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slinderman
Interpretable neural spike train models with fully-Bayesian inference algorithms
Hi there, Thanks for this nice toolbox. I tried the synthetic.py with time-binned neural data (1000 observations, 124 neurons). I.e. it was not binary, and m.resample_model() runs for a couple...
Now that we've implemented Bernoulli, NB and binomial are very simple extensions. We just need to implement the proper `a_func`, `b_func`, and `c_func` methods to extend the base class. See...
The old versions of pyglm relied on [graphistician](https://github.com/slinderman/graphistician) to support different network models. To reduce dependencies, I've copied the most important network models into pyglm. I still need to copy...
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Interpretable neural spike train models with fully-Bayesian inference algorithms