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Desynchronization in Networks of Globally Coupled Neurons with Dendritic Dynamics

机译:具有树突动力学的全局耦合神经元网络中的去同步

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摘要

Effective desynchronization can be exploited as a tool for probing the functional significance of synchronized neural activity underlying perceptual and cognitive processes or as a mild treatment for neurological disorders like Parkinson’s disease. In this article we show that pulse-based desynchronization techniques, originally developed for networks of globally coupled oscillators (Kuramoto model), can be adapted to networks of coupled neurons with dendritic dynamics. Compared to the Kuramoto model, the dendritic dynamics significantly alters the response of the neuron to the stimulation. Under medium stimulation amplitude a bistability of the re- sponse of a single neuron is observed. When stimulated at some initial phases, the neuron displays only modulations of its firing, whereas at other initial phases it stops oscillating entirely. Significant alterations in the duration of stimulation-induced transients are also observed. These transients endure after the end of the stimulation and cause maximal desynchronization to occur not during the stimulation, but with some delay after the stimulation has been turned off. To account for this delayed desynchronization effect, we have designed a new calibration procedure for finding the stimulation parameters that result in optimal desynchronization. We have also developed a new desynchronization technique by low frequency entrainment. The stimulation techniques originally developed for the Kuramoto model, when using the new calibration procedure, can also be applied to networks with dendritic dynamics. However, the mechanism by which desynchronization is achieved is substantially different than for the network of Kuramoto oscillators. In particular, the addition of dendritic dynamics significantly changes the timing of the stimulation required to obtain desynchronization. We propose desynchronization stimulation for experimental analysis of synchronized neural processes and for the therapy of movement disorders.
机译:有效的去同步功能可以用作探究感知和认知过程背后的同步神经活动的功能意义的工具,也可以作为对帕金森氏病等神经系统疾病的温和治疗。在本文中,我们展示了最初为全局耦合振荡器网络(Kuramoto模型)开发的基于脉冲的去同步技术,可以适应具有树突动力学的耦合神经元网络。与仓本模型相比,树突动力学显着改变了神经元对刺激的反应。在中等刺激幅度下,观察到单个神经元响应的双稳态。在某些初始阶段受到刺激时,神经元仅显示其发射的调制,而在其他初始阶段,它会完全停止振荡。还观察到了刺激引起的瞬变持续时间的显着变化。这些瞬变在刺激结束后会持续存在,并导致最大的不同步发生在刺激过程中,而不是在刺激过程中发生,而是在关闭刺激后出现一些延迟。为了解决这种延迟的失步效应,我们设计了一种新的校准程序,用于查找导致最佳失步的激励参数。我们还通过低频夹带开发了一种新的去同步技术。当使用新的校准程序时,最初为Kuramoto模型开发的刺激技术也可以应用于具有树突动力学的网络。但是,实现不同步的机制与仓本振荡器网络的机制大不相同。特别是,树突动力学的添加会显着改变获得失步所需的刺激时间。我们提出去同步刺激用于同步神经过程的实验分析和运动障碍的治疗。

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