Coursera – University of Washington – Computational Neuroscience

Coursera – University of Washington – Computational Neuroscience

Name Product: Coursera – University of Washington – Computational Neuroscience
Download Size: 831 MB
COST: Your Free
Author: Rajesh P. N. Rao & Adrienne Fairhall
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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 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.

Beginning with the Spring 2015 offering, Signature Track and Verified Certificates are available for this class. The formatting on the verified certificate is very slightly different from those for courses from other institutions. An example certificate is here. (The date on the actual certificates will be different.)
Course Syllabus

Topics covered include:

1. Basic Neurobiology
2. Neural Encoding and Decoding Techniques
3. Information Theory and Neural Coding
4. Single Neuron Models (Biophysical and Simplified)
5. Synapse and Network Models (Feedforward and Recurrent)
6. Synaptic Plasticity and Learning

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