Department of Mechanical Engineering, University of California, Santa Barbara, CA, 93106, USA.
Department of Mechanical Engineering, Program in Dynamical Neuroscience, University of California, Santa Barbara, CA, 93106, USA.
Biol Cybern. 2021 Jun;115(3):219-235. doi: 10.1007/s00422-021-00874-w. Epub 2021 Apr 28.
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.
神经振荡器的发射时间调制一直是一个重要的控制目标，其应用包括帕金森病、妥瑞氏症、癫痫和学习。这种调制的一个常见目标是去同步化，其中两个或更多的振荡器被刺激以从相互同步的发射过渡到相位不同的发射。这种刺激的优化已经得到了很好的研究，但这通常依赖于系统的维度降低或系统的参数和状态的完全了解。这限制了结果在神经控制实际问题中的适用性。在这里，我们提出了一个经过训练的人工神经网络，它能够使用神经元的最小输出信息准确地估计方波刺激对神经元的影响。然后，我们将该网络的结果应用于解决去同步化中的几个相关控制问题，包括去同步化神经元对和在存在耦合和噪声的情况下实现神经元的聚类亚群。
