Computational Neuroscience I: Models of Neurons and Networks Lecturers: Aertsen, Kumar, Rotter
Class type: Lecture + Exercises
Amount: 1 + 2 hours per week
Semester: Winter
Core-Program
ECTS: 3 Topics: - Stochastic theory of ionic channels
- Theory of action potential generation
- The integrate-and-fire neuron model
- Stochastic theory of synaptic integration
- Stochastic theory of neuronal spiking
- Stochastic dynamics of recurrent networks
- Conductance based neurons and networks
- Cable theory and dendritic integration
- Correlations between pairs of neurons
- Population dynamics or recurrent networks
- Higher-order correlations in neuronal populations
- Synfire chains and pulse packets
- Random graphs and structured networks
- Activity dynamics of complex networks
- Synaptic dynamics and synaptic plasticity
« back to top Computational Neuroscience II: Data Analysis Lecturers: Aertsen, Arieli, Boucsein, Egert, Kumar, Mehring, Rotter, Staude
Class type: Lecture + Exercises
Amount: 1 + 2 hours per week
Semester: Summer
Core-Program
ECTS: 3 Topics: - different types of neuronal signals: SUA, MUA, LFP, ECoG, EEG
- filtering and sampling, analog and digital
- Fourier transform and frequency domain methods
- correlation and coherence, joint PSTH, higher-order correlations
- decoding of motor-related signals, classification, regresssion
« back to top Neurobiology I: Membranes, Neurons, Networks, and the Brain Lecturers: Aertsen, Fischbach, Metzger
Class type: Lecture
Amount: 2 hours per week
Semester: Winter
Core-Program
ECTS: 2 Content:
Introduction to the structure and functional principles underlying brain function. This includes the basic electrical properties of biological membranes, the structure and function of single neurons (dendrites, axons, synapses), the generation and exchange of action potentials, the interactions of neurons within and between neuronal networks, synaptic plasticity and learning. Emphasis will be on functional aspects of neural information processing, neural coding and neural computation. Literature:
Nicholls JG, Martin AR, Wallace BG, Fuchs, PA (2001) From Neuron to Brain. Sinauer Assoc., Sunderland MA. (4th ed)
Kandel ER, Schwartz JH, Jessell TM (2000) Principles of Neural Science. McGraw-Hill (4th ed)
Topics: - Signaling in the nervous system
- Ion channels and signaling, current and membrane potential, action potential generation
- Action potential propagation, synapses
- Networks in the brain
- Synaptic plasticity - Learning and memory
- Synaptic plasticity - Synaptogenesis and molecular mechanisms
- Genetic programming of inborn behaviour
- Neural circuits of learning in Drosophila
- Genetic dissection of aggression in invertebrates
- Neural coding
- Neural coding and decoding - Towards neuronal prostheses
- Cortical network dynamics - Variability and precision
« back to top Neurobiology II: Systems' physiology Lecturers: Aertsen, Ball, Boucsein, Cardoso, Egert, Kirsch, Kumar, Roux
Class type: Lecture
Amount: 2 hours per week
Semester: Summer
Core-Program
ECTS: 2 Content:
This lecture gives an introduction into the peripheral, subcortical and cortical parts of the sensory and motor systems. Focuses are on the visual, somatosensory and auditory system, the hippocampus, and different parts of the motor system. Furthermore, it includes the topography of system’s functions in the cortex and imaging techniques that reveal it. Topics: - Investigating systems physiology in humans
- Visual system
- Somatosensory system
- Motor system
- Auditory system
- Hippocampus and Memory
- The Prefrontal Cortex
- Higher Cognitive Functions: a comparative approach
« back to top Neurobiology III: Measurement and Model Lecturers: Rotter
Class type: Lecture + Exercises
Amount: 2 + 2 hours per week
Semester: Winter
Core-Program
ECTS: 4 Content:
Introduction to the principles and methods of measurement and model building in the brain sciences. Emphasis will be on stochastic models and statistical analysis approaches in neurobiology. Concepts and methods developed in the lecture will be illustrated and further elaborated in the accompanying exercises, which provide an essential and, hence, obligatory part of this course. Topics: - Chance Probability
- Discrete Variables
- Continous Variables
- Moments
- Conditional Probability
- Averaging, Smoothing
- Regression
- Classification
- Information Theory
- Stochastic Processes
- Markov Processes
- Poisson Process
- Renewal Process
- Neuron Models
« back to top Neurobiology IV: Systems and Signals Lecturers: Aertsen
Class type: Lecture + Exercises
Amount: 1 + 2 hours per week
Semester: Summer
Core-Program
ECTS: 3 Contents:
Introduction to the principles and methods of systems theoretical approaches and signal analyis in the brain sciences. Emphasis will be on deterministic, linear and non-linear models and advanced signal analysis techniques (both time and frequency domain approaches). Concepts and methods developed in the lecture (1h) will be illustrated and further elaborated in the accompanying exercises (2h), which provide an essential and, hence, obligatory part of this course. Topics: - Linear Systems
- Impulse Response
- Transfer Function
- Fourier Analysis
- Transfer Function (Revisited)
- A Simple Neuron Model
- Correlation Analysis
- The Spiking Neuron as a System
- Non-Linear Systems
« back to top From Mathematical Biology to Systems Biology Lecturers: Lebiedz, Timmer
Class type: Lecture + Exercises
