Soteropoulos Demetris S, Baker Stuart N
Institute of Neuroscience, Medical School, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK.
J Neurophysiol. 2009 Jan;101(1):402-17. doi: 10.1152/jn.90767.2008. Epub 2008 Nov 19.
Single-neuron firing is often analyzed relative to an external event, such as successful task performance or the delivery of a stimulus. The perievent time histogram (PETH) examines how, on average, neural firing modulates before and after the alignment event. However, the PETH contains no information about the single-trial reliability of the neural response, which is important from the perspective of a target neuron. In this study, we propose the concept of using the neural activity to predict the timing of the occurrence of an event, as opposed to using the event to predict the neural response. We first estimate the likelihood of an observed spike train, under the assumption that it was generated by an inhomogeneous gamma process with rate profile similar to the PETH shifted by a small time. This is used to generate a probability distribution of the event occurrence, using Bayes' rule. By an information theoretic approach, this method yields a single value (in bits) that quantifies the reduction in uncertainty regarding the time of an external event following observation of the spike train. We show that the approach is sensitive to the amplitude of a response, to the level of baseline firing, and to the consistency of a response between trials, all of which are factors that will influence a neuron's ability to code for the time of the event. The technique can provide a useful means not only of determining which of several behavioral events a cell encodes best, but also of permitting objective comparison of different cell populations.
单神经元放电通常相对于外部事件进行分析，例如任务成功完成或刺激的施加。事件周围时间直方图（PETH）研究在对齐事件之前和之后，神经放电平均而言是如何调制的。然而，PETH不包含有关神经反应单次试验可靠性的信息，而从目标神经元的角度来看，这一点很重要。在本研究中，我们提出了利用神经活动来预测事件发生时间的概念，这与利用事件来预测神经反应相反。我们首先在假设观察到的尖峰序列是由一个非齐次伽马过程生成的情况下，估计其似然性，该过程的速率分布类似于PETH并在时间上有小的偏移。利用贝叶斯规则，这被用于生成事件发生的概率分布。通过信息论方法，该方法产生一个单一值（以比特为单位），该值量化了在观察到尖峰序列后关于外部事件时间的不确定性的降低。我们表明，该方法对反应的幅度、基线放电水平以及试验之间反应的一致性敏感，所有这些都是会影响神经元对事件时间进行编码能力的因素。该技术不仅可以提供一种有用的方法来确定一个细胞对几种行为事件中的哪一种编码最佳，还可以允许对不同细胞群体进行客观比较。
