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Learner Reviews & Feedback for Computational Neuroscience by University of Washington

4.6
stars
835 ratings
197 reviews

About the Course

This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. Specific topics that will be covered include representation of information by spiking neurons, processing of information in neural networks, and algorithms for adaptation and learning. We will make use of Matlab/Octave/Python demonstrations and exercises to gain a deeper understanding of concepts and methods introduced in the course. The course is primarily aimed at third- or fourth-year undergraduates and beginning graduate students, as well as professionals and distance learners interested in learning how the brain processes information....

Top reviews

AG
Jun 10, 2020

Brilliant course. For a HS student the math was challenging, but the quizzes and assignments were perfect. The tutorials and supplementary materials are super helpful. All in all, I loved it.

JB
May 24, 2019

I really enjoyed this course and think that there was a good variety of material that allowed people of many different backgrounds to take at least one thing away from this.

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151 - 175 of 195 Reviews for Computational Neuroscience

By Wilder R

Jun 28, 2017

I loved the course and the way Professors Rajesh and Adrienne conducted it. I only think the slides and lecture notes could have some more material. I'm a Software Engineer, with a background in Computer Science, but I have been far from math for quite some time (that's why I'm now doing a Cauculus 1 course). I got lost a few times in the quizzes due to lack of information.

But I loved the course and all the new knowledge I acquired. I will certainly recommend. it.

By Misael A A M

Nov 25, 2020

This is an awesome course! I love it because it brings you the real neural part of the artificial neural networks, a thing all courses I've seen till now misses or gives at a really high level.

I don't give it 5 stars because the lectures are sometimes really boring. And I'm not complaining about the topics itself, but the videos are on average 20 minutes long, and the voices are really low, so it's really difficult to keep the focus on.

By Shengliang D

Jan 18, 2020

The contents are well organized and arranged corresponding to the textbook Theoretical Neuroscience. There are supplementary materials for the lecture of each week. The assignments are very helpful for understanding the lectures, with code and data for Matlab, Python 2 and Python 3, which is very friendly for people who are only familiar with some of them. It would be better if the assignments could cover more about the lecture.

By Joost v T

Dec 2, 2020

Great course with great lectures given by great people. I liked the variety of topics in the course and all the fun little jokes and trivia offered in the lectures. The quizes were of fairly high level for me, so I really feel like I've learned something new. I would have liked to have exercises before trying the quiz though. And after the quiz it was hard to see what went wrong.

By Wojtek P

Jul 8, 2017

Extremely interesting subject, many ideas and methods presented. Basic disadvantage is a method of source which is closer to seminar rather than leacture. But, lost of details is acceptable due to a huge amount of material. Advanced mathematics from various areas is necessary to fully understand all the ideas. Anyway, I recommend the course.

By Víthor R F

Mar 10, 2018

Many of the lectures do not make a plenty of sense relative to their quizzes. The lectures are rather theoretical and the quizzes are rather practical. Also, one of the professors have better didactics than the other. Either way, it was quite an adventure (my hat almost didn't survive).

By Manuel P

Dec 15, 2017

I enjoyed the course very much and hopefully learned quite a bit about how to model neurons and some interesting new ways to look at methods like perceptrons and PCA. The course videos are short by very dense. Make sure you make enough notes and prepare enough time for all of them.

By george v

Mar 18, 2017

Very good teaching skills by both professors and interesting guest lectures and tutorials. Assignements that demand your full attention. I would like some more depth as far as the developement of programming skills and the practice. Great intuition and explanation.

By lcy9086

Mar 15, 2018

This course provides you with a brief introduction to computational neural science. You can benefit from it as long as you have basis in calculus and linear algebra. But for those who want to get the best from it, you need to build up your mathematics.

By Krasin G

Nov 16, 2016

This is a very interesting course that provides many interesting ideas. At the same time it is quite challenging. Solid background in probability theory, linear algebra and signal processing is needed. Considering it "Introductory" level is misleading.

By Marek C

Apr 9, 2018

Good introduction to the topic. Course quite easy for engineers, may be quite challenging fro non-engineers. I didn't like quizes - they were too easy and were not provoking too much creative thinking. They were also easier than the lecture material.

By Peter K

May 30, 2017

Great course introducing fundamental concepts in computational neuroscience. People with weak mathematical background can master it although from time to time some more clarification could be helpful. Thanks so much for providing this :-)

By Medha S

Feb 25, 2021

It was a little difficult to get all the mathematical concepts in such a short time, but I really enjoyed the course and it gave me a good insight of what computational neuroscience encompasses.

Thank you for a wonderful course!

By Adrian M

Apr 3, 2021

Maybe adding more coding examples during course videos might be useful to get a better understanding of how to implement the concepts. Quiz code questions are good for that, but maybe more guided examples might be great.

By Chiang Y

Jul 30, 2020

Pretty comprehensive for beginners, the only drawback is that the course doesn't offer organized ppt or notes for review. Writing notes took me lots of unnecessary time so I suggest a more efficient teaching method.

By Diego J V (

Feb 20, 2017

This course serves as a nice introduction to the field of computational neuroscience. However, at some points, more than basic knowledge of differential equations and probability & statistics is needed.

By Gustavo S d S

Nov 15, 2016

Learnt concepts about Neural Networks, Supervised / Unsupervised / Reinforcement Learning. Covers topics about Information Theory, Statistic and Probability. Matlab / Python assignments.

By Beatriz B

Aug 3, 2019

In my opinion, the course level ought to be intermediate, not beginner. You can take more out of the course if you already have knowledge in this, or related, areas.

By Hui L

Feb 25, 2017

interesting instructor and interesting content. Now I know more about the theoretical research related to neuro function and its connection to machine learning now.

By Mark A

Jul 13, 2017

A good look at mathematical models focusing mainly at the synapse and neuron level. The math came a little fast and furious for my 30+ years antique math training.

By Anurag M

Feb 3, 2019

Starts off great but get rushed 3/4ths into the course. Too much content, too little explanation, but recovers swiftly to end on a high.

Recommended

By Akshay K J

Aug 17, 2017

Overall - A good introductory course. But the last week, reinforcement learning and neural networks, could have involved programming questions.

By Driss A L

Dec 2, 2018

As a self-paced student, I like this kind of course. I hope to see a whole specialization in this field with final capstone project. Thanks.

By Pho H

Dec 27, 2018

Pretty good. A bit of mathematical ambiguity and lax notational conventions, but the course content was solid and presented clearly.

By Ricardo C

Oct 27, 2020

it delivers what it promisses: a first grasp of computational neurosciences, with a good overview of the fundamental concepts.