Amount: 3 + 2 hours per week
Semester: Summer
Core-Program
ECTS: 6 Content:
The physically motivated mathematical modelling of biological systems represents an important start to quantify the, usually quite qualitative biology and thereby deliver a more dynamic understanding. Whereas the mathematical biology investigates the properties of relatively simple systems the framework of systems biology focusses the behaviour of complex networks. In the lecture the biological principles of exemplary models and their mathematical and physical properties will be discussed. Literature:
J.D. Murray: Mathematical Biology
J. Keener, J. Sneyd: Mathematical Physiology
L. Alberhina, H.V. Westerhoff: Systems Biology
E. Klipp et al.: Systems Biology in Practice Topics: - Mathematical Biology
♦ Population Dynamics
♦ Neuron Models
♦ Structure Formation
♦ Enzyme Dynamics - Systems Biology
♦ Metabolic Networks
♦ Signal Transduction Cascades
♦ Regulation of Genes
♦ Examples: Chemotaxix and JAK-STAT Signalling « back to top Biology for Engineers Lecturers: Egert
Class type: Lecture
Amount: 2 hours per week
Semester: Winter
Core-Program
ECTS: 2 Content:
The goal of this lecture is to understand basic biomedical concepts, processes and structures that influence the function of technical compontents in biomedial applications. The lecture conveys background of different biological processes and structures with the goal to illustrate the context for the measurement of signals and applications of microsystems technology components in biology and medicine. Emphasis will be put on the processes that - generate and influence the microsystems technological measurable physiological signals like clinically relevant key molecules, electrical signals in muscles and the nervous system
- influence the usability of MST-Components (sensors and implants), e.g. due to corrosion, encapsulation, changes in measuring circumstances etc.
- are relevant for typical application fields, as implantable sensors, prostheses, neurotechnology etc.
During the course of the lecture a relatively broad scope is conveyed with a certain focus on electrical biological signals. Due to this the depth the topics are covered is relatively small. Topics: - Basic concepts of biological tissue an functions
- Cell construction, cell growth processes and metabolism, cellular specializations
- Basics in genetics
- Functional systems of the human body
- Biophysics of electrical potentials
- Sensory Systems
- Biological foundation of learning and memory
- Energy metabolism and excretion
- Respiration
- Blood and circulation
« back to top Measurement and Analysis of Neuronal Activity - A Technical Introduction Lecturers: Boucsein, Egert, Nawrot
Class type: Practical Course
Amount: 2 weeks full time
Semester: Winter
Core-Program
ECTS: 4 Content:
The course is intended to give a thorough introduction to the use of typical, electronic laboratory equipment and analysis techniques in neurobiological research, typical problems encountered and their solutions. These include oscilloscope, amplifier, computer, high pass-, low pass- and band pass filters, analog to digital, and digital to analog converters, storage and handling of digitized data, problems related to digital data aquisition (sampling rate and information loss), data analysis on the computer (what is possible, what are the limits). Although no 'real' biological experiments will be performed, the data analyzed are representative for those. Topics: - Basic measurement techniques
- analog filters
- amplifiers
- Real world setups
- data analysis in matlab
- Read & display extracellular data / trial cutting / spike detection
- dot displays, PSTH, rate analysis
- PSP analysis
- local field potentials
« back to top Neurophysiology in Brain Slices Lecturers: Boucsein, Egert, Staiger
Class type: Practical Course
Amount: 2.5 weeks full time
Semester: Summer
Core-Program
ECTS: 6 Content:
On the basis of selected experiments, the course provides a practical introduction (2.5 weeks full time in the semester break) to modern electrophysiological techniques (intra-cellular recording, whole cell patch-clamping, extra-cellular recording with multi-electrode-arrays), immunostaining of brain slices, synaptic plasticity and advanced data analysis methods. Experiments are performed in in vitro brain slice preparations of the rat brain: neo-cortex, hippocampus. Topics: - Neuroanatomy of the hippocampus and neocortex
- Synaptic plasticity
- extracellular recordings
- Single-cell properties
- patch-clamp recordings
« back to top Non-invasive measurements and analysis in neurobiology Lecturers: Ball, Mehring
Class type: Practical Course
Amount: 2 weeks full time
Semester: Winter
Core-Program
ECTS: 4 Content:
text. Topics: « back to top Scientific Programming - An Introduction to Python / Scientific Python Lecturers: Helias
Class type: Practical Course
Amount: 2 weeks full time
Semester: Winter
Core-Program
ECTS: 4 Content:
Python is a modern object oriented programming language with increasing popularity in computational intense sciences like physics and neurobiology. Many current simulators use it as a scripting language to control simulations. Extended by scientific libraries (SciPy/NumPy), Python becomes a versatile and powerful tool for numerical calculations and simulations. Being open source, it provides a valuable and freely accessible resource especially for students.
