurons using temporal coding

2021 ◽  
Author(s):  
Masood Zamani
Keyword(s):  
Neural Networks ◽  
Associative Memory ◽  
Action Potentials ◽  
Storage Capacity ◽  
Spike Timing ◽  
Temporal Coding ◽  
Spiking Neurons ◽  
Biological Plausibility ◽  
Bidirectional Associative Memory ◽  
Coding Scheme

In this thesis, we proposed a spiking bidirectional associative memory (BAM) using temporal coding. The information processing in biological neurons is beyond of[sic] that applied in the current Artificial Neural Networks (ANNs). The coding scheme used in ANNs known as “mean firing rate” cannot answer the fast and complex computations occurring in the cortex. In biological neural networks the information is coded and processed based on the timing of action potentials. To improve the biological plausibility of the standard BAM, we employed spiking neurons for its processing units, and information is presented to the BAM in the form of temporal coding. The neurons employed in the model are heterogeneous, and being able to generate various spike-timing patterns. Genetic Algorithm and Co-evolution are used for training, and the experiment results of the proposed BAM are compared to those of the standard BAM. The results show improvements in recall, storage capacity and convergence which are of interest to design a BAM.

THE HAMMING ASSOCIATIVE MEMORY REVISITED

2001 ◽  
Vol 12 (01) ◽  
pp. 79-90 ◽  
Author(s):  
M. ANDRECUT ◽  
M. K. ALI
Keyword(s):  
Associative Memory ◽  
Network Architecture ◽  
Cellular Network ◽  
Storage Capacity ◽  
Memory Element

Here, we review the classical Hamming associative memory and we discuss a cellular implementation. We show how the patterns to be stored can be superimposed or enfolded onto a single memory element with exponential storage capacity and how these memory elements can be organized in a cellular network architecture suitable for pattern association.

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