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
- Computational Neuroscience
- Artificial Neural Network
- Reinforcement Learning
- Biological Neuron Model
Syllabus - What you will learn from this course
Introduction & Basic Neurobiology (Rajesh Rao)
What do Neurons Encode? Neural Encoding Models (Adrienne Fairhall)
Extracting Information from Neurons: Neural Decoding (Adrienne Fairhall)
Information Theory & Neural Coding (Adrienne Fairhall)
- 5 stars71.52%
- 4 stars22.22%
- 3 stars3.86%
- 2 stars1.58%
- 1 star0.79%
TOP REVIEWS FROM COMPUTATIONAL NEUROSCIENCE
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.
A very nice introduction to Computational Neuroscience world. The main course advantage is the matching between theory and practice (programming).
Its an amazing course. You will love the way they teach. I'm so glad to get guidance under Prof . Rajesh through this course. One word "Its great".
Excellent course, very clearly and well explained, suitable for beginners. Also, Rajesh's sense of humor makes the course very enjoyable :) Highly recommended!
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