Optimizing Time Histograms for Non-Poissonian Spike Trains
Optimizing Time Histograms for Non-Poissonian Spike Trains
复制标题
DOI：
10.1162/neco_a_00213
复制
发表时间：
2011-12-01
影响因子：
2.9
通讯作者：
Shinomoto, Shigeru
中科院分区：
文献类型：
--
作者：
Omi, Takahiro;Shinomoto, Shigeru
时间直方图是表示非均匀事件密度（如神经元放电）的基本工具。直方图的形状主要取决于划分时间轴的箱子的大小。然而，在大多数神经生理学研究中，当分析神经元活动的波动时，研究人员随意地选择了箱子的大小。最近导出了一种严格的方法来选择合适的箱大小，以便最小化时间直方图与未知潜在率之间的平均积分平方误差（Shimazaki & Shinomoto, 2007）。这个推导假设尖峰是由给定的速率独立产生的。然而，在实践中，生物神经元在其放电模式中表现出非泊松特征，因此峰值的发生取决于之前的峰值，这不可避免地会降低优化效果。在这封信中，我们通过考虑可能的非泊松特征来修改选择箱大小的方法。对时间直方图拟合优度的改善进行了评估，并通过由给定的波动率推导出的数值模拟非泊松脉冲序列来证实。对于一些实验数据，改进算法将时间直方图的形状从泊松优化方法变换而来。
The time histogram is a fundamental tool for representing the inhomogeneous density of event occurrences such as neuronal firings. The shape of a histogram critically depends on the size of the bins that partition the time axis. In most neurophysiological studies, however, researchers have arbitrarily selected the bin size when analyzing fluctuations in neuronal activity. A rigorous method for selecting the appropriate bin size was recently derived so that the mean integrated squared error between the time histogram and the unknown underlying rate is minimized (Shimazaki & Shinomoto, 2007). This derivation assumes that spikes are independently drawn from a given rate. However, in practice, biological neurons express non-Poissonian features in their firing patterns, such that the spike occurrence depends on the preceding spikes, which inevitably deteriorate the optimization. In this letter, we revise the method for selecting the bin size by considering the possible non-Poissonian features. Improvement in the goodness of fit of the time histogram is assessed and confirmed by numerically simulated non-Poissonian spike trains derived from the given fluctuating rate. For some experimental data, the revised algorithm transforms the shape of the time histogram from the Poissonian optimization method.