Real-time stabilization of neurons into clusters
Real-time stabilization of neurons into clusters
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DOI：
10.23919/acc.2017.7963376
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发表时间：
2017-05
期刊：
影响因子：
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通讯作者：
T. Matchen;J. Moehlis
中科院分区：
文献类型：
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作者：
T. Matchen;J. Moehlis
脑深部电刺激（DBS）是一种广泛用于治疗帕金森病相关震颤的方法，但其机制尚未完全了解。有一种假说得到了实验的支持，即帕金森氏症的某些症状与基底神经节神经元的病理性同步有关。出于这个原因，近年来人们一直有兴趣寻找有效的方法来使快速作用和低功耗的神经元去激活。最近的研究结果协调重置和周期性强迫振荡器表明，形成不同的神经元集群可能被证明是更有效的比实现完全desertification通过促进可塑性效应，可能会持续刺激关闭后。现有的方法实现聚类经常需要多个输入源或预先计算的控制信号。在这里，我们提出了一种控制策略的聚类，基于一组相同的神经元的减少相位模型的分析，允许实时，单输入控制的神经元与低振幅，低总能量信号的人口。
Deep brain stimulation (DBS) is a widespread method of combating tremors associated with Parkinson's disease, but whose mechanisms are not fully understood. One hypothesis, supported experimentally, is that some symptoms of Parkinson's are associated with pathological synchronization of neurons in the basal ganglia. For this reason, there has been interest in recent years in finding efficient ways to desynchronize neurons that are both fast-acting and low-power. Recent results on coordinated reset and periodically forced oscillators suggest that forming distinct clusters of neurons may prove to be more effective than achieving complete desynchronization by promoting plasticity effects that might persist after stimulation is turned off. Existing proposed methods for achieving clustering frequently require either multiple input sources or precomputing the control signal. We propose here a control strategy for clustering, based on an analysis of the reduced phase model for a set of identical neurons, that allows for real-time, single-input control of a population of neurons with low-amplitude, low total energy signals.