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Berlin Additional Topics Block course

Ethics and Neuroscience

Philosophical theories of ethics, mental privacy, ethical aspects of animal experiments, ethical aspects of clinical neuroscience and patient research, good scientific practice, data protection and computer security, neurolaw, ethics committees.

Lecture, Group Work, block

MSc, PhD

none

BCCN Berlin, Berlin School of Mind and Brain, Berlin

Winter, semester break, 1 week / 2-3 ECTS

International Winter School

Visit the course website
Berlin Additional Topics Block course

Mathematics Prep-Course

This course is intended as a refreshment of mathematical tools of analysis, linear algebra and statistics which will be necessary for the CNS students in the first year. Students will acquire broadmathematical knowledge of functions in one resp. several real variables, in linear algebra, in differential equations, in probability theory and statistics, as needed for Computational Neuroscience. Basic mathematical skills for the analysis and approximation of functions, solutions of differential equations and signals, for solving linear systems and systems of ordinary differential equations will be refreshed.
Participants will learn to apply mathematical foundations to the modeling and analysis of neural data and to use basic mathematical techniques to problems in Computational Neuroscience with guided assistance.

Lecture, block

MSc

Maths: 3 semesters
Programming: none
Neuroscience: none

BCCN Berlin

Schwalger
Winter, semester break, 2 weeks / 4 ECTS

Two-week course before new MSc students start their first term (Sep-Oct)

Visit the course website
Berlin Additional Topics Block course

Neurobiology Prep-Course

This course is intended as bridge for students without physiology training, enrolling in Computational Neuroscience. The aim is to provide the basics in neurophysiology.

Lecture, block

MSc

Maths: none
Programming: none
Neuroscience: none

BCCN Berlin

Larkum
Winter, semester break, 9 days / 2 ECTS

One-week course before new MSc students start their first term (Sep-Oct)

Visit the course website
Berlin Data Analysis Running lecture

Machine Intelligence

Course description:
Participants learn basic concepts, their theoretical foundation, and the most common algorithms used in machine learning and artificial intelligence. After completing the module, participants should understand strengths and limitations of the different paradigms, should be able to correctly and successfully apply methods and algorithms to real world problems, should be aware of performance criteria, and should be able to critically evaluate results obtained with those methods. Participants should also be able to modify algorithms to new tasks at hand as well as to develop new algorithms according to the paradigms presented in this course.


Contents include:
Artificial neural networks: Connectionist neurons, the multilayer perceptron, radial basis function networks, learning by empirical risk minimization, gradient-based optimization, overfitting and underfitting. Learning theories and support vector machines: statistical learning, learning by structural risk minimization.
Probabilistic methods: Bayesian inference and neural networks, generative models. Projections methods. Principal Component Analysis, Independent Component Analysis and blind source separation. Stochastic optimization. Clustering and embedding.

Lecture Maths Tutorial, weekly

MSc, PhD

Maths: 3 semesters
Programming: basic
Neuroscience: none
Programming language: Python, Matlab, R

Technische Universität Berlin, BCCN Berlin, Berlin

Obermayer
Winter and summer, 2 x 4h per week / 2 x 6 ECTS

Runs for 2 semesters, each semester can be taken separately for 6 ECTS

Visit the course website
Berlin Theoretical Neuroscience Running lecture

Acquisition and Analysis of Neural Data

Students will gain knowledge about the most important methods for experimental acquisition of neural data and the respective analytical methods, they will learn about the different fields of application, the advantages and disadvantages of the different methods and will become familiar with the respective raw data. They will be enabled to choose the most appropriate analysis method and apply them to experimental data.
Contents include:
Acquisition of neural data (1st semester): large scale signals (fMRI, 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.

Analytical / Programming Tutorial Practical Course (Lab)

MSc, PhD
  • Maths: 3 semesters
  • Programming: basic
  • Neuroscience: basic
  • Progr. Lang.: Python

This module is compulsory for students of the Master program Computational Neuroscience, compulsory elective or elective for the specialization Computational Neuroscience, Artificial Intelligence, and Signal Processing (generally for advanced Diploma students or master students).

