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Computational Neuroscience

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.
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University of Washington

Rating 4.4 based on 119 ratings
Length 9 weeks
Starts Jun 29 (3 weeks ago)
Cost $49
From University of Washington via Coursera
Instructors Rajesh P. N. Rao, Adrienne Fairhall
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Science Data Science
Tags Computer Science Life Sciences Data Science Algorithms Machine Learning Bioinformatics Health Informatics

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What people are saying

According to other learners, here's what you need to know

computational neuroscience in 21 reviews

Ihave given me exactly the flavor of what Computational Neuroscience is and what are the field of applications, which are REALLY interesting.

Extremely enlightening course on how Neuron's work and the science of computational neuroscience.

I believe this course should be renamed to "An Introduction to Computational Neuroscience" 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.

I found the course very informative and covers topics in computational neuroscience that are critical to further my research in the computational direction.

In regards to computational neuroscience as a course, the material itself is beginner-level, but the math/programming is definitely not (more like intermediate/advanced).

Its an eye opener One of the best courses on computational neuroscience I found this course helpful and inspiring for my research activity.

Its a fantastic course for any one interested in the computational neuroscience field.

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linear algebra in 5 reviews

You can benefit from it as long as you have basis in calculus and linear algebra.

It has a nice structure, and the progress is quite reasonable assuming you have decent background in linear algebra and calculus derivations.

A solid background in linear algebra, statistics, and some basic calculus is recommended to get the most out of the course.

Solid background in probability theory, linear algebra and signal processing is needed.

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rajesh and adrienne in 4 reviews

I loved the course and the way Professors Rajesh and Adrienne conducted it.

I really appreciated the effort of Rajesh and Adrienne to explain the complex mechanisms of neurons and brain functions in a clear and enjoyable way.

Thanks Rajesh and Adrienne!

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machine learning in 4 reviews

But it is exciting for me as a Machine learning and deep learning practitioner!!

Thanks so much for providing this :-) Great class for both professional in machine learning and computational neuroscience.

To get the most out of this course you should have some background knowledge in programming, machine learning, information theory, electrical circuits, bioelectricity or else you might get overwhelmed by the complex math used in 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.

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very interesting in 4 reviews

Very interesting and well taught course.

This is a very interesting course that provides many interesting ideas.

Very interesting and well organised course.

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rich pang in 3 reviews

Supplementary Video Tutorials by Rich Pang were awesomely simplistic and understandable but not enough for this course... All we need is more detailed and well explained examples...In sort, after successfuly completing the course, I can say that I havent really learned anything.

The supplementary tutorials by Rich Pang are extremely helpful.

Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Basic Training $50k

Computational Scientist Contractor $62k

EMT-Basic 1 $63k

Basic Education $67k

Undergraduate Computational Researcher $68k

Computational Biologist 1 $93k

Associate Computational Biologist 2 $95k

Computational Linguist Job $123k

Computational Lithography $141k

Computational Mathematician $148k

Senior Computational Linguist $153k

Senior Computational Mathematician $233k

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Coursera

&

University of Washington

Rating 4.4 based on 119 ratings
Length 9 weeks
Starts Jun 29 (3 weeks ago)
Cost $49
From University of Washington via Coursera
Instructors Rajesh P. N. Rao, Adrienne Fairhall
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Science Data Science
Tags Computer Science Life Sciences Data Science Algorithms Machine Learning Bioinformatics Health Informatics

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