nSTAT: open-source neural spike train analysis toolbox for Matlab.
nSTAT: open-source neural spike train analysis toolbox for Matlab.
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DOI：
10.1016/j.jneumeth.2012.08.009
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发表时间：
2012-11-15
影响因子：
3
通讯作者：
Brown, E. N.
中科院分区：
文献类型：
--
作者：
Cajigas, I.;Malik, W. Q.;Brown, E. N.
关键词：
在过去的十年里，神经科学家在理解和模拟神经功能方面的分析工具有了巨大的进步。特别是，点过程-广义线性模型（PPGLM）框架已成功地应用于从神经内分泌生理学到神经解码的问题。然而，缺乏自由分发的软件实现的出版PP-GLM算法，以及特定问题的修改，需要他们的使用，限制了这些技术的广泛应用。为了使现有的PP-GLM方法更容易被神经科学界所使用，我们开发了nSTAT -一个Matlab®的开源神经尖峰序列分析工具箱。通过采用面向对象的编程（OOP）方法，nSTAT允许用户通过对与实验有直观联系的对象（尖峰序列、协变量等）执行操作来轻松操纵数据，而不是通过处理向量/矩阵形式的数据。在nSTAT中实现的算法解决了许多常见问题，包括刺激周围时间直方图的计算，神经元的时间响应特性的量化，以及试验内和试验间神经可塑性的表征。nSTAT为探索性数据分析提供了一个起点，允许简单而系统地构建和测试点过程模型，并基于神经功能的点过程模型解码刺激变量。通过提供一个开源工具箱，我们希望建立一个科学界可以轻松使用、修改和扩展的平台，以解决当前技术的局限性，并将可用技术扩展到更复杂的问题。
Over the last decade there has been a tremendous advance in the analytical tools available to neuroscientists to understand and model neural function. In particular, the point process - Generalized Linear Model (PPGLM) framework has been applied successfully to problems ranging from neuro-endocrine physiology to neural decoding. However, the lack of freely distributed software implementations of published PP-GLM algorithms together with problem-specific modifications required for their use, limit wide application of these techniques. In an effort to make existing PP-GLM methods more accessible to the neuroscience community, we have developed nSTAT – an open source neural spike train analysis toolbox for Matlab®. By adopting an Object-Oriented Programming (OOP) approach, nSTAT allows users to easily manipulate data by performing operations on objects that have an intuitive connection to the experiment (spike trains, covariates, etc.), rather than by dealing with data in vector/matrix form. The algorithms implemented within nSTAT address a number of common problems including computation of peri-stimulus time histograms, quantification of the temporal response properties of neurons, and characterization of neural plasticity within and across trials. nSTAT provides a starting point for exploratory data analysis, allows for simple and systematic building and testing of point process models, and for decoding of stimulus variables based on point process models of neural function. By providing an open-source toolbox, we hope to establish a platform that can be easily used, modified, and extended by the scientific community to address limitations of current techniques and to extend available techniques to more complex problems.
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影响因子：
4
作者：
Chestek CA;Gilja V;Nuyujukian P;Foster JD;Fan JM;Kaufman MT;Churchland MM;Rivera-Alvidrez Z;Cunningham JP;Ryu SI;Shenoy KV
通讯作者：
Shenoy KV
影响因子：
2.9
作者：
Brown, EN;Barbieri, R;Frank, LM
通讯作者：
Frank, LM
DOI：
10.1109/tnsre.2005.847368
发表时间：
2005-06-01
影响因子：
4.9
作者：
Barbieri, R;Wilson, MA;Brown, EN
通讯作者：
Brown, EN
影响因子：
20.6
作者：
BOASHASH, B
通讯作者：
BOASHASH, B
影响因子：
3.8
作者：
Chen, Zhe;Purdon, Patrick L.;Harrell, Grace;Pierce, Eric T.;Walsh, John;Brown, Emery N.;Barbieri, Riccardo
通讯作者：
Barbieri, Riccardo