贝叶斯单次试验分析时变神经信号的敏感性和特异性
Sensitivity and specificity of a Bayesian single trial analysis for time varying neural signals
摘要：
我们最近报道了神经信号中存在的波动，这可能允许神经元在时间上顺序地编码多个同时刺激。这需要采用一种新的统计方法，以允许在个体试验的规模上调查神经活动。在这里，我们提出了使用合成数据来评估这种分析的灵敏度和特异性的测试。我们制作了数据集，以匹配来自单刺激响应分布的几种潜在响应模式中的每一种。特别是，我们模拟了双刺激试验尖峰计数，反映了波动的混合物的单刺激尖峰计数，稳定的中间平均值，单刺激赢家通吃，或响应分布的范围外定义的单刺激响应（如求和或抑制）。然后，我们评估了分析如何恢复正确的反应模式，作为模拟试验次数的函数，以及对每个“刺激”的模拟反应之间的差异。我们发现，当试验次数>20次，单刺激反应率分别为50 Hz和20 Hz时，混合、中间和外部类别的恢复效果非常好（正确率> 97%），单/赢家通吃类别的恢复效果也很好（正确率>90%）。更大数量的试验和更大的分离之间的单一刺激放电率提高分类准确性。这些结果提供了一个基准，数据收集的指导方针，使用这种方法来调查编码的多个项目在个人试验的时间尺度。
英文摘要：
We recently reported the existence of fluctuations in neural signals that may permit neurons to code multiple simultaneous stimuli sequentially across time. This required deploying a novel statistical approach to permit investigation of neural activity at the scale of individual trials. Here we present tests using synthetic data to assess the sensitivity and specificity of this analysis. We fabricated datasets to match each of several potential response patterns derived from single-stimulus response distributions. In particular, we simulated dual stimulus trial spike counts that reflected fluctuating mixtures of the single stimulus spike counts, stable intermediate averages, single stimulus winner-take-all, or response distributions that were outside the range defined by the single stimulus responses (such as summation or suppression). We then assessed how well the analysis recovered the correct response pattern as a function of the number of simulated trials and the difference between the simulated responses to each "stimulus" alone. We found excellent recovery of the mixture, intermediate, and outside categories (>97% percent correct), and good recovery of the single/winner-take-all category (>90% correct) when the number of trials was >20 and the single-stimulus response rates were 50Hz and 20Hz respectively. Both larger numbers of trials and greater separation between the single stimulus firing rates improved categorization accuracy. These results provide a benchmark, and guidelines for data collection, for use of this method to investigate coding of multiple items at the individual-trial time scale.
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