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.
- 5 stars71.40%
- 4 stars22.30%
- 3 stars3.90%
- 2 stars1.60%
- 1 star0.80%
A well curated course on an equally interesting topic! I've caught an interest for Computational Neuroscience after this experience.
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.
Pretty good. A bit of mathematical ambiguity and lax notational conventions, but the course content was solid and presented clearly.
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