BAYESIAN MODELING AND CLASSIFICATION OF NEURAL SIGNALS
BAYESIAN MODELING AND CLASSIFICATION OF NEURAL SIGNALS
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DOI：
10.1162/neco.1994.6.5.1005
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发表时间：
1994-09-01
影响因子：
2.9
通讯作者：
LEWICKI, MS
中科院分区：
文献类型：
--
作者：
LEWICKI, MS
识别和分类细胞外神经波形中的动作电位形状一直是研究的主题，尽管一些算法已经成功地应用于此目的，但它们的使用受到一些突出问题的限制。首先是如何确定波形中动作电位的形状，其次是如何确定有多少形状是不同的。一个更难的问题是，动作电位经常重叠，这使得确定脉冲的形状和分类都变得困难。在本报告中，通过应用贝叶斯概率论得到了每个问题的解决方案。通过定义波形的概率模型，可以量化尖峰形状的形式和数量的概率。此外，该框架还为任意复杂重叠序列的分解提供了有效的算法。该算法可以提取比以往方法多许多倍的信息，并且有利于神经元类和神经元回路内相互作用的细胞外研究。
Identifying and classifying action potential shapes in extracellular neural waveforms have long been the subject of research, and although several algorithms for this purpose have been successfully applied, their use has been limited by some outstanding problems. The first is how to determine shapes of the action potentials in the waveform and, second, how to decide how many shapes are distinct. A harder problem is that action potentials frequently overlap making difficult both the determination of the shapes and the classification of the spikes. In this report, a solution to each of these problems is obtained by applying Bayesian probability theory. By defining a probabilistic model of the waveform, the probability of both the form and number of spike shapes can be quantified. In addition, this framework is used to obtain an efficient algorithm for the decomposition of arbitrarily complex overlap sequences. This algorithm can extract many times more information than previous methods and facilitates the extracellular investigation of neuronal classes and of interactions within neuronal circuits.