Computational Neuroscience

Biologically inspired neural networks perform a range of computational functions analogous to cognitive processing in the living brain, e.g., as pattern recognition, associative learning, motor control, forecast and prediction. In this course we will describe basic principles and algorithms underlying these functions. With a sandbox neural-network construction software we will implement these networks and familiarize with their properties and functions.

1. The assessment consists of weekly peer reviewed quiz questions on the basis of the weekly projects and a final presentation on a chosen topic. The presentation will be delivered as a video (of about 10 minutes).

1.a There is no written exam.

2. There is no resit.

3. The final grade is composed as follows: 

• Presentation grade (50%)

• Quiz questions (50%)

4. No registration is needed for final exam (projects are delivered by the last week of the course)

5. Students needing to turn work in late due to personal circumstances must contact the Academic Counsellor prior to turning it in to make arrangements. Other late work might not be graded.