Table 9.
Features of open-source toolboxes for generalized linear modeling of spike data regarding visualization tools, principal and usage programming language, availability of documentation, number of citations (for the paper with the introduced method), support by updates at least once per year and implemented methods.
| Toolbox, version | Methods | Visuali- | Language | Documen- | Cited | Support |
|---|---|---|---|---|---|---|
| zation | tation | |||||
| Case-Studies | pGLM | + | MATLAB | + | <30 | + |
| GLMcode1 | pGLM | + | MATLAB | + | <30 | − |
| GLMcode2 | pGLM | + | MATLAB | + | <30 | − |
| GLMspiketools v1 | cGLM, pGLM, SHF, | + | MATLAB | + | >900 | + |
| STB | ||||||
| GLMspike- | cGLM, gGLM, | + | MATLAB | + | >900 | + |
| traintutorial | pGLM, SHF | |||||
| neuroGLM | pGLM, SHF, STB | + | MATLAB | + | >90 | + |
| NIMclass v1.0 | GLM, GQM, GNM, | − | MATLAB | − | >90 | + |
| NIM | ||||||
| nStat v2 | ppGLM | + | MATLAB | In part | <30 | + |
| spykesML v0.1.dev | pGLM, SHF | − | Python | + | <30 | + |
cGLM, GLM with coupling filters; gGLM, linear Gaussian GLM; GNM, Generalized Nonlinear Model (Butts et al., 2011); GQM, Generalized Quadratic Model (Park and Pillow, 2011); pGLM, Poisson GLM (Truccolo et al., 2005); ppGLM, point-process GLM (Paninski et al., 2007); NIM, Nonlinear Input Model (McFarland et al., 2013); SHF, Spikes and covariates History Filters; STB, Smooth Temporal Basis. Bold values indicate the number of citations higher than 90.