University of Washington - coursera: Computational Neuroscience
Introduction
This coursera course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. Computational Neuroscience has three main puposes:
- Create descriptive model of the brain: how do neurons respond to external stimuli ? How can we extract informations from neurons ?
- Create mechanistic models of brain cells and networks: how can we simulate the behaviour of the single neuron ? How do we simulate a whole network of neurons ?
- Create interpretive models of the brain: Why neurons / networks operate the way they do ?
The reason I took this course is both because I am interested on how the brain works and because of my related interest in Artificial Intelligence.
Syllabus
This is a 8 weeks course. As most coursera classes, it is composed of videos, quizzes and programming assignments (automatically checked online). Completing all this takes me around 4 hours a week ; about 1 hour and a half watching videos, and 2 hours and half answering quizzes and programming assignments.
- Week 1: Course Introduction and Basic Neurobiology
- Week 2: What do Neurons Encode? Neural Encoding Models
- Week 3: Extracting Information from Neurons: Neural Decoding
- Week 4: Information and Coding Principles
- Week 5: Simulating the Brain from the Ground Up: Models of Single Neurons
- Week 6: Modeling Synapses and Networks of Neurons
- Week 7: How do Brains Learn? Modeling Synaptic Plasticity and Learning
- Week 8: Learning to Act: Reinforcement Learning
Feedback
The quality of the videos were great, I found the course content a little bit difficult to understand because of the very high mathematical level.