We teach an example driven rather than a language feature based approach using selected problems from neurobiology and physics. In an introductory session the UNIX/Linux operating system and the typical hard and software environment of scientific programming are introduced.
New concepts are successively introduced as required by the examples. The two weeks block course consists of two distinct units: In the first week, brief lectures followed by practical work on examples will introduce the language concepts and tools. In the second week, every student works on a mini-project applying and deepening the acquired knowledge. The course ends with a colloquium; there will be no written protocols after the course.
The collection of numerical algorithms and functions provided by SciPy will be used from the beginning of the course to provide the students with powerful tools for numerical computing, simulation and data analysis. This allows rapid progression towards interesting applications. Technicalities and low-level programming will be avoided where possible and adequate. The work with the interactive iPython shell allows for learning with direct feedback also employing advanced plotting functions (matplotlib), similar to Matlab.
In addition to basic control structures, functions and variables, also more advanced concepts like object oriented programming and data structures like lists, vectors and dictionaries, needed to manage large projects will be covered. « back to top The Human Brain - Anatomy and Function Teacher: Kirsch, N.N.
Class type: Course
Amount: 50 hours
Semester: Winter
Core-Program
ECTS: 2
Content:
The goal of this course is to educate the students in basic knowledge of human neuroanatomy including the principal functions of the brain areas.
We wish for students participate actively and literally “grasp” the structures in order to experience the learning of neuroscientific contents in a positive and consolidatory manner.
Each brain area (8 brain areas in total) is introduced by one or two students.
The students learn the general construction, set-up and position of this structure, in addition to their primary function. Students shall model this brain structure with plasticine with the substructures coded in different colors. Additionally, students will be given 3D-models of human brains, to study the position of the actual brain structure and its relation to the rest of the brain. In order to learn the function of the respective brain area, the function will be first demonstrated in small experiments (e.g. patella-reflex for the brain structure “spinal cord”).
Additionally, the neuronal circuits which serve this function are embedded in the plasticine model with plastic pearls (soma) and strings (axons and dendrites).
To support self-learning, students will be given black and white line drawings of the brain structure in which they have to identify the substructures and color code them by painting.
Furthermore, the students are able to borrow the 3D brain models to study at home for consolidation.
Afterwards the students will be given the opportunity to study real conserved brains as well as histological brain slices, to transfer their previously acquired “theoretical” knowledge about the respective brain area to real brain and slices of different scaling, thereby completing their concept of the human brain.
Additionally, students will learn aspects of malfunctioning brains: effects of lesions, stroke and some mental diseases.
