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Associative Memories via Predictive Coding.

Tommaso Salvatori1, Yuhang Song1,2, Yujian Hong1

  • 1Department of Computer Science, University of Oxford, UK.

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|June 6, 2022
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Summary
This summary is machine-generated.

This study introduces a new hierarchical generative network for associative memory, outperforming existing models in retrieving corrupted or incomplete data. The model demonstrates high accuracy in recalling patterns, particularly for natural images, mimicking hippocampal function.

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Machine Learning

Background:

  • Associative memories are crucial for human intelligence, storing and retrieving sensory information.
  • Computational models of associative memory have been developed for decades.
  • Existing models like autoencoders and Hopfield networks have limitations in retrieval accuracy and robustness.

Purpose of the Study:

  • To present a novel neural model for associative memory based on a hierarchical generative network.
  • To train the model using predictive coding, inspired by cortical information processing.
  • To evaluate the model's retrieval capabilities on corrupted and incomplete data.

Main Methods:

  • Developed a hierarchical generative network architecture.
  • Employed predictive coding for error-based learning.
  • Conducted retrieval experiments using corrupted and incomplete data points.
  • Performed comparative analysis against autoencoders and Hopfield networks.

Main Results:

  • The novel model significantly outperforms popular associative memory models in retrieval accuracy and robustness.
  • Achieved remarkable accuracy in completing partial data points, especially for natural image datasets like ImageNet.
  • Demonstrated high performance even with a minimal fraction of original image pixels.

Conclusions:

  • The proposed model offers a plausible computational framework for studying memory learning and retrieval in the brain.
  • The model closely mimics the hippocampus's function as a memory index and generative model.
  • This work advances the understanding of artificial associative memory systems and their biological plausibility.