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On Neural Associative Memory Structures: Storage and Retrieval of Sequences in a Chain of Tournaments
Asieh Abolpour Mofrad1, Samaneh Abolpour Mofrad2, Anis Yazidi3
1Selmer Center, Department of Informatics, University of Bergen, 5020 Bergen, Norway asieh.abolpour-mofrad@oslomet.no.
Neural Computation
|August 19, 2021
Summary
This study enhances tournament-based neural networks for sequence storage, introducing feedback learning and bidirectional retrieval. New algorithms improve associative memory performance and efficiency.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Neural Networks
Background:
- Associative memories offer robust error correction, noise resistance, and efficient storage.
- Tournament-based neural networks provide a novel architecture for sequence storage and retrieval.
Purpose of the Study:
- To extend tournament-based neural networks for improved sequence learning and retrieval.
- To introduce bidirectional retrieval capabilities and novel algorithms for enhanced performance.
Main Methods:
- Developed feedback tournament-based neural networks for generalized sequence learning.
- Implemented bidirectional (forward and backward) sequence retrieval.
- Introduced and evaluated 'cache-winner' and 'explore-winner' retrieval algorithms.
Main Results:
- Feedback learning enhances sequence memorization and recall accuracy.
- Bidirectional retrieval allows sequence reconstruction from partial inputs.
- Simulation results highlight the distinct performance characteristics of the new algorithms.
Conclusions:
- The proposed feedback tournament-based neural networks offer a more versatile and efficient associative memory.
- The novel retrieval algorithms provide tunable performance trade-offs for sequence recall.
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