基于推理的单次试验神经元反应重新排列方法
Inference based method for realignment of single trial neuronal responses
摘要：
对感觉刺激的神经元反应或与行为相关的神经元反应通常通过对大量实验试验的神经元活动进行平均来提取。执行这种试平均以减少噪声并减少与相应的刺激或行为无关的其他信号的影响。然而，如果所记录的神经元响应相对于相应的刺激或行为在时间上抖动，则在试验上求平均值可能会扭曲对潜在神经元响应的估计。在这里，我们提出了一种算法，命名为dTAV算法，用于重新排列记录的神经元活动的任意内部触发。使用模拟数据，我们表明，dTAV算法可以减少神经元响应的抖动的信噪比为0.2或更高，即在噪声的标准偏差是高达五倍大于神经元响应幅度的情况下。通过消除抖动，从而能够更准确地估计神经元响应，dTAV算法可以改善响应的分析和解释，并提高依赖于从神经元记录中异步检测事件的系统的准确性。
英文摘要：
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 reduce 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. Here, we present an algorithm, named dTAV algorithm, for realigning the recorded neuronal activity to an arbitrary internal trigger. Using simulated data, we show that the dTAV algorithm can reduce the jitter of neuronal responses for signal to noise ratios of 0.2 or higher, i.e. in cases where the standard deviation of the noise is up to five times larger than the neuronal response amplitude. By removing the jitter and, therefore, enabling more accurate estimation of neuronal responses, the dTAV algorithm can improve analysis and interpretation of the responses and improve the accuracy of systems relaying on asynchronous detection of events from neuronal recordings.
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