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：我们提出了一种基于 Hawkes 进程的简单认知模型来学习分类任务。我们的学习算法名为 EWAK（指指数加权平均和 Kalikow 分解），它采用了一种基于输出节点的发放率的局部突触学习规则。我们能够使用局部遗憾界在更严格的假设下数学证明网络能够在平均意义下进行学习，甚至在渐近意义下进行学习。