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