Computational neurosciences: biophysics

Summary

The course introduces students to a synthesis of modern neuroscience and state-of-the-art data management, modelling and computing technologies with a focus on the biophysical level.

Content

Keywords

data management, biophysically detailed modeling, scientific computing, simulation

Learning Prerequisites

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

The course will take place in presence on the EPFL campus.

Structure: each week there will be

• 2x45min lecture
• 45min interactive discussion with the teachers & TAs
• 45min introduction of homework exercises, Q&A, group work (TAs present)

Exercises

• practical programming/problem solving on topics from the lectures
• done in groups, which remain for the entire semester
• are graded on a weekly basis

Expected student activities

• Students attend lectures
• 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: 70%
• Continuous control (homework): 30%

Resources

Moodle Link

• https://go.epfl.ch/NX-450