Cherif Sofiane, Cullen Kathleen E, Galiana Henrietta L
Department of Biomedical Engineering, McGill University, Montreal, PQ, Canada H3A 2B4.
J Neurosci Methods. 2008 Aug 15;173(1):165-81. doi: 10.1016/j.jneumeth.2008.05.021. Epub 2008 Jun 3.
In most neural systems, neurons communicate by means of sequences of action potentials or 'spikes'. Information encoded by spike trains is often quantified in terms of the firing rate which emphasizes the frequency of occurrence of action potentials rather than their exact timing. Common methods for estimating firing rates include the rate histogram, the reciprocal interspike interval, and the spike density function. In this study, we demonstrate the limitations of these aforementioned techniques and propose a simple yet more robust alternative. By convolving the spike train with an optimally designed Kaiser window, we show that more robust estimates of firing rate are obtained for both low and high-frequency inputs. We illustrate our approach by considering spike trains generated by simulated as well as experimental data obtained from single-unit recordings of first-order sensory neurons in the vestibular system. Improvements were seen in the prevention of aliasing, phase and amplitude distortion, as well as in the noise reduction for sinusoidal and more complex input profiles. We review the generality of the approach, and show that it can be adapted to describe neurons with sensory or motor responses that are characterized by marked nonlinearities. We conclude that our method permits more robust estimates of neural dynamics than conventional techniques across all stimulus conditions.
在大多数神经系统中，神经元通过动作电位序列或“尖峰”进行通信。由尖峰序列编码的信息通常根据放电率来量化，放电率强调动作电位的发生频率而非其确切时间。估计放电率的常用方法包括速率直方图、互相关峰间隔和尖峰密度函数。在本研究中，我们展示了上述技术的局限性，并提出了一种简单但更稳健的替代方法。通过将尖峰序列与优化设计的凯泽窗进行卷积，我们表明，对于低频和高频输入，都能获得更稳健的放电率估计。我们通过考虑由模拟产生的尖峰序列以及从前庭系统中一阶感觉神经元的单单元记录获得的实验数据来说明我们的方法。在防止混叠、相位和幅度失真以及降低正弦和更复杂输入波形的噪声方面都有改进。我们回顾了该方法的通用性，并表明它可以适用于描述具有明显非线性特征的感觉或运动反应的神经元。我们得出结论，在所有刺激条件下，我们的方法比传统技术能更稳健地估计神经动力学。
