Parameters for burst detection.
Parameters for burst detection.
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DOI：
10.3389/fncom.2013.00193
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发表时间：
2013
影响因子：
3.2
通讯作者：
Takahashi H
中科院分区：
文献类型：
--
作者：
Bakkum DJ;Radivojevic M;Frey U;Franke F;Hierlemann A;Takahashi H
神经元内和整个网络中的动作电位爆发被认为在体内和体外神经元如何处理和存储信息方面发挥作用。因此，准确检测爆发的发生和持续时间是许多研究的关键。已经提出了许多算法来这样做，但标准方法尚未被采用。这部分是由于许多算法需要调整多个ad-hoc参数和进一步的post-hoc标准，以产生令人满意的结果。在这里，我们广泛地分类现有的方法，并提出了一种新的方法，只需要选择一个参数：尖峰N的数量，包括最小的突发考虑。如果在小于T ms内发生N个尖峰，则识别出爆发，其中阈值T通过观察尖峰间间隔的概率分布自动确定。性能进行了比较与不同类别的检测器从体外神经元网络生长在微电极阵列上收集的数据。我们的方法提供了一些有用的功能，包括：一个简单的实现，不需要特设或事后的标准，和精确分配的突发边界时间点。与现有的方法不同，检测不偏向于更大的爆发，允许识别和分析更大范围的神经元和网络动力学。
Bursts of action potentials within neurons and throughout networks are believed to serve roles in how neurons handle and store information, both in vivo and in vitro. Accurate detection of burst occurrences and durations are therefore crucial for many studies. A number of algorithms have been proposed to do so, but a standard method has not been adopted. This is due, in part, to many algorithms requiring the adjustment of multiple ad-hoc parameters and further post-hoc criteria in order to produce satisfactory results. Here, we broadly catalog existing approaches and present a new approach requiring the selection of only a single parameter: the number of spikes N comprising the smallest burst to consider. A burst was identified if N spikes occurred in less than T ms, where the threshold T was automatically determined from observing a probability distribution of inter-spike-intervals. Performance was compared vs. different classes of detectors on data gathered from in vitro neuronal networks grown over microelectrode arrays. Our approach offered a number of useful features including: a simple implementation, no need for ad-hoc or post-hoc criteria, and precise assignment of burst boundary time points. Unlike existing approaches, detection was not biased toward larger bursts, allowing identification and analysis of a greater range of neuronal and network dynamics.
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影响因子：
2.5
作者：
Jäckel D;Frey U;Fiscella M;Franke F;Hierlemann A
通讯作者：
Hierlemann A
海马培养物中自发活动的尖峰特征爆发。
影响因子：
3.2
作者：
Pimashkin A;Kastalskiy I;Simonov A;Koryagina E;Mukhina I;Kazantsev V
通讯作者：
Kazantsev V
影响因子：
2.5
作者：
LEGENDY, CR;SALCMAN, M
通讯作者：
SALCMAN, M
影响因子：
25
作者：
Ji, Daoyun;Wilson, Matthew A.
通讯作者：
Wilson, Matthew A.
影响因子：
5.4
作者：
Frey, Urs;Sedivy, Jan;Hierlemann, Andreas
通讯作者：
Hierlemann, Andreas