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.
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
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.