Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

University of Washington

Computational Neuroscience

University of Washington via Coursera


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.

Taught by

Rajesh Rao and Adrienne Fairhall


3.6 rating, based on 9 Class Central reviews

Start your review of Computational Neuroscience

  • Anonymous

    Anonymous completed this course.

    First Lecture Series was very good and interesting. If a "Cloud-Based" Lab was added to get real experience with Neurons and Modelling via Octave/Matlab would really be an improvement over existing educational programs.
  • Kristina Šekrst completed this course and found the course difficulty to be hard.

    This course was a huge inspiration, but it required a lot of prerequisites in high-level mathematics and programming. Even though I've passed the course with high marks, the programming exercises took a lot of time, and the quizzes weren't easy. It's not an introductory course, but the first few lectures ought to be enough if you're looking for a glance into what CN is. However, I'd recommend giving more practical introduction to certain theoretical approaches studied, for example, to combine the formula given in the slides with ways how to calculate them in Python and similar. I'm giving it one star more because the lecturers had done a huge job by themselves.
  • Ali Taheri completed this course.

  • Amaan Cheval

    Amaan Cheval completed this course.

  • Daniel Finol

    Daniel Finol completed this course.

  • Colin Khein completed this course.

  • Martin Nilsson

    Martin Nilsson completed this course.

  • Profile image for Alex Ivanov
    Alex Ivanov

    Alex Ivanov completed this course.

  • Filip Melinscak

    Filip Melinscak completed this course.

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.