Extracting information in spike time patterns with wavelets and information theory

Vítor Lopes-dos-Santos , Stefano Panzeri , Christoph Kayser , Mathew E. Diamond , Rodrigo Quian Quiroga
Journal of Neurophysiology Published 1 February 2015 Vol. 113 no. 3, 1015-1033 DOI: 10.1152/jn.00380.2014
Abstract
We present a new method to assess the information carried by temporal patterns in spike trains. The method first performs a wavelet decomposition of the spike trains, then uses Shannon information to select a subset of coefficients carrying information, and finally assesses timing information in terms of decoding performance: the ability to identify the presented stimuli from spike train patterns. We show that the method allows: 1) a robust assessment of the information carried by spike time patterns even when this is distributed across multiple time scales and time points; 2) an effective denoising of the raster plots that improves the estimate of stimulus tuning of spike trains; and 3) an assessment of the information carried by temporally coordinated spikes across neurons. Using simulated data, we demonstrate that the Wavelet-Information (WI) method performs better and is more robust to spike time-jitter, background noise, and sample size than well-established approaches, such as principal component analysis, direct estimates of information from digitized spike trains, or a metric-based method. Furthermore, when applied to real spike trains from monkey auditory cortex and from rat barrel cortex, the WI method allows extracting larger amounts of spike timing information. Importantly, the fact that the WI method incorporates multiple time scales makes it robust to the choice of partly arbitrary parameters such as temporal resolution, response window length, number of response features considered, and the number of available trials. These results highlight the potential of the proposed method for accurate and objective assessments of how spike timing encodes information.

我们提出了一种新的方法来评估在包含在时间峰电位模式 (temporal patterns in spike train) 的信息。方法首先执行把时间峰电位信息 (spike train) 进行小波分解，然后采用Shannon方法选择一个承载信息的系数子集，最后评估定时信息的解码表现 -以确定从时间峰电位中的刺激的能力。我们发现该方法：1）提供一个稳健评估峰电位时间模式所携带的信息，即使该信息是分布跨越多个时间尺度和时间点；2）有效替栅格图去噪，优化估计时间峰电位刺激调谐 (stimulus tuning)；3）能分析分布于跨神经元的时间峰电位信息。使用模拟数据，比较对峰电位时间斗动 (spike time-jitter)，背景噪声，和样本量大小之影响，小波信息（wavelet-information, WI）比传统方法如主成分分析  (PCA)，直接从数字化时间峰电位估计信息，或基于度量的方法性能更好及更稳健。此外，通过以色列AlphaOmega 公司SNR系统记录神经元活动，在从猴子听觉皮层和大鼠桶皮层采集的时间峰电位信号，WI方法允许提取大量的峰电位时间信息。重要的是WI方法采用多时间尺度，对选择部分任意参数，如时间分辨率，响应窗口长度，反应特点数目，和试验的数量等参数考虑上，更为稳健。这些结果突出了这新方法对峰电位时间编码信息准确和客观的评估的潜在应用。

ALPHAOMEGA ENGINEER INC. (AO-Eng )是以色列著名神经设备公司研发生产商，产品旨在帮助更多的科研者提升电生理领域的研究。AO-Eng公司产品系统被广泛应用于载体研究及临床应用 (部分更得到FDA/ CE/ CFDA认证), 为少数能转化研究到临床的多道系统研发商。

AlphaLab SnR是一套综合性及完整的数据采集系统。以前所未有的便捷和灵活性使用户从虚拟无限数量的信道上进行记录与刺激。该系统将微电极记录从被动观察带到了主动控制和与神经回路复杂互动的新水平。. AlphaLab SnR多通道神经电生理实验系统是目前市场上最先进的大规模神经元动作电采集统，AlphaLab SnR系统以前所未有的便捷和灵活性使用户从虚拟无限数量的信道上进行记录与刺激。SnR 为AO-Eng公司专用于研究产品,不能作为临床使用。