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.
Founded in 1861, the University of Washington is one of the oldest state-supported institutions of higher education on the West Coast and is one of the preeminent research universities in the world.
- 5 stars71.41%
- 4 stars22.21%
- 3 stars4.03%
- 2 stars1.59%
- 1 star0.74%
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.
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.
interesting instructor and interesting content. Now I know more about the theoretical research related to neuro function and its connection to machine learning now.