A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings.
A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings.
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DOI：
10.1186/1471-2202-10-81
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发表时间：
2009-07-16
期刊：
影响因子：
2.4
通讯作者：
Panzeri S
中科院分区：
文献类型：
--
作者：
Magri C;Whittingstall K;Singh V;Logothetis NK;Panzeri S
信息论是研究大脑如何编码感官信息的一个越来越流行的框架。尽管它广泛用于单个神经元和小神经群体的尖峰序列的分析，但它对其他类型的神经生理信号（EEG、LFP、BOLD）的分析的应用到目前为止仍然相对有限。这是由于有限的抽样偏差，影响信息的计算，技术的复杂性，以消除偏差，并缺乏公开可用的快速例程的多维响应的信息分析。在这里，我们介绍了一个新的基于C和Matlab的信息理论工具箱，专门为神经科学数据开发。该工具箱实现了一种新的计算优化算法，用于估计神经科学应用中使用的许多主要信息理论量和偏差校正技术。我们以几种方式说明和测试工具箱。首先，我们验证了这些算法提供了准确和无偏的估计模拟大脑信号（即LFP，EEG或BOLD）所携带的信息，即使在使用有限的实验数据。这个测试很重要，因为现有的算法到目前为止主要是在尖峰序列上测试的。其次，我们应用工具箱的EEG记录从一个主题看自然电影的分析，我们的特征电极的位置，频率和信号特征进行最视觉信息。第三，我们解释了如何使用工具箱将神经信号的不同特征所携带的信息分解为不同的成分，反映神经信号部分之间的相关性有助于编码的不同方式。我们通过分析在自然主义电影的呈现过程中从初级视觉皮层记录的LFPs来说明这种分解。这里提出的新工具箱实现了对神经数据的信息论分析中使用的最相关量的快速和数据鲁棒性计算。该工具箱可以在Matlab中轻松使用，大多数神经科学实验室都使用Matlab环境来获取，预处理和绘制神经数据。因此，它可以显着扩大信息论在神经科学中的应用领域，并导致有关神经代码的新发现。
Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD) has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses. Here we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD) even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies. The new toolbox presented here implements fast and data-robust computations of the most relevant quantities used in information theoretic analysis of neural data. The toolbox can be easily used within Matlab, the environment used by most neuroscience laboratories for the acquisition, preprocessing and plotting of neural data. It can therefore significantly enlarge the domain of application of information theory to neuroscience, and lead to new discoveries about the neural code.
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影响因子：
9.2
作者：
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通讯作者：
Logothetis, Nikos K.
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