BCCN Berlin

Haynes et al.
Winter and summer, weekly/ 2 x 4 h per week / 5 - 12 ECTS

Runs for 2 semesters, possible combination would be the winter term course with focus on data acquisition, 5 ECTS without the project

Visit the course website
Berlin Theoretical Neuroscience Running lecture

Models of Higher Brain Functions

Participants should learn the basic concepts and most important topics in the Cognitive Neurosciences. In addition, they should know the state-of-the-art models in these domains and their theoretical foundations. After completing the module, participants should understand strengths and limitations of the different modeling approaches (e.g. bottom-up versus top-down), should be able to understand the rationale behind models and their implementation, and should be aware of performance criteria and critical statistical tests. Participants should also be able to modify models of cognitive processes as well as to apply existing models to novel experimental paradigms, situations or data.
Contents include:
Auditory and visual system, natural image statistics and sensory processing, motor system, psychology and neuroscience of attention, memory systems, executive control, decision making, science of free will and consciousness. Data modeling and essential statistics, psychometric methods, signal detection theory, models of visual processing, models of visual attention, models of executive function. Signal processing, sensory and cognitive modeling using Python.

Lecture, Analytical Tutorial, Programming Tutorial

MSc, PhD
  • Maths: 3 semesters
  • Programming: basic
  • Neuroscience: basic
  • Progr. lang.: Python

BCCN Berlin

Haynes and Sprekeler
2 h weekly in winter- and 6 h weekly in summer semester / 2-12

Starts with seminar “Cognitive Neuroscience” in Winter Semester (2 ECTS), the rest runs during the summer term, multiple combinations possible

This year: No course in summer semester 2019, summer course will be  postponed to winter semester 2019/20!

Visit the course website
Berlin Theoretical Neuroscience Running lecture

Models of Neural Systems

Participants should learn basic concepts, their theoretical foundation, and the most common models used in computational neuroscience. The module also provides the relevant basic neurobiological knowledge and the relevant theoretical approaches as well as the findings resulting form these approaches so far. After completing the module, participants should understand strengths and limitations of the different models. Participating students will learn to appropriately choose the theoretical methods for modeling neural systems. They will learn how to apply these methods while taking into account the neurobiological findings, and they should be able to critically evaluate results obtained. Participants should also be able to adapt models to new problems as well as to develop new models of neural systems.
Contents include:
Hodgkin-Huxley model, Channel models, Synapse models, Single-compartment neuron models, Models of dendrites and axons, Models of synaptic plasticity and learning, Network models, Phase-space analysis of neuron and network models (linear stability analysis, phase portraits, bifurcation theory).

Theoretical Lecture, Experimental Lecture, Analytical Tutorial, Computer Tutorial

MSC, PhD
  • Maths: 3 semesters
  • Programming: basic
  • Neuroscience: basic
  • Progr lang.: Python

BCCN Berlin

Lindner
Winter term /weekly/8 h per week / 12

Combinations of a subset of the courses for fewer ECTS possible

Visit the course website
Bochum Additional Topics Lab Rotation

Modeling Episodic Memory and Spatial Navigation

The computational neuroscience group at the Ruhr University Bochum studies the cognitive and neural mechanisms underlying episodic memory and spatial navigation. The group employs a wide range of computational methods ranging from (spiking) neural networks, abstract cognitive models to machine learning algorithms such as unsupervised and reinforcement learning.

MSc

Python

Institut für Neuroinformatik, Ruhr-Universität Bochum

Cheng et al.
Winter or summer, individually planned / no ECTS
Visit the course website
Bremen Additional Topics Running lecture

Programming

Learn to write your own computer programs to analyse data and simulate neuronal systems. In the first half of the lectures and practical exercises, you will achieve the basic skills to write computer programs which perform simple calculations, and we will advise you how to break down a more complex problem into simple tasks a computer can perform. In the second half of the course, you will apply your acquired skills to analyse neural signals (mean and variance, estimation of firing rates, reverse correlation, ROC analysis, etc.), and simulate single neurons or synapses (integrate-and-fire neuron, Hodgkin-Huxley neuron).

Lecture, Programming Tutorial

Master, (Bachelor)

Maths: none
Programming: none
Neuroscience: none
Programming language:  Matlab or Python

University of Bremen

Erhard et al.
Lectures and exercises 2 semester hours per week: Block course from Nov. 4 to Nov. 22, 2019, Thuesday and Thursday, 14:00-16:00 and Dec. 2 to Dec. 20, Wednesday and Thursday, 14:00-16:00, 2019, / 3 ECTS
Visit the course website
Bremen Theoretical Neuroscience Running lecture

Theoretical Neurosciences: Computational Neuroscience I

Introduction to fundamental concepts in Computational Neuroscience. In the first term, we will study basic encoding and decoding schemes, analysis of neural signals, and the dynamics of single neurons. In the second term, we will focus on synapses and neural networks, and study emergent phenomena such as computation and classification, learning and memory, pattern formation, and synchronization.

