Agüera y Arcas Blaise, Fairhall Adrienne L
Rare Books Library, Princeton University, Princeton, NJ 08544, USA.
Neural Comput. 2003 Aug;15(8):1789-807. doi: 10.1162/08997660360675044.
The computation performed by a neuron can be formulated as a combination of dimensional reduction in stimulus space and the nonlinearity inherent in a spiking output. White noise stimulus and reverse correlation (the spike-triggered average and spike-triggered covariance) are often used in experimental neuroscience to "ask" neurons which dimensions in stimulus space they are sensitive to and to characterize the nonlinearity of the response. In this article, we apply reverse correlation to the simplest model neuron with temporal dynamics-the leaky integrate-and-fire model-and find that for even this simple case, standard techniques do not recover the known neural computation. To overcome this, we develop novel reverse-correlation techniques by selectively analyzing only "isolated" spikes and taking explicit account of the extended silences that precede these isolated spikes. We discuss the implications of our methods to the characterization of neural adaptation. Although these methods are developed in the context of the leaky integrate-and-fire model, our findings are relevant for the analysis of spike trains from real neurons.
神经元所执行的计算可以被表述为刺激空间中的降维和尖峰输出中固有的非线性的组合。白噪声刺激和反向相关（即尖峰触发平均和尖峰触发协方差）在实验神经科学中经常被用于“询问”神经元它们对刺激空间中的哪些维度敏感，并表征响应的非线性。在本文中，我们将反向相关应用于具有时间动态的最简单模型神经元——漏电整合-发放模型——并发现即使对于这个简单的情况，标准技术也无法恢复已知的神经计算。为了克服这一点，我们通过仅选择性地分析“孤立”尖峰并明确考虑这些孤立尖峰之前的延长沉默期，开发了新颖的反向相关技术。我们讨论了我们的方法对神经适应性表征的影响。尽管这些方法是在漏电整合-发放模型的背景下开发的，但我们的发现与对真实神经元的尖峰序列分析相关。
