Modulation of the firing times of neural oscillators has long been an important control objective, with applications including Parkinson’s disease, Tourette’s syndrome, epilepsy, and learning. One common goal for such modulation is desynchronization, wherein two or more oscillators are stimulated to transition from firing in phase with each other to firing out of phase. The optimization of such stimuli has been well studied, but this typically relies on either a reduction of the dimensionality of the system or complete knowledge of the parameters and state of the system. This limits the applicability of results to real problems in neural control. Here, we present a trained artificial neural network capable of accurately estimating the effects of square-wave stimuli on neurons using minimal output information from the neuron. We then apply the results of this network to solve several related control problems in desynchronization, including desynchronizing pairs of neurons and achieving clustered subpopulations of neurons in the presence of coupling and noise.

长期以来，调节神经振荡器的激发时间一直是一个重要的控制目标，其应用包括帕金森病、图雷特综合征、癫痫和学习。这种调制的一个共同目标是去同步，其中激励两个或多个振荡器从彼此同相发射转变为异相发射。这种刺激的优化已经得到很好的研究，但这通常依赖于系统维数的减少或系统参数和状态的完整知识。这限制了结果对神经控制中实际问题的适用性。在这里，我们提出了一个训练有素的人工神经网络，能够使用神经元的最小输出信息准确估计方波刺激对神经元的影响。