Neural Spike Train Analysis Toolbox for Matlab
nSTAT is an open-source, object-oriented Matlab toolbox that implements a range of models and algorithms for neural spike train data analysis. Such data are frequently obtained from neuroscience experiments and our intention in writing nSTAT is to facilitate quick, easy and consistent neural data analysis.
One of nSTAT's key strengths is point process generalized linear models for spike train signals that provide a formal statistical framework for processing signals recorded from ensembles of single neurons. It also has extensive support for model fitting, model order analysis, and adaptive decoding. In addition to point process algorithms, nSTAT also provides tools for Gaussian signals, ranging from correlation analysis to the Kalman filter, which can be applied to continuous normally-distributed neural signals such as local field potentials, EEG, ECoG, etc.
Although created with neural signal processing in mind, nSTAT can be used as a generic tool for analyzing any types of discrete and continuous signals, and thus has wide applicability.
Like all open-source projects, nSTAT will benefit from your involvement, suggestions and contributions. This platform is intended as a repository for extensions to the toolbox based on your code contributions as well as for flagging and tracking open issues.
The current release version of nSTAT can be downloaded from http://www.neurostat.mit.edu/nstat .
For mathematical and programmatic details of the toolbox, see:
Cajigas I, Malik WQ, Brown EN. nSTAT: Open-source neural spike train analysis toolbox for Matlab. Journal of Neuroscience Methods 211: 245–264, Nov. 2012 http://doi.org/10.1016/j.jneumeth.2012.08.009
If you use nSTAT in your work, please remember to cite the above paper in any publications. nSTAT is protected by the GPL v2 Open Source License.
Iahn Cajigas (2019). iahncajigas/nSTAT (https://www.github.com/iahncajigas/nSTAT), GitHub. Retrieved .