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A Real-world What-Where-When Memory Test
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Forgetting Memories and Their Attractiveness.

Enzo Marinari1

  • 1Dipartimento di Fisica, Sapienza Università di Roma; INFN Sezione di Roma 1; and Nanotech-CNR, UOS di Roma, 00185 Roma, Italy enzo.marinari@uniroma1.it.

Neural Computation
|January 16, 2019
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Summary
This summary is machine-generated.

This study numerically investigates Parisi

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Area of Science:

  • Computational Neuroscience
  • Statistical Physics of Neural Networks

Background:

  • Introduced in 1986 by Parisi, the 'memory that forgets' model bounds synaptic strength.
  • This mechanism aims to prevent confusion and prioritize recent memories.
  • It possesses a physiologically well-defined meaning within neural systems.

Purpose of the Study:

  • To numerically analyze the 'memory that forgets' model.
  • Investigate learning features for finite neurons and patterns.
  • Examine system behavior in large, finite limits and pattern attraction basins.

Main Methods:

  • Numerical simulations of the 'memory that forgets' model.
  • Analysis of synaptic strength bounding and pattern learning.
  • Investigation of system dynamics in the large but finite N limit.

Main Results:

  • Characterization of learning dynamics for finite systems.
  • Demonstration of the system's behavior in large, finite regimes.
  • Quantification of the basin of attraction for learned patterns.

Conclusions:

  • The 'memory that forgets' model exhibits specific learning characteristics.
  • Basins of attraction for learned patterns are exponentially small with pattern age.
  • The model offers a physiologically plausible mechanism for memory consolidation and forgetting.