  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 leads 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 thanks to the synaptic currents and define by this way 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 as forgotten (probability of error above a given
threshold). Our results hold for finite size networks as well as in the large
size asymptotic. Our results are based on a finite time analysis rather than
large time asymptotic. We finally present numerical illustrations of our
results.