Lecture, Math exercises,
weekly (alternating lecture/
exercises)

Master, (Bachelor)

Maths: 1 semester
Programming: none
Neuroscience: none

University of Bremen

Ernst et al.
Winter, starting on 5th of November 2019 / 2 ECTS

Lecture and exercises 2 semester hours per week: Block course from Dec. 2, 2019 to Jan. 24, 2020 Tuesday and Friday, 14:00-16:00
Lectures will be held in Cognium, Room 1030

Visit the course website
Cologne Data Analysis Block course

Advanced Course on Neural Data Analysis

This advanced course aims at providing deeper insights in state-of-the-art questions in neuroscience, analysis approaches and how to formalize questions to neuronal data so they can be answered quantitatively. The course addresse ecellent master studetns and PhD students interested in data analytics and in getting hands-on experience in the analysis of electrophysiological data (multiple-parallel spike trains and local field potentials). In the first week of the course, international experts will give lectures on statistical data analysis and data mining methods with accompanied exercises. In the second week the participants will pursue their own data analysis projects on a common data set.

Lectures, Tutorial, Exercises,
block

MSc, PhD

Good programming skills, background in mathematics (algebra) and statistics
Programming language: Python

Jülich Research Center, University of Cologne,  LMU Munich/German Neuroinformatics Node/
Jülich/Cologne (Haus Overbach)

Organizers: Gruen, Nawrot, Wachtler et al.
Spring, block, 2.5 weeks, 14.04.-2020-30.04.2020 / 6 ECTS

Deadline for application is November 15, 2019.

Visit the course website
Cologne Data Analysis Block course

Introduction to Scientific Programming in Python with Application to Neural Data Analysis

This module will equip the student with basic skills of scientific programming with PYTHON and provide the student with hands-on experience in the statistical analysis of experimental neurophysioloigcal data sets and the adequate presentation of results. No previous programming skills are required.

Tutorials, Exercises, block

MSc

no background required

University of Cologne - Institute of Zoology

Rostami, Nawrot
Feb 10-18, 2020; daily 9:30 am – 17:00 pm / 3 ECTS

Registration (2 places available): vrostami@uni-koeln.de

Deadline: Dec 20, 2019

Visit the course website
Darmstadt Computational Modelling Running seminar

Applied Cognitive Modeling

Advanced introduction to the implementation of cognitive models; levels of description: computational, algorithmic, and implementational models; reading, understanding, and implementing recent publications involving cognitive models, e.g. human information processing, decoding of physiological signals, artificial cognitive systems, machine learning in psychology, motor control.

Seminar and Programming Lab, weekly

Programming languages: e.g. Python, Matlab, BUGS/JAGS/Stan, R

Technische Universität Darmstadt

Rothkopf et al.
Winter, weekly / 6 ECTS
Visit the course website
Frankfurt Additional Topics Running lecture

Systems Engineering Meets Life Sciences I

This multi-semester course focuses on emerging interdisciplinary
perspectives on ‘Systems Science and Engineering for Intelligence’. We focus on natural systems, human and computer vision, bio-inspired vision system designs, and systems theory required for modeling, analysis, simulation and validation of cognitive vision systems. The emphasis is on abstractions, modeling, and rigorous statistical approaches to performance evaluation. Connections are also made between engineering designs and architectural
designs in natural systems. The course draws upon years of systems engineering – involving systems modeling, analysis, and validation of computer vision systems and explores links them to latest viewpoints from Machine Learning, Artificial Intelligence, and Brain Sciences. The core emphasis in the course is that there is a natural correspondence between (Application Contexts, Tasks, Performance Requirements) and (Hardware and Software Programs and their Parameters). Paradigms popular for systems design – model-based systems engineering vs data-driven machine learning
will be contrasted. The course material is largely based on dissertations, publications, and online video material.
The objective of the course is to teach foundations in systems thinking that can be applied to design, analysis, and validation of intelligent systems. Through case-studies in computer vision the students learn systems modeling, simulation and optimization of intelligent systems.

Lecture + exercise, weekly

Goethe University, Frankfurt

Ramesh et al.
Winter, 4h per week / 6 ECTS

The course can be offered as a blended learning course with a distributed team project executed as a block course for one to two weeks at the end of the semester, too.

