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.

翻译：我们提出一个简单的霍克斯过程网络作为认知模型，能够学习对物体进行分类。我们的学习算法名为HAN（霍克斯神经元聚合），基于各输出节点的脉冲概率采用局部突触学习规则。通过利用局部遗憾界，我们从数学上证明该网络在平均意义上能够学习，甚至在更严格假设下可实现渐近学习。