Computational Neuroscience

What it is about

You will learn concepts and implementations of central models and methods of Computational Neuroscience in Python. At the end you will be able to implement single-neuron and network models, analyze their properties, and transfer models to new questions, as well as apply fundamental methods of Machine Learning and information analysis.

What you will be able to do

• Describe the field and subareas of Computational Neuroscience
• Understand different modeling levels and approaches
• Understand general concepts of model fitting (e.g., mean squared error)
• Implement and compare classical neural models (e.g., LIF, Hodgkin-Huxley, Wilson-Cowan, Hopfield)
• Implement and analyze neural network models
• Decompose Computational-Neuroscience papers into their conveyed components

What the module consists of

• VorlesungIntroduction to neural models and concepts
• ÜbungenCase studies on the application and combination of lecture content
• Hands-on-TutorialsDeepening through practical programming with exercise sheets
• Literaturarbeit/PräsentationReading model descriptions in the scientific literature and discussion of selected models

Teaching method

• Vorlesungen (PowerPoint)Introduction to models and concepts
• Übungsblätter und Whiteboard-DiskussionenPracticing and discussing practical tasks
• Hands-on-Tutorials in PythonStrengthening implementation and analysis skills
• Fallstudien und LiteraturdiskussionTransfer and critical engagement with scientific models