MOOC Review: Computational Neuroscience

by Kuei-Ti Lu

Course Name: 
Computational Neuroscience
Link: 
https://www.coursera.org/course/compneuro
Institution: 
University of Washington
Instructors: 
Rajesh P. N. Rao, Adrienne Fairhall

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Videos: 

The total length of the videos per week was typical for a MOOC, but the breadth was large - the subject was interdisciplinary. The first week was an introduction to neurobiology; biology and chemistry were the main focus. However, the following weeks were mainly on mathematical/statistical models in neuroscience. Some of these involved signal processing; some information theory; some machine learning; and etc. Week 5 involved modeling neurons using circuits, which are mainly used in electrical engineering.

Because of the variety of the areas covered, one must have solid mathematics basics to understand the materials. Derivations beyond the prerequisites were done in the lectures, and only simple models were covered in the course.

The instructors spoke clearly, and slides as well as subtitles were provided. Following the lectures should not be a big issue for most students (provided that they have met the prerequisites).

Homework Quizzes: 

The quizzes followed the main topics in the videos. Some MATLAB programming was used for a few problems. For most such problems, one might have to read the comments in the code provided to know what code to write to meet the formats of the variables given by the code provided. Other than that, the programming part should not be difficult.

Supplementary Materials: 

Additional materials about the topics could be found (in the formats of texts, videos, and etc.). Moreover, math tutorials were provided by a community TA. so that people who had to learn or review the math used in the course had resources. MATLAB tutorials could also be found.

Difficulty: 

The difficulties might vary a lot for people of different backgrounds. For those from math, statistics, sciences, and engineering, most materials should not be problems. For those from biology and chemistry, this course might be quite challenging due to the math used in the course but not typically used in most areas in undergraduate biology and chemistry.

Overall: 

The course was a survey on computational neuroscience. The breadth of the topics was large, and therefore, those who liked to learn more in depth had to find other resources. Nevertheless, this course should serve as a good introduction. The course might also be interesting to those who like to apply math to different areas.

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.