Visit the course website
Frankfurt Computational Modelling Running lecture

Machine Learning I

Supervised, unsupervised and semi-supervised learning, reinforcement
learning, Bayesian learning, Energy minimization and optimization.

Lecture and Exercise, weekly

MSc, PhD

Basic linear algebra and programming skills
Programming language: flexible

Goethe University, Frankfurt

Kaschube, Ramesh et al.
Winter, 4h per week / 6 ECTS
Visit the course website
Frankfurt Computational Modelling Block course

Models for Neural Circuit Development

We will discuss the principles guiding the formation of sensory maps and receptive fields during circuit development. The students will examine how different mechanisms including: emergence of diverse single neuron properties and activity-dependent synaptic plasticity interact during development to give rise to functional circuits. The students will have the opportunity to analyze data from visual cortex and build their own models of the assembly and tuning of developing neuronal circuits.

Research module, block

MSc or PhD

Programming languages: Matlab, C, Python

Max Planck Institute for Brain Research, Frankfurt

Gjorgjieva et al.
Winter and summer, 4-6 weeks /
Visit the course website
Frankfurt Theoretical Neuroscience Running lecture

Theoretical Neuroscience

This module provides an introduction to modern theoretical neuroscience with an attempt to cover all relevant spatial scales (from molecules to brain areas) as well as temporal scales (sub- millisecond to evolutionary times scales).

Lecture and exercise, weekly

Bachelor, MSc, PhD

Basic analysis, linear algebra and programming skills
Programming language: flexible

Goethe University Frankfurt

Kaschube et al.
Weekly, 4h per week / 6 ECTS
Visit the course website
Giessen Additional Topics Running seminar

Sensation and Perception

This is an introductory-level seminar series on the psychology and biology of visual perception. Topics include illusions, retina, contours, colour, motion, Gestalt psychology, faces and objects, attention and consciousness, computer vision, eye movements, neuropsychology and art. Each week 2-3 students present one paper each for 20 minutes. After each presentation there is a question and answer and discussion section. Each student can also receive a 30-min tutorial with me a few days before their presentation. Participants receiving five or more CP must also complete an essay on the same topic as their presentation.

Seminar, weekly

MSc

Justus-Liebig-Universität, Department of Psychology (FB6), Giessen

Fleming et al.
Winter or Summer, weekly, 2h per week / 3 - 5 ECTS
Giessen Computational Modelling Lab Rotation

Masters Thesis Project on Visual Perception

Masters projects for motivated students seeking a career in research are available in Roland Fleming's lab.  Research in the lab focusses on mid- and high-level vision, including the perception of 3D shape (e.g. shape-from-shading) and the physical properties of objects and materials (e.g. viscosity, stiffness). We make extensive use of photorealistic computer graphics and simulations. The project would combine psychophysics with modeling (e.g. deep learning).

Lab Exchange, Master Thesis Project

MSc

Maths: 1 semester, Programming: fluent, Neuroscience: basic, Programming languages: Matlab and/or Python. Familiarity with Caffe is an advantage.

Justus-Liebig-Universität, Department of Psychology (FB6), Giessen

Fleming
2 Semesters /
Göttingen Data Analysis Lab Rotation

Information Dynamics/Information theoretic analysis of neural data

Students will learn to estimate active information storage, transfer and modifcation using the IDTxl toolbox. datasets will be electrophysiological recordings from non-human primates and MEG data from patients with Autism.

Max Planck Institute for Dynamics and Self Organization - Departement of Nonlinear Dynamics & Network Dynamics Group

Michael Wibral
all year, minimum of 8 weeks /
Visit the course website
Göttingen Theoretical Neuroscience Block seminar

Neural Networks and Information Processing

An introductory meeting will take place on Wednesday, 24th of October at 12:15 at the MPI for Dynamics and Self-Organization, room 0.77
The block seminar will take place in the week 18.-22. February 2019
Programming skills are required. The number of participants is limited to 15.
Inspired by the architecture of the human brain, artificial neural networks have been developed in the past decades to process and solve various types of tasks. To understand processing in such networks, a natural framework is provided by information theory. The seminar will focus on information theory to study the design, dynamics, and function of such networks.
The seminar is organized as a block seminar, in the spirit of summer schools or code jams. The students choose a topic to work on in small groups, preparing the background theory, implementing the main results, and ideally obtaining novel insights based on their own research projects within the block seminar.

