  We propose a simple network of Hawkes processes as a cognitive model capable
of learning to classify objects. Our learning algorithm, named HAN for Hawkes
Aggregation of Neurons, is based on a local synaptic learning rule based on
spiking probabilities at each output node. We were able to use local regret
bounds to prove mathematically that the network is able to learn on average and
even asymptotically under more restrictive assumptions.
