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.
First Lecture Series was very good and interesting. If a "Cloud-Based" Lab was added to get real experience with Neurons and Modelling via Octave/Matlab code...it would really be an improvement over existing educational programs.
Kristina Šekrst completed this course and found the course difficulty to be hard.
This course was a huge inspiration, but it required a lot of prerequisites in high-level mathematics and programming. Even though I've passed the course with high marks, the programming exercises took a lot of time, and the quizzes weren't easy. It's not an introductory course, but the first few lectures ought to be enough if you're looking for a glance into what CN is. However, I'd recommend giving more practical introduction to certain theoretical approaches studied, for example, to combine the formula given in the slides with ways how to calculate them in Python and similar. I'm giving it one star more because the lecturers had done a huge job by themselves.