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 Alex U

•Aug 02, 2017

With a extremely rich content, this course is a challenge for students, even for those with maths, ML or neuroscience background. The course requires students to master knowledge of these three fields, but it will prove that it DESERVES the efforts.

By Shwetank P

•Apr 27, 2019

This course will be one of the most satisfying pursuits for any individual interested in exploring the intersection of neurobiology, AI and Statistics. The course is really well-rounded covering all major portions in the computational neuroscience. The supplementary material provided for exploration is really intriguing and a must go for people interested in understanding the gory details behind the equations. Hands down! this one is the best MOOC experience so far for me.

By Jacob B

•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.

By Matthew W

•Jun 23, 2019

As a beginning PhD student in computational neuroscience, I found this course to be incredibly useful as a refresher. And as an introduction to the subject, it is incredibly engaging, interesting and, of course, one fun adventure! Many thanks to both Rajesh and Adrienne for this course!

By Shahbaz K

•Jun 25, 2019

Made it really easy for me to get into this field. So very inspired.

By Yi-Yin H

•Jun 29, 2019

It was an amazing journey!

By Chinmay S H

•Jul 29, 2019

I learned a great amount from this course. Now, I want to learn more about neural coding

By NA P

•Jul 29, 2019

Perfect course. The only feedback I would give is, if possible, to include slides in the weekly material for review instead of just text. Thank you for this amazing tour through Computational Neuroscience!

By Vili V

•Jul 28, 2019

Very enjoyable course!

By Julio C d C M

•Jul 31, 2019

A very nice introduction to Computational Neuroscience world. The main course advantage is the matching between theory and practice (programming).

By Hariharan L

•Aug 24, 2019

I find it really interesting

By 小妮

•Aug 27, 2019

Great! While hope for more teaching on programming!

By Keith R

•Jul 02, 2019

Excellent Course! Very in-depth and informative! Exceptional faculty and extensive supplementary material as well!

By akhil v

•Jul 08, 2019

Intreginity of strategy of learning from stractch

By 熊华东

•Oct 04, 2019

非常好的一门课，第六周有点不够详细，第七周讲机器学习，我是有一些基础的才看懂。

By Cian M M

•Oct 06, 2019

Very nice indeed!

By Varun M M

•Nov 01, 2019

It is just perfect for an introductory level course! Paced sort of like a web series, it keeps you hooked throughout. I absolutely loved it, and as a Physics graduate going into the world of computational neuroscience, this course has helped me with building my comp neuro arsenal.

By Yuyan Z

•Oct 30, 2019

A very good introduction to computational neuroscience! The course demands a relatively high level of mathematics (such as linear algebra, optimization problems, etc.), but all of them are quite clearly explained in the lectures.

By Swarn ( W

•Nov 03, 2019

The discussion forums helped a lot.

By Amogh M

•Nov 19, 2019

Excellent course. Got to learn concepts across a wide range of subjects like information theory, statistics, biology, chemistry, etc. (I could go on).

Coming from a CS + ML background, this helped me appreciate the building blocks of abstractions that we can so easily take for granted in the age of Deep Learning. It really helps to learn and think about these things because it makes you realize how nothing is set in stone and the popularity of one model (MLPs) has a lot to do with history and not just mathematics.

By Driss A L

•Dec 02, 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 28, 2018

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

By Aditya A

•Mar 28, 2019

I liked the course. I enjoyed solving the problems and I am now confident in learning more advanced concepts and getting my hands dirty in neural networks and machine learning.

I only have one complaint like suggestion, if only the TAs or the instructors could show some examples of solutions or algorithms for the concepts, it would have been much easier. Although, i have understood the concepts, I have not yet grasped the implementations of the concepts in actual codes and programs. Please update the course regarding that. Thanks a lot again to Rajesh, Adrienne and Richard.

By Anurag M

•Feb 03, 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 Renaldas Z

•Jun 30, 2017

Great course, if a little bit outdated today.

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