Block seminar, 1 week

Max Planck Institute for Dynamics and Selforganization, Göttingen

Priesemann, Levina et al.
18.02.2019 to 22.02.2019, Introductionary meeting on 24.10.2018 /
Göttingen Theoretical Neuroscience Lab Rotation

Nonlinear dynamics

Collective dynamics; subsampling; information theory; synaptic plasticity; principles of local learning.
Short research projects or collaborations comprising any of these topics are defined together with the student.

Programming or analytical skills are required

Max-Planck Institute for Dynamics and Self-Organization - Departement of Nonlinear Dynamics & Network Dynamics Group, Göttingen

Viola Priesemann
Time and duration depend on topic /
Visit the course website
Göttingen Theoretical Neuroscience Running lecture

Theoretical and Computational Neuroscience: Collective dynamics of biological neural Networks II

Introduction to Neurophysics, Modeling and Methods, Nonlinear Dynamics, Statistical Physics, Neurobiology, Neural Networks. This lecture course offers an introduction to advanced modeling strategies for biological neural networks. After a short introduction to the biophysics of single cells and an overview of their basic firing patterns, we explain fundamental properties of networks models of neurons, starting from simple uniform connectivity and progressing to spatially extended and to arbitrarily complex interaction networks. These network models explain and predict key dynamical aspects of neural circuits, including irregular activity of cortical dynamics, feature selectivity, self-organization of neural maps, and the coordination of precisely timed spikes across networks. The summer term course has its focus on neural field models.

Lecture

MSc or BSc (Physics, Mathematics, Applied Informatics)

Institute für Nichtlineare Dynamik, departement of physics, Univesity of Göttingen

Wolf, Priesemann et al.
Summer, weekly, 2h per week / 3 ECTS

Language: English

Visit the course website
Hamburg Additional Topics Running lecture

Basics of Brain Connectivity

Fundamental concepts; Structural connectivity; Functional connectivity; Clinical aspects

Lecture

MSc, PhD

none

University Medical Center Hamburg-Eppendorf (UKE), Hamburg

Hilgetag
flexible /
Visit the course website
Jülich Additional Topics Running lecture

Statistical Physics

Statistical theory of equilibrium states (e.g. collective phenomena, phase transitions, renormalization group theory) and nonequilibrium processes (e.g. kinetic theory, stochastic processes).

Students learn the principles, on which the modeling of complex systems is based, as well as the mathematical methods that are employed in deriving and solving such models.

lecture and exercise, weekly

RWTH Aachen University

Helias
Summer, 4h per week /
Visit the course website
Jülich Data Analysis Running lecture

Statistical mechanics of neural networks

The neural networks of the brain form one of the most complex systems we know. Many qualitative features of the emerging collective phenomena, such as correlated activity, stability, response to inputs, chaotic and regular behavior, can, however, be understood in simple models that are accessible to a treatment in statistical mechanics. This course presents the fundamentals behind contemporary developments in neural network theory that are based on methods from statistical mechanics of classical systems with a large number of interacting degrees of freedom.
The focus on classical systems allows us to introduce the standard language and tools employed in statistical field theory in a simple and didactic form moments, cumulants, generating function[al]s, Wick's theorem, linked cluster theorem, perturbation theory, Feynman diagrams, mean-field approximation, loopwise expansion). We will explain and derive these concepts as far as they are needed in the context of neural networks. This
first part will be familiar to students with some knowledge in (quantum) field theory and statistical physics.

Lecture and Exercise, weekly

RWTH Aachen

Helias
Winter, 3h per week /
Visit the course website
Jülich Theoretical Neuroscience Running lecture

Introduction to Computational Neuroscience

Models of neurons, synapses and networks; concepts of neuronal coding and cortical information processing; plasticity and learning. Data analysis and visualization by self-written programs; Usage of scientific programming languages (Matlab and Python), also for documenting the analyses; hypothesis tests by numerically generated modified data ('surrogate data'); Simulation of neuronal circuits.

Lecture and exercise, weekly

MSc (biology, physics, ect.)

mathematical background; considerable advantage: programm skills
Programming language: Python

RWTH Aachen University, Jülich

Grün et al.
Winter, 3h per week / biology: 6 ECTS; math: 7,5 ECTS; physics: 10 ECTS

This course is taught in combination with "Cortical Structure and Function" taught during the winter semester. This course can also function as Computational Neuroscience (I), if "Cortical Structure and Function" is used as (II). However, I is not a prerequisite for II.

