Back to Computational Neuroscience

4.7

584 ratings

•

136 reviews

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....

Jun 15, 2017

This course is an excellent introduction to the field of computational neuroscience, with engaging lectures and interesting assignments that make learning the material easy.

May 25, 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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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 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 Serena R

•Aug 31, 2017

I found this course helpful and inspiring for my research activity. I suggest it to anyone who has basic mathematical skills.

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 Ivy T

•Oct 26, 2017

I'm a professor in psychiatry with a background in clinical psychology. I conduct clinical research to understand the neural mechanisms involved in psychiatric diseases. I found the course very informative and covers topics in computational neuroscience that are critical to further my research in the computational direction.

The course involves a moderate amount of math, which is absolutely necessary to understand the materials. For someone like me who did calculus more than 20 years ago (i.e., rusty), I often found the explanation of the math too fast. I had to pause the videos multiple times to digest the formulas and re-watch some videos to get a true understanding of the materials in order to complete the quizzes successfully (especially in later weeks as the concepts get more advanced). The supplementary tutorials by Rich Pang are extremely helpful. He talks at a slower pace, allowing time for you to think along the way. He is also very good at helping you to get an intuitive understanding of the complex concepts. I would recommend watching Rich's tutorials before watching the lecture videos. That way, you would understand the lectures more readily.

The quizzes are overall well designed and helpful in terms of facilitating the consolidation of your understanding of the concepts and methods covered in that particular week. I don't know if it's just me. I tended to spend a lot more time than the estimated time (e.g., 3 hours instead of 1 hour) to complete and pass a quiz (especially later ones that involve more Matlab programming).

Overall, I found this course very useful and overall well constructed.

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 Marek C

•Apr 09, 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 Hui L

•Feb 26, 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 Wojtek P

•Jul 08, 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 lcy9086

•Mar 16, 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 Moustapha M A

•May 26, 2018

The course over all was very good but I didnt given it five because of the following : in course 2-5 the lectures were not coherent and the there was no expalantion for how certain experiments or measurments were done and hence natural progression to associate the mathematics. The lecturer tends to speak fast and sometimes eat her words so there was absence of clarity . The lectures were not well structured . on the otherhand lectures 6-8 were much clearer in presenation and scope and more linked with the quizes.

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 Claudio G

•May 22, 2018

I have really liked this course,but there is a lot of statistics I didn't expect to find at the beginning. Ihave given me exactly the flavor of what Computational Neuroscience is and what are the field of applications, which are REALLY interesting. Honestly I have found a bit too condensed the part regarding the description of "cause" and all the related statistic stuff which I think should deserve some 1 or 2 videos with solved problems. All summed up, I think this course is really worth of taking. Best regards to the professors and to the mentors and to those who have given me a lot of help with their posting on the forum. Their doubts and the relative answers have really been enlightening for driving me towards a better understanding of the matter. Thank you to all of you.

By Jeff C

•Nov 14, 2016

In general very good, but some concepts are rushed over due to the short length of the course.

By Huzi C

•Feb 14, 2017

Great course and really helpful for me.

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 Vanya E

•Jul 09, 2017

Great overview of a really cool field, gives nice intuitions for ideas in computational neuroscience.

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 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 Cezary W

•Sep 27, 2017

Quite interesting. I would see more explanation of some phenomena, though.

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 shiyang t

•Jul 29, 2019

Being a high school student with zero background in computer programming, i find this course a bit hard.

By Beatriz B

•Aug 03, 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 Patricia R

•Aug 14, 2019

Interesting but too complicated for beginners

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