Sequential Detection of Regime Changes in Neural Data
Sequential Detection of Regime Changes in Neural Data
复制标题
神经数据中状态变化的顺序检测
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
V. Tarokh
中科院分区：
文献类型：
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作者：
T. Banerjee;Stephen A. Allsop;K. Tye;Demba E. Ba;V. Tarokh
研究了神经数据中放电模式变化的检测问题。该问题被描述为最快变化检测(QCD)问题。对文献中的重要算法进行了综述。讨论了一种新的算法技术来检测与学习的基线行为的偏差。所研究的算法既适用于尖峰电势数据，也适用于局部场势数据。这些算法被应用于小鼠的刺激波数据，以验证行为学习的存在。
The problem of detecting changes in firing patterns in neural data is studied. The problem is formulated as a quickest change detection (QCD) problem. Important algorithms from the literature are reviewed. A new algorithmic technique is discussed to detect deviations from learned baseline behavior. The algorithms studied can be applied to both spike and local field potential data. The algorithms are applied to mice spike data to verify the presence of behavioral learning.