DIS-NNSTAT-MECHNEMay 31, 2018

Forgetting Memories and their Attractiveness

arXiv:1805.12368v15 citations
Originality Synthesis-oriented
AI Analysis

This work addresses memory retention issues in neural models, but it is incremental as it builds on a 1986 model to analyze specific numerical properties.

The authors studied a forgetting memory model with bounded synaptic strength, showing that the basin of attraction for learned patterns decreases exponentially with pattern age, which is identified as a non-physiological feature.

We study numerically the memory which forgets, introduced in 1986 by Parisi by bounding the synaptic strength, with a mechanism which avoid confusion, allows to remember the pattern learned more recently and has a physiologically very well defined meaning. We analyze a number of features of the learning at finite number of neurons and finite number of patterns. We discuss how the system behaves in the large but finite N limit. We analyze the basin of attraction of the patterns that have been learned, and we show that it is exponentially small in the age of the pattern. This is a clearly non physiological feature of the model.

Foundations

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