We propose a simple network of Hawkes processes as a cognitive model capable of learning to classify objects. Our learning algorithm, named EWAK for Exponentially Weighted Average and Kalikow decomposition, is based on a local synaptic learning rule based on firing rates 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能够学习分类对象的认知模型。我们的学习算法名为EWAK（指数加权平均与卡利科夫分解），基于每个输出节点放电率的局部突触学习规则。我们利用局部遗憾界从数学上证明了该网络能够平均学习，甚至在更严格的假设下能够渐进学习。