  This article underlines the learning and discrimination capabilities of a
model of associative memory based on artificial networks of spiking neurons.
Inspired from neuropsychology and neurobiology, the model implements top-down
modulations, as in neocortical layer V pyramidal neurons, with a learning rule
based on synaptic plasticity (STDP), for performing a multimodal association
learning task. A temporal correlation method of analysis proves the ability of
the model to associate specific activity patterns to different samples of
stimulation. Even in the absence of initial learning and with continuously
varying weights, the activity patterns become stable enough for discrimination.
