具有最小刺激电荷的相互作用神经元网络的卷吸
Entrainment of a network of interacting neurons with minimum stimulating charge
摘要：
周期性脉冲训练刺激通常用于研究神经系统的功能并抵消疾病相关的神经元活动，例如，集体周期性神经元振荡在不损害脑组织的情况下有效控制神经元动力学是研究和临床目的的关键。我们在这里适应最小电荷控制理论，最近开发的一个单一的神经元，网络的相互作用的神经元表现出集体周期性振荡。我们提出了一个一般表达式的最佳波形，它提供了一个夹带的神经网络的刺激频率与刺激电流的最小绝对值。在单个神经元的情况下，最佳波形是bang-off-bang类型，但其参数现在由整个网络的有效相位响应曲线的参数确定，而不是单个神经元的参数。理论结果证实了三个具体的例子：两个小规模的网络的FitzHugh-Nagumo神经元与突触和电耦合，以及一个大规模的网络的突触耦合的二次积分和消防神经元。
英文摘要：
Periodic pulse train stimulation is generically used to study the function of the nervous system and to counteract disease-related neuronal activity, e.g., collective periodic neuronal oscillations. The efficient control of neuronal dynamics without compromising brain tissue is key to research and clinical purposes. We here adapt the minimum charge control theory, recently developed for a single neuron, to a network of interacting neurons exhibiting collective periodic oscillations. We present a general expression for the optimal waveform, which provides an entrainment of a neural network to the stimulation frequency with a minimum absolute value of the stimulating current. As in the case of a single neuron, the optimal waveform is of bang-off-bang type, but its parameters are now determined by the parameters of the effective phase response curve of the entire network, rather than of a single neuron. The theoretical results are confirmed by three specific examples: two small-scale networks of FitzHugh-Nagumo neurons with synaptic and electric couplings, as well as a large-scale network of synaptically coupled quadratic integrate-and-fire neurons.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
