Khorasani Abed, Daliri Mohammad Reza
Biomedical Engineering Department, Faculty of Electrical Engineering, Iran University of Science and Technology, Narmak, 16846-13114 Tehran, Iran.
Biomed Tech (Berl). 2013 Aug;58(4):377-86. doi: 10.1515/bmt-2013-0060.
The computation of neural firing rates based on spike sequences has been introduced as a useful tool for extraction of an animal's behavior. Different estimating methods of such neural firing rates have been developed by neuroscientists, and among these methods, time histogram and kernel estimators have been used more than other approaches. In this paper, the problem in the estimation of firing rates using wavelet density estimators has been considered. The results of simulation study in estimation of underlying rates based on spike sequences sampled from two different variable firing rates show that the proposed wavelet density method provides better and more accurate estimation of firing rates with smooth results compared to two other classical approaches. Furthermore, the performance of a different family of wavelet density estimators in the estimation of the underlying firing rate of biological data have been compared with results of both time histogram and kernel estimators. All in all, the results show that the proposed method can be useful in the estimation of firing rate of neural spike trains.
基于尖峰序列计算神经放电率已被作为一种提取动物行为的有用工具引入。神经科学家们开发了不同的此类神经放电率估计方法，在这些方法中，时间直方图和核估计器的使用比其他方法更为广泛。本文考虑了使用小波密度估计器估计放电率时存在的问题。基于从两种不同可变放电率采样得到的尖峰序列对潜在放电率进行估计的模拟研究结果表明，与其他两种经典方法相比，所提出的小波密度方法能提供更好、更准确的放电率估计，且结果平滑。此外，还将不同族的小波密度估计器在估计生物数据潜在放电率方面的性能与时间直方图和核估计器的结果进行了比较。总体而言，结果表明所提出的方法在估计神经尖峰序列的放电率方面可能是有用的。