Visit the course website
Leipzig Theoretical Neuroscience Running lecture

Mathematical topics in the neurosciences

In this course, I plan to treat the dynamics of populations of neurons and of synaptic learning.

Lecture, weekly

MSc, PhD

some background in mathematics

Max Planck Institute for Mathematics in the Sciences, Leipzig

Jürgen Jost
Winter, weekly, 2h per week /

Language: English

Starting Nov 01, 2019

Visit the course website
Magdeburg Theoretical Neuroscience Running lecture

Spiking Networks

Special mathematical and statistical tools are required to model and analyze biologically plausible networks of spiking neurons. The course offers a step-­by-­step introduction to stochastic dynamical systems up to and including current mean-­field approaches to recurrent networks of spiking neurons. Stochastic variables, stochastic processes, interval distributions, autocorrelation and power spectrum, Wiener process and white noise, LIF neurons with Poisson input, diffusion limit, Fokker-­Planck equation of VIF neuron, mean-­field theory of recurrently connected populations, application to decision making and confidence. In the practical part, students simulate and analyze recurrent networks of spiking neurons on the basis of Matlab and Neuron.  

Lecture, exercise, tutorial, weekly

MSc, PhD

MSc level algebra and calculus, Matlab programming

Institute for Biology, OvGU Magdeburg

Winter, weekly, 2h per week / 4 ECTS
Visit the course website
Magdeburg Theoretical Neuroscience Running lecture

Theoretical neuroscience I

Based on Chapters 5-­6 and Chapters 1-­4 of Dayan & Abbott. Electrochemical equilibrium and Nernst Equation, equivalent circuits for single-­compartment model, leaky integrate-­and-­fire model, Hodgkin-­Huxley and Connor-­Stevens models of action potential, cable equation and neuron morphology, characterizing neuronal responses with tuning curves and receptive fields, signal-­ detection theory and psychometric function, comparison of neuronal and behavioural responses with neurometric function, population coding, statistically efficient decoding with maximum likelihood and maximum a posteriori likelihood, introduction to Shannon information, application of Shannon information to neural responses.

Lecture, exercise, tutorial, weekly

MSc, PhD

BSc level algebra and calculus, Matlab programming

Institute for Biology, OvGU Magdeburg

Winter, 2h+2h+2h per week / 5 ECTS
Visit the course website
Freiburg Additional Topics Online

MidsummerBrains: Computational neuroscience and field biology

Computational neuroscience is a highly interdisciplinary field ranging from mathematics, physics and engineering to biology, medicine and psychology.

Interdisciplinary collaborations have resulted in many groundbreaking innovations both in the research and application.

The basis for successful collaborations is the ability to communicate across disciplines: What projects are the others working on? Which techniques and methods are they using? How is data collected, used and stored?

In the MidsummerBrains colloquium, experts from the SMARTSTART faculty describe their view on computational neuroscience in theory and application, and share experiences they had with interdisciplinary projects.

This is a recording of the lecture "There and back again: Computational neuroscience and field biology", held by Jan Benda, head of the department of neuoethology at the University of Tübingen (Dep. of Neuroethology).

Jan Benda talked about his point of view on computational neuroscience, the tricky life beyond lab conditions and quite explicitly talking fish. 

Online lecture, colloquium

All students of computational neuroscience

None

SMARTSTART in collaboration with the University of Tübingen

Jan Benda
/
Visit the course website
Freiburg Additional Topics Online

MidsummerBrains: Engineers in Computational Neuroscience

Computational neuroscience is a highly interdisciplinary field ranging from mathematics, physics and engineering to biology, medicine and psychology.

Interdisciplinary collaborations have resulted in many groundbreaking innovations both in the research and application.

The basis for successful collaborations is the ability to communicate across disciplines: What projects are the others working on? Which techniques and methods are they using? How is data collected, used and stored?

In the MidsummerBrains colloquium, experts from the SMARTSTART faculty describe their view on computational neuroscience in theory and application, and share experiences they had with interdisciplinary projects.

This is a recording of the lecture "My View on Computational Neuroscience", held by Stefan Glasauer, head of the department of computational neuroscience at the Brandenburg University of Technology (b-tu.de/en/).

In this lecture, he gives insights on his career from early interests in nature to engineering and how different views can help to solve the bigger problems.

Online lecture, colloquium

All students of computational neuroscience

None

SMARTSTART in collaboration with the Brandenburg University of Technology

Stefan Glasauer
/
Visit the course website
Freiburg Additional Topics Online

MidsummerBrains: Physics and computational neuroscience

Computational neuroscience is a highly interdisciplinary field ranging from mathematics, physics and engineering to biology, medicine and psychology.