Literature:
Pinel: Biopsychologie, Spektrum Verlag
Kandel, Schwartz, Jessell: Neurowissenschaften, Spektrum Verlag
Kandel, Schwartz, Jessell: Principles of Neural Science, McGraw Hill
Dudel, Menzel, Schmidt: Neurowissenschaften, Springer Verlag
Kolb, Wilshaw: Neuropsychologie, Spektrum Verlag
Trepel: Neuroanatomie - Struktur und Funktion, Elsevier Verlag
Nieuwenhuys, Voogd, van Huijzen: The Human Central Nervous System: A Synopsis and Atlas, Springer Verlag
Augustine: Human Neuroanatomy, Academic Press
Topics: - Anatomical Divisions
- Spinal cord (Medulla spinalis)
- Medulla oblongata
- Cerebellum
- Midbrain
- Thalamus
- Basal ganglia and limbic system
- Cortex
« back to top Nonlinear Dynamics Teacher: Timmer
Class type: Lecture with Exercises
Amount: 3 + 2 hours per week
Semester: Winter
Advanced-Program
ECTS: 6
Content:
The Nonlinear Dynamics, commonly called "Chaos Theory", tries to understand complex behavior of dynamical systems by nonlinear, dissipative, low-dimensional, deterministic differential equations. In the lecture we will discuss the concepts of the nonlinear dynamics starting from the classical mechanics and the linear stochastic systems. Literature:
H.G. Schuster: Deterministisches Chaos. VCH, 1994
H. Kantz, T. Schreiber: Nonlinear Time Series Analysis. Cambridge University Press, 1997
Topics: - The time before the nonlinear dynamics
- Lorenz' discoveries
- Lyapunov exponents
- Fractale attractors
- Ways into chaos
- Unstable periodic orbits
- Chaoscontrol
- Synchronization
- Takens' Theorem
- Nonlinear Dynamics in practice
« back to top Time Series Analysis I + II Teacher: Timmer
Class type: Lecture with Exercises
Amount: 3 + 2 hours per week
Semester: Winter
Advanced-Program
ECTS: 6 (I) and 4 (II)
Content:
Complex dynamical systems usually are not suited for first-principle modelling. In many cases the measurement of the dynamics, so called "time series" are the only source of information. In the lecture methods will be presented with which the inverse problem, learning something about the system by measuring the time series, can be approached. Literature:
J.D. Hamilton: Time Series Analysis. Princeton University Press, 1994
P.J. Brockwell, R.A. Davis: Time Series: Theory and Methods. Springer, 1998
J. Honerkamp: Stochastic Dynamical Systems. VCH, 1994
Topics: - Basic terms
- Hypotheses tests
- Deterministic systems
- Stochastic systems
- Specta analysis
- Cross-Spectra analysis
- Parameter estimation in dynamic systems
- EM Algorithm
- Hidden Markov Model
- Multiple shooting Algorithm
- Point Proceses
- Methods of the nonlinear dynamics
- Processes in the financial mathematics
- Wavelets
- Neuronal Networks
- ...
« back to top Numerical Methods Teacher: Timmer
Class type: Lecture with Exercises
Amount: 3 + 2 hours per week
Semester: Summer
Advanced-Program
ECTS: 6
Content:
Numerical methods, meanwhile, play an important role in many areas of modern physics. Consequently, "computational physics" is regarded as the new, third area in physics after experimental and theoretical physics. The lecture focusses on numerical methods that are relevant for the analysis, modelling and simulation of complex systems. Literature:
W.H. Press and B.P. Flannery and S.A. Saul and W.T. Vetterling: Numerical Recipes. Cambridge University Press Topics: - Some statistics
- Solution of linear equation systems
- Optimization
- Nonlinear Modelling
- Kernel estimator
- Differential equations-Integrators
- Fourier-Analysis
- Markov Chain Monte Carlo Procedure
« back to top Machine Learning Teacher: Rietmiller
Class type: Lecture with Tutorials
Amount: 4 + 1 hours per week
Semester: Summer
Advanced-Program
ECTS: 6
Content:
This lecture gives an introduction to the field of machine learning. It focuses on basic machine learning concepts, methods, and algorithms. Among other topics the lecture covers version spaces, decision tree learning, inductive logic programming, artificial neural networks, instance-based learning and case-based reasoning. « back to top Bioinformatics Teacher: Backofen
Class type: Lecture with Exercises
Amount: 2 + 2 hours per week
Semester: Winter
Advanced-Program
ECTS: 4
Content:
The course shall give an overview of bioinformatics topics and understanding of fundamental algorithms. The special focus of the course is on sequence analysis.
The topics of the course include sequence alignment, multiple alignment, homology search, molecular phylogeny, comparative genomics, gene and genome annotation, RNA analysis, protein structure, hidden markov models, machine learning. « back to top Informatics Theory Teacher: Wei, Lausen
Class type: Lecture with Exercises
Amount: 4 hours per week
Semester: Summer
Advanced-Program
ECTS: 6
Content:
The goals of the course are: 1. get an in-depth knowledge on the data structures and graph algorithms, as well as database systems, 2. Improve the problem solving ability by doing the exercises, 3) Practicing skills in conducting research work: Reading papers efficiently, writing reviews and surveys.
Algorithm design and analysis, Complexity, Introduction to database algorithms. Topics: - Tree
- balanced tree
- Hashing, Dynamic tables, Randomization
- Text search
- Relational algebra, relational calculus
- Relational DB design theory
- Transaction theory
- ...
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