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up-to-date code for model-based inference of spike trains from calcium imaging
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rysk-t/oopsi
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This is a repo containing the most current code for doing model-based spike train inference from calcium imaging. Manuscripts explaining the theory and providing some results on simulated and real data are available from the fast-oopsi, smc-oopsi, and pop-oopsi github repositories. Those repositories also contain code that you may run and data to download to play with things. Any question, ask them in the issues tab. Please let me know of any positive or negative experiences. Much Obliged, jovo Note that there are two python implementations of fast-oopsi (foopsi) that i expect to work better and faster: https://github.com/liubenyuan/py-oopsi and https://github.com/alimuldal/PyFNND. A question we often get is: "how shall we interpret the output? probability of spiking? instantaneous firing rate?? the answer is: yes, maybe :) the "issue" is the lack of calibration data, which means we do not know the absolute size of a calcium transient evoked from a single spike. we estimate it from the data, but if the neuron happens to always spike twice, for example, our estimate will be off by a factor of 2. we can think of the above this way: is it likely that the neuron to spikes multiple times per time bin? if so, i don't normalize the outp 52BB ut, and interpret it as the instantaneous firing rate. if not, i normalize the output so that its max is 1, and then i interpret it as the probability of a spike. i hope this helps! if not, please post issues [here](https://github.com/jovo/oopsi/issues)
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up-to-date code for model-based inference of spike trains from calcium imaging
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