A Mathematical Analysis of Memory Lifetime in a Simple Network Model of Memory.

Abstract:

:We study the learning of an external signal by a neural network and the time to forget it when this network is submitted to noise. The presentation of an external stimulus to the recurrent network of binary neurons may change the state of the synapses. Multiple presentations of a unique signal lead to its learning. Then, during the forgetting time, the presentation of other signals (noise) may also modify the synaptic weights. We construct an estimator of the initial signal using the synaptic currents and in this way define a probability of error. In our model, these synaptic currents evolve as Markov chains. We study the dynamics of these Markov chains and obtain a lower bound on the number of external stimuli that the network can receive before the initial signal is considered forgotten (probability of error above a given threshold). Our results are based on a finite-time analysis rather than large-time asymptotic. We finally present numerical illustrations of our results.

journal_name

Neural Comput

journal_title

Neural computation

authors

Helson P

doi

10.1162/neco_a_01286

subject

Has Abstract

pub_date

2020-07-01 00:00:00

pages

1322-1354

issue

7

eissn

0899-7667

issn

1530-888X

journal_volume

32

pub_type

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