Courses

Acquisition and Analysis of Neural Data: Acquisition of neural data (1st semester): large scale signals (fMRI, Berlin EEG, MEG etc) and cellular signals, hands‐on experience with neural data acquisition techniques. Analysis of neural data (2nd semester): firing rates, spike statistics, spike statistics and the neural code, neural encoding, neural decoding, discrimination and population decoding, information theory, statistical analysis of EEG) data, spatial filters, classification, adaptive classifiers.
Models of Higher Brain Functions
Models of Neural Systems
Stochastic Partial Differential Equations
Stochastic Processes in Neuroscience
Theoretical and Computational Neuroscience: Collective dynamics of biological neural Networks II
Neural Networks and Information Processing
Theoretical Neurosciences: Computational Neuroscience I
Theoretical Neurosciences: Computational Neuroscience II
Programming
Advanced Course on Neural Data Analysis
Introduction to Scientific Programming in Python with Application to Neural Data Analysis
Neurophysics (Advanced Studies I)
Neuro Dynamics (alternating with Neuro-informatics)
Neuro-Informatics
Probabilistic Modeling of Perception and Cognition
Machine Learning I
Machine Learning II
Action & Cognition I
Action & Cognition II
Bayesian Statistics
Cognitive Human-Computer Interaction
Colloquium Computer Vision
Colloquium of the Institute of Cognitive Science
Colloquium of the PhD Programme
Masters Thesis Project on Visual Perception
Sensation and Perception
SFB 936 ONLINE COURSE "NETWORK NEUROSCIENCE"
Mathematical topics in the neurosciences
Spiking Networks
Theoretical neuroscience I
Theoretical Neuroscience
Introduction to Computational Neuroscience
Computational Neuroscience: A Lecture Series from Models to Applications
Fundamentals in Neuroscience
Theoretical Biophysics and Cellular Physiology
The Neural Code
Models of neural systems
Neural Dynamics
Computational Neuroscience - Introduction
Machine Intelligence
Statistical mechanics of neural networks
Fundamentals of Computer Science for Neuroengineering
Fundamentals of Mathematics for Neuroengineering
Machine Learning I
Machine Learning II
Practical Short Course Methods for Computational Neuroscience
Practical Course Methods in Functional Imaging
Neuroscientific Data Analysis in Matlab
Machine Learning I
Models for Neural Circuit Development
Systems Engineering Meets Life Sciences I
Individual Project in Computational Neuroscience group
Individual Project in Auditory Neuroscience group
Introduction to Biological Data Analysis in Matlab
Computational Neuroscience - Statistical Learning
Invertebrate Neuroscience
Communication Acoustics
Neuroprosthetics
Psychoacoustics and Audiological Applications
Neural Experimental Techniques
Ethics and Neuroscience
Mathematics Prep-Course
Neurobiology Prep-Course
Applied Cognitive Modeling
Advanced Seminar in Computational Neuroscience
Spatial and Temporal Cognition
Information Dynamics/Information theoretic analysis of neural data
Theoretical Neuroscience – Correlation structure of neuronal networks
Statistical Physics
Modeling Episodic Memory and Spatial Navigation
Nonlinear dynamics
MidsummerBrains: The Virtual Brain
MidsummerBrains: Engineers in Computational Neuroscience
MidsummerBrains: Quantifying nonlinear representations in the visual systems
MidsummerBrains: Computational neuroscience and field biology
MidsummerBrains: Physics and computational neuroscience
ANDA: Cortial variability dynamics experimental observations and mechanistic models
Online Lecture: Theoretical Neuroscience I
Online Lecture: Theoretical Neuroscience II