Summary

"In silico Neuroscience" introduces students to a synthesis of modern neuroscience and state-of-the-art data management, modelling and computing technologies.

Content

Learning Prerequisites

Recommended courses

Neuroscience II

Introduction to programming

Projects in informatics

Important concepts to start the course

general knowledge on cellular neuroscience

experience in elementary programming (preferentially python)

Learning Outcomes

By the end of the course, the student must be able to:

• Interpret discrepancies between experimental findings
• Assess / Evaluate different level of detail formulations of models
• Integrate biological facts into detailed neuron and tissue models
• Apply model concepts in simulations
• Exploit standard modelling and simulation software
• Analyze model predictions
• Explain formalisms and approaches in simulation software

Teaching methods

Due to the general COVID-19 situation, the course will be given remotely and no physical presence on campus is required for this course (apart from final exam)

Structure: each week there will be

• a pre-recorded 90min lecture (which students can watch Tuesdays, 8:15-10am, or beforehand); note: the lecture of the first week is given live
• 45min interactive discussion with the teachers (Tuesdays, 10:15-11am)
• 45min group work on exercises (TAs present)

Exercises

• practical programming/problem solving on topics from the lectures
• done in groups (~3 students/group), which remain for the entire semester
• are graded on a weekly basis (20% of grade)
• prepare for the final exam

Expected student activities

• Students review lecture material on their own
• Students actively participate in the discussion on the topics of the lecture in the discussion session
• Students complete weekly practical programming assignments relevant to the week’s lecture in groups
• Students write final exam in exam period

Assessment methods

Written exam (80%);

Continuous control (20%)