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A modular attractor associative memory with patchy connectivity and weight pruning.

Cristina Meli1, Anders Lansner

  • 1Department of Computational Biology (CB), School of Computer Science and Communication (CSC), Royal Institute of Technology (KTH) , Stockholm , Sweden.

Network (Bristol, England)
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Summary
This summary is machine-generated.

This study explores memory capacity in modular neural networks using a Hebbian learning rule. A novel pruning technique significantly enhances storage capacity, approaching theoretical limits for associative memories.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Artificial Neural Networks

Background:

  • Understanding memory mechanisms and biological information capacity is crucial in neuroscience.
  • Attractor networks with recurrent connections mimic cortical structures and are studied for memory functions.

Purpose of the Study:

  • To investigate the performance and storage capacity of a modular attractor network.
  • To evaluate the impact of connectivity patterns and learning rules on memory storage.

Main Methods:

  • A modular attractor network was trained using the Binary Correlation Pairing-Probability Neurons (BCPNN) learning rule with sparse random patterns.
  • Network performance was assessed across various sizes (500 to 46K units) with constant activity and diluted connectivity.
  • A new connectivity pruning technique was developed and applied.

Main Results:

  • The modular network's storage capacity was comparable to theoretical estimates for simple associative memories.
  • The developed pruning technique effectively enhanced the network's storage capacity.
  • Capacity was measured experimentally by gradually diluting connectivity in networks of varying sizes.

Conclusions:

  • Modular attractor networks, trained with BCPNN, exhibit significant storage capacity for associative memory.
  • Connectivity pruning is a viable strategy to optimize and maximize information storage in these networks.
  • The findings contribute to understanding biological information processing and developing advanced artificial memory systems.