Interdisciplinary collaborations have resulted in many groundbreaking innovations both in the research and application.

The basis for successful collaborations is the ability to communicate across disciplines: What projects are the others working on? Which techniques and methods are they using? How is data collected, used and stored?

In the MidsummerBrains colloquium, experts from the SMARTSTART faculty describe their view on computational neuroscience in theory and application, and share experiences they had with interdisciplinary projects.

This is a recording of the lecture "My View on Computational Neuroscience", held by Tatjana Tchumatchenko, head of the theory of neural dynamics group at the MPI for Brain Research in Frankfurt (tchumatchenko.de/).

She talked about her point of view on computational neuroscience as a physicist and the winded path from experiment to insight and publication.

Online lecture, colloquium

All students of computational neuroscience

None

SMARTSTART in collaboration with the MPI for Brain Research Frankfurt

Tatjana Tchumatchenko
/
Visit the course website
Freiburg Additional Topics Online

MidsummerBrains: Quantifying nonlinear representations in the visual systems

Computational neuroscience is a highly interdisciplinary field ranging from mathematics, physics and engineering to biology, medicine and psychology.

Interdisciplinary collaborations have resulted in many groundbreaking innovations both in the research and application.

The basis for successful collaborations is the ability to communicate across disciplines: What projects are the others working on? Which techniques and methods are they using? How is data collected, used and stored?

In the MidsummerBrains colloquium, experts from the SMARTSTART faculty describe their view on computational neuroscience in theory and application, and share experiences they had with interdisciplinary projects.

This is a recording of the lecture "Quantifying nonlinear representations in the visual systems", held by Fabian Sinz, group leader of the neuronal intelligence lab in Tübingen as part of the CyberValley initiative (sinzlab.org/).

He is talking about the difficulties of nonlinear representations and what black boxes mean for machine learning.

Online lecture, colloquium

All students of computational neuroscience

None

SMARTSTART in collaboration with the University of Tübingen

Fabian Sinz
/
Visit the course website
Freiburg Additional Topics Online

MidsummerBrains: The Virtual Brain

Computational neuroscience is a highly interdisciplinary field ranging from mathematics, physics and engineering to biology, medicine and psychology.

Interdisciplinary collaborations have resulted in many groundbreaking innovations both in the research and application.

The basis for successful collaborations is the ability to communicate across disciplines: What projects are the others working on? Which techniques and methods are they using? How is data collected, used and stored?

In the MidsummerBrains colloquium, experts from the SMARTSTART faculty describe their view on computational neuroscience in theory and application, and share experiences they had with interdisciplinary projects.

This is a recording of the lecture "The Virtual Brain - Improving Life through Simulation", held by Petra Ritter from the Charité Berlin on June 24th, 2019.

Online lecture, colloquium

All students of computational neuroscience

None

SMARTSTART in collaboration with Charité Berlin

Petra Ritter
/
Visit the course website
Munich Additional Topics Running seminar

Advanced Seminar in Computational Neuroscience

Special topics in computational neuroscience, incl. talks by guest speakers, review of new publications in the field and progress report on ongoing research projects

Seminar, weekly

MSc, PhD

LMU, Munich

Herz, Leibold et al.
Winter and summer, 2h per week / 2 ECTS

Mo, 10:30 - 12:00

Visit the course website
Munich Additional Topics Online

Communication Acoustics

In this engineering course, we will cover all aspects of communication acoustics, which is the way sounds travels from a source, through a channel and finally to a receiver. We will look at the different system components involved in acoustic communication, including those between humans, between humans and machines, and between machines. This includes:

  • speech acoustics
  • hearing acoustics
  • electroacoustics
  • spatial sound capture and presentation
  • simulation of acoustical environments
  • the human auditory system
  • digital audio processing methods

You will learn from top experts in the field of communication acoustics, who are all affiliated with TU9, the nine leading Universities of Technology in Germany. Together, they have pooled their expertise in order to teach a comprehensive basic understanding and indicate current research trends to you.

Lecture with integrated exercises, weekly

MSc, PhD

TUM, Munich

Seeber et al.
Winter, 2h+2h per week / 6 ECTS

video-course, online

Visit the course website
Munich Additional Topics Running lecture

Neuroprosthetics

The lecture covers the theoretical foundations of neuroprostheses. As the underlying principle of all neuroprostheses is the electrical excitation of neurons, we will cover this topic in depth using cochlea implants as an example. In the practical computer laboratory (2SWS), which complements
the lecture (2SWS), we will program a solver for the cable equation of an active axon and implement a computer model of a cochlea implant.

