Signal Detection Theory and Rods: Thoughts on Fred Rieke's Guest Lecture

This week, the Computational Neuroscience course on Coursera has a guest lecture by Fred Rieke, a professor in Department of Physiology & Biophysics at University of Washington. In the lecture, he talks about single photon detection with rod signals and noise under dim light.

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My summary of his talk: 

Between the rods and the rod bipolar cell connected to them, there can be a nonlinear filter having a threshold to determine whether a photon is detected or not. The threshold is one for the amplitude of the received signal, which can be contributed by the signal resulted from the photon or noise.

How to pick a threshold to have a higher possibility of correctly determining whether a photon is received is the main topic of the talk. If applying the maximum likelihood using the probability distribution of the amplitude of the signal and noise without considering the probability of receiving a photon, the threshold picked is lower than that if the probability of receiving a photon, which is quite low, is considered.

Therefore, a reasonable pick on the threshold is one at which the probability of the received signal being noise is extremely low. What is emphasized is that the prior probability matters.
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In his lecture, my favorite part is the application of the signal detection theory, which I used when learning basic radar detection concepts, to single photon detection in neuroscience. For me, this is a new application of the same theory I learned before, which is interesting.

From his lecture, I learned more about the rods beyond what I had learned before about the nervous system and a beautiful model (I assume it is a simplified one) about the rod signals.

The most difficult part in the lecture, for me, is the new materials about the cells. Although not long, it took me a little time learning about the rod and cone bipolar cells.

Thanks to my random signal processing professor, whose help made me able to enjoy this lecture quite a lot, and thanks to the Computational Neuroscience course instructors and staff, as well as Fred Rieke, who brought this good experience for me.