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Entropic associative memory for manuscript symbols.

Rafael Morales1, Noé Hernández2,3, Ricardo Cruz2,4

  • 1Universidad de Guadalajara, SUV, Guadalajara, Jalisco, Mexico.

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Summary

This study introduces an entropic digital memory system for storing and retrieving manuscript symbols. This novel associative memory framework demonstrates efficient recall, even with incomplete information, offering potential for practical applications.

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Area of Science:

  • Cognitive Science
  • Computer Science
  • Information Theory

Background:

  • Existing associative memory models, including neural networks, have limitations in handling incomplete or noisy data.
  • The need for robust memory systems capable of efficient storage, recognition, and retrieval of symbolic information is critical.
  • Declarative memory systems require mechanisms for direct rejection of irrelevant cues and parallel processing capabilities.

Purpose of the Study:

  • To propose and evaluate an entropic digital memory framework for manuscript symbols (letters and numerals).
  • To investigate the role of entropy in memory operations, specifically the trade-off between precision and recall.
  • To demonstrate the system's capability in retrieving objects with complete and incomplete information, including occlusions.

Main Methods:

  • Representation of manuscript symbols in Associative Memory Registers with associated entropy.
  • Implementation of memory recognition obeying an entropy trade-off between precision and recall.
  • Development of a constructive memory retrieval operation with direct rejection of non-contained cues.
  • Utilizing parallel computations for memory operations.

Main Results:

  • The entropic digital memory successfully stores, recognizes, and retrieves manuscript symbols.
  • The entropy level directly impacts the quality of retrieved objects, demonstrating an entropy-precision-recall trade-off.
  • The system effectively rejects irrelevant cues without requiring a search, enhancing efficiency.
  • Demonstrated retrieval of objects even with severe occlusions, highlighting robustness with incomplete information.

Conclusions:

  • The proposed entropic associative memory offers a viable framework for computational models of natural memory.
  • The system's ability to handle incomplete information and its efficient retrieval mechanisms show potential for practical applications.
  • This approach provides a novel perspective contrasting with traditional neural network models of associative memory.