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.
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I really enjoyed this course and think that there was a good variety of material that allowed people of many different backgrounds to take at least one thing away from this.
This course is an excellent introduction to the field of computational neuroscience, with engaging lectures and interesting assignments that make learning the material easy.
Great course! Really enjoyed the variety of topics and the just enough computational work in the quiz's. And that Eigen hat had me smiling and laughing about it for a week.
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.
interesting instructor and interesting content. Now I know more about the theoretical research related to neuro function and its connection to machine learning now.
A good look at mathematical models focusing mainly at the synapse and neuron level. The math came a little fast and furious for my 30+ years antique math training.
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.\n\nThanks a lot
Very challenging course with fascinating new content that refers to a lot of research in the area! Good start for someone considering computational neuroscience.
Starts off great but get rushed 3/4ths into the course. Too much content, too little explanation, but recovers swiftly to end on a high.\n\nRecommended
Overall - A good introductory course. But the last week, reinforcement learning and neural networks, could have involved programming questions.
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.
Pretty good. A bit of mathematical ambiguity and lax notational conventions, but the course content was solid and presented clearly.
An amazing course for people with a computer science background wishing to explore the world of human brain from CS perspective.
I found this course helpful and inspiring for my research activity. I suggest it to anyone who has basic mathematical skills.
Overall I enjoyed this class, but towards the end it gets more into machine learning and away from the neuroscience.
Excellent Course! Very in-depth and informative! Exceptional faculty and extensive supplementary material as well!
Quite nice if you follow the recommended textbook - Peter Dayan - with it. The lectures touch important point.
this course is perfect for who wants to get familiar with computational neuroscience general concepts.
Great overview of a really cool field, gives nice intuitions for ideas in computational neuroscience.
Excellent overview of the different areas of computational neuroscience taught by engaging academics.
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