Lecture with integrated exercises, weekly

MSc, PhD

TUM, Munich

Hemmert et al.
Winter, 4h per week /
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Munich Additional Topics Running lecture

Psychoacoustics and Audiological Applications

Binaural hearing: binaural cues, masking, directional hearing, movement perception, precedence effect, models; Hearing impairment: Kinds of hearing impairment, frequency selectivity and auditory filters, masking and across-frequency processes, loudness and recruitment, temporal and
spectral processing, pitch perception, models of peripheral processing; Speech understanding: cues, models (Articulation Index, Speech Intelligibility Index), binaural speech understanding, effect of noise and reverberation on speech understanding; Auditory scene analysis; Music perception: Harmony,
consonance, dissonance; Hearing aids: function and algorithms; Cochlear implants: function, algorithms, temporal and spectral resolution, speech understanding.

Lecture with integrated exercises, weekly

MSc, PhD

TUM, Munich

Seeber et al.
Winter, 2h+2h per week / 6 ECTS

Lecture held in English

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Munich Additional Topics Running seminar

Spatial and Temporal Cognition

The hippocampus and surrounding medial temporal lobe (MTL) contain cell types that represent place, time and are important for memory. Both brain regions are conserved across species implying general function. For years this function has been suggested in pattern separation, pattern completion, and attractor dynamics that would be necessary for memory. This winter term we first examine recent evidence for pattern completion, pattern separation and attractor dynamics based on the anatomy and circuitry of the hippocampus. Later we will broaden the view and look at papers on how hippocampus and MTL are related to time and space.

Seminar, weekly

MSc, PhD

LMU, Munich

Flanagin, Thurley
Wednesdays 10-12 c.t. / 3 ECTS
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Munich Data Analysis Running lecture

Fundamentals of Computer Science for Neuroengineering

Introduction to computer science, computer programming, and data
processing. Differences between programming procedural vs object
oriented; discussing efficiency of code execution vs. simplicity of
programming; introduction to programming in C, python and matlab (or
similar respective environments).

Lecture, weekly

MSc, PhD

TUM, Munich

Macke et al.
Winter, 4h per week / 5 ECTS
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Munich Data Analysis Running lecture

Fundamentals of Mathematics for Neuroengineering

For further information please contact the lecturer Jakob Macke

Lecture, weekly

MSc, PhD

TUM, Munich

Macke et al.
Winter, 4h per week / 5 ECTS
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Munich Data Analysis Block seminar

Practical Course Methods in Functional Imaging

The goal of this practical course is to give students the tools, knowledge and hands-onexperience needed to plan, conduct and analyse a task-based fMRI or PET experiment. In the first week of the course, there will be theoretical lectures on data acquisition and analysis as well as guided tutorials on how to analyse fMRI and structural MRI data with SPM12 and Melodic in FSL. The tutorials are self-paced. In the second week, you will be asked to analyse a data set on your own and write a short report to be handed in at the end of the course.

Practical course, block

MSc, PhD

LMU, Munich

Flanagin et al.
20.01.-31.01.2020 / 3 ECTS

Registration per email: neuroimaging@med.uni-muenchen.de

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Munich Data Analysis Block seminar

Practical Short Course Methods for Computational Neuroscience

For further information please contact the lecturer Christian Leibold.

Practical course, block

MSc, PhD

LMU, Munich

Leibold et al.
13.01.-17.01.2020 /
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Munich Theoretical Neuroscience Running lecture

Computational Neuroscience: A Lecture Series from Models to Applications

Interdisciplinary lecture series taught by neuroscience experts from TUM and LMU that provides an introduction to computational neuroscience.
General overview: Anatomical and physiological basis of neuroscience
Modeling: Neural dynamics and coding
Towards integration in the nervous system Engineering for Neuroscience and Neuroprothetics

Lecture, weekly

MSC, PhD

LMU, TUM, Munich

Herz, Luksch, Seeber, Thurley et al.
Winter 2018/19 + Summer 2019 / 3 ECTS
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Munich Theoretical Neuroscience Running lecture

Fundamentals in Neuroscience

The lecture gives an introduction to the following topics: Neurons & glia, passive membrane properties, ion channels, cation potentials, synaptic transmