Open kimtonyhyun opened 3 years ago
Hi Tony,
Thanks a lot for your very clear report and your feedback! Your example recording looks really good to me, given that it comes probably from 2P GRIN lens imaging!
I think there is no error from your side, both in the application and in the interpretation of the algorithm.
Your intuition that the output of the algorithm should be more precisely interpreted as expected number of spikes is correct. It is, therefore, strictly speaking, wrong to use the term spike probability. I decided to use spike probability because I thought the difference between expected number of spikes and expected probability of a spike would not make a difference to most users ... but I can see how this can result in confusion.
To ensure backwards compatibility and consistency with the paper, I would prefer to leave spike probability, but I will add an explanation that reflects your comment. And I will also leave your issue un-closed for the moment, so it has more visibility for others who might run into the same confusion.
Let me know if you find other issues!
Best wishes,
Peter
P.S. Thanks for spotting the typo as well!
Hi Peter,
Thanks for the quick response! I get why you had originally chosen "spike probability", for simplicity. For future users, I think a FAQ entry like "Can the spike probability be greater than 1?" would probably be helpful. (And yep, the 2P recording was performed through a GRIN lens.)
-Tony
That's a good suggestion. I have added such an entry. I some point in the future I will go through the entire FAQ again and check whether it is still consistent.
Peter
Hi again.
Since we last spoke, I have been applying CASCADE to my two-photon Ca2+ imaging datasets and generally have been very pleased with the transformation of Ca2+ fluorescence traces into inferred spike rate traces. CASCADE captures many of the intuitions that I have with regards to the dynamics of GECIs and how they relate to underlying electrical activity of the neuron.
This time, I wanted to ask about the interpretation of the
spike_prob
output of CASCADE. The FAQ of the "Calibrated_spike_inference_with_Cascade" demo script states:Thus, my initial interpretation was that the value of
spike_prob
for a frame was the "probability" that a spike had occurred within that frame. As a "probability", I expected that the value would be constrained to be between 0 and 1.However, during my use of CASCADE on my data (technical details at the end of the post), I observed that it's possible for the
spike_prob
value to be greater than 1 for some frames. Here's an example:So first, I wanted to ask if you thought my interpretation of
spike_prob
is wrong, and whether you thought that values ofspike_prob
that exceed 1 was due to an error in my use of the algorithm.I then started thinking about the interpretation of
spike_prob
. The FAQ states that:(By the way, typo on "rates".)
If
spike_prob
, as a probability, is constrained to be between 0 and 1, then this implies that the maximum spike rate that can be predicted by CASCADE is the imaging frame rate (since it would be frame rate multiplied by prob=1). This, however, didn't seem right to me, since a neuron could easily spike more rapidly than the imaging frame rate. For example, if one were imaging at 5 Hz, i.e. with 200 ms frame periods, a neuron can definitely spike more than once within that frame.Thus, I started to wonder whether the
spike_prob
variable should be interpreted not as a "probability" that a spike occurred within that frame, but instead the expected number of spikes in that frame. This way, I think the output retains the stated properties ofspike_prob
(e.g. sum the trace in time to get total # of spikes; multiply by the frame rate to get the instantaneous spike rate), but without the intuition that the values should be constrained to lie between 0 and 1. I wonder what you think?Here are some technical details on my use of CASCADE (i.e. in the image above):
I'd be happy to share the DFF traces with you if that would help the discussion.
Thanks again for sharing CASCADE!