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 Computjournal_title
Neural computationauthors
Helson Pdoi
10.1162/neco_a_01286subject
Has Abstractpub_date
2020-07-01 00:00:00pages
1322-1354issue
7eissn
0899-7667issn
1530-888Xjournal_volume
32pub_type
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