Milekovic Tomislav, Mehring Carsten
Bernstein Center Freiburg, University of Freiburg, Hansastr. 9A, 79104 Freiburg, Germany.
Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany.
PLoS One. 2016 May 9;11(5):e0153773. doi: 10.1371/journal.pone.0153773. eCollection 2016.
Neuronal responses to sensory stimuli or neuronal responses related to behaviour are often extracted by averaging neuronal activity over large number of experimental trials. Such trial-averaging is carried out to reduce noise and to diminish the influence of other signals unrelated to the corresponding stimulus or behaviour. However, if the recorded neuronal responses are jittered in time with respect to the corresponding stimulus or behaviour, averaging over trials may distort the estimation of the underlying neuronal response. Temporal jitter between single trial neural responses can be partially or completely removed using realignment algorithms. Here, we present a measure, named difference of time-averaged variance (dTAV), which can be used to evaluate the performance of a realignment algorithm without knowing the internal triggers of neural responses. Using simulated data, we show that using dTAV to optimize the parameter values for an established parametric realignment algorithm improved its efficacy and, therefore, reduced the jitter of neuronal responses. By removing the jitter more effectively and, therefore, enabling more accurate estimation of neuronal responses, dTAV can improve analysis and interpretation of the neural responses.
神经元对感觉刺激的反应或与行为相关的神经元反应通常是通过对大量实验试次中的神经元活动进行平均来提取的。进行这种试次平均是为了减少噪声，并减少与相应刺激或行为无关的其他信号的影响。然而，如果记录的神经元反应相对于相应的刺激或行为在时间上有抖动，那么在试次上进行平均可能会扭曲对潜在神经元反应的估计。使用重排算法可以部分或完全消除单次试次神经反应之间的时间抖动。在这里，我们提出了一种名为时间平均方差差（dTAV）的度量，它可以在不知道神经反应内部触发因素的情况下用于评估重排算法的性能。使用模拟数据，我们表明使用dTAV来优化既定参数重排算法的参数值可以提高其有效性，从而减少神经元反应的抖动。通过更有效地消除抖动，从而能够更准确地估计神经元反应，dTAV可以改善对神经反应的分析和解释。
