Code for spike inference available

Together with Lucas Theis, a PhD student in our lab, I developed a new method to infer spike rates from calcium signals using supervised learning in flexible probabilistic models. In addition, together with our experimental collaborators we collected a large dataset of simultaneously recorded electrophysiology and calcium imaging data that allowed us to benchmark our algorithm against many available methods. predictions

We show that our algorithm performs better than previoulsy published methods (preprint) and make the code available on github. The package includes a model trained on all available data, which should also perform well without training data.  Let us know when you use the code in your research!