Department of Anesthesia and Critical Care, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
J Neurosci Methods. 2012 Nov 15;211(2):245-64. doi: 10.1016/j.jneumeth.2012.08.009. Epub 2012 Sep 5.
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 (PP-GLM) 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.
在过去的十年中，神经科学家可用的分析工具在理解和模拟神经功能方面取得了巨大的进步。特别是，点过程-广义线性模型（PP-GLM）框架已成功应用于从神经内分泌生理学到神经解码的各种问题。然而，由于缺乏已发布的 PP-GLM 算法的免费分发软件实现，以及使用这些算法所需的特定于问题的修改，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接受，我们开发了 nSTAT--一个用于 Matlab®的开源神经尖峰序列分析工具箱。通过采用面向对象编程（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）执行操作来轻松处理数据，而不是通过处理向量/矩阵形式的数据。nSTAT 中实现的算法解决了许多常见问题，包括计算刺激前时间直方图、量化神经元的时间响应特性，以及在试验内和试验间表征神经可塑性。nSTAT 为探索性数据分析提供了一个起点，允许简单和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对刺激变量进行解码。通过提供一个开源工具箱，我们希望建立一个平台，科学界可以轻松使用、修改和扩展该平台，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。
