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.
Computational NeuroscienceUniversity of Washington
About this Course
Skills you will gain
- 5 stars71.40%
- 4 stars22.30%
- 3 stars3.90%
- 2 stars1.60%
- 1 star0.80%
TOP REVIEWS FROM COMPUTATIONAL NEUROSCIENCE
A very nice introduction to Computational Neuroscience world. The main course advantage is the matching between theory and practice (programming).
This course is an excellent introduction to the field of computational neuroscience, with engaging lectures and interesting assignments that make learning the material easy.
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.
This is a wonderful start for a biologist , to get idea of concepts of learning . An advanced course focused more on brain circuitry is suggested.
Thanks a lot
Frequently Asked Questions
When will I have access to the lectures and assignments?
What will I get if I purchase the Certificate?
Is financial aid available?
More questions? Visit the Learner Help Center.