用于通过基于成本的度量和比对来比较多神经元放电序列的动态规划算法。
Dynamic programming algorithms for comparing multineuronal spike trains via cost-based metrics and alignments.
作者信息
Victor Jonathan D, Goldberg David H, Gardner Daniel
机构信息
Department of Neurology and Neuroscience, Weill Medical College of Cornell University, 1300 York Avenue, New York City, NY 10021, USA.
出版信息
J Neurosci Methods. 2007 Apr 15;161(2):351-60. doi: 10.1016/j.jneumeth.2006.11.001. Epub 2006 Dec 15.
Cost-based metrics formalize notions of distance, or dissimilarity, between two spike trains, and are applicable to single- and multineuronal responses. As such, these metrics have been used to characterize neural variability and neural coding. By examining the structure of an efficient algorithm [Aronov D, 2003. Fast algorithm for the metric-space analysis of simultaneous responses of multiple single neurons. J Neurosci Methods 124(2), 175-79] implementing a metric for multineuronal responses, we determine criteria for its generalization, and identify additional efficiencies that are applicable when related dissimilarity measures are computed in parallel. The generalized algorithm provides the means to test a wide range of coding hypotheses.
基于成本的度量标准将两个脉冲序列之间的距离或不相似性概念形式化，并且适用于单神经元和多神经元反应。因此，这些度量标准已被用于表征神经变异性和神经编码。通过研究一种有效算法的结构 [阿罗诺夫 D，2003 年。用于多个单个神经元同步反应的度量空间分析的快速算法。《神经科学方法杂志》124(2)，175 - 79]，该算法实现了一种用于多神经元反应的度量标准，我们确定了其泛化的标准，并识别出在并行计算相关不相似性度量时适用的其他效率。这种泛化算法提供了测试广泛编码假设的方法。
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