Dynamic programming algorithms for comparing multineuronal spike trains via cost-based metrics and alignments.
Dynamic programming algorithms for comparing multineuronal spike trains via cost-based metrics and alignments.
复制标题
动态编程算法，用于通过基于成本的指标和对齐来比较多神经元尖峰序列。
DOI：
10.1016/j.jneumeth.2006.11.001
复制
发表时间：
2007
影响因子：
3
通讯作者：
Gardner,Daniel
中科院分区：
文献类型：
--
作者：
Victor,JonathanD;Goldberg,DavidH;Gardner,Daniel
基于成本的指标正式的概念之间的距离，或相异性，两个尖峰列车，并适用于单和多神经元的反应。因此，这些指标已用于表征神经变异性和神经编码。通过检查一个有效算法的结构[Aronov D，2003年。多个单神经元同时响应的度量空间分析的快速算法。J Neurosci Methods 124（2），175-79]实现了多神经元响应的度量，我们确定了其推广的标准，并确定了当并行计算相关相异性度量时适用的附加效率。广义算法提供了测试各种编码假设的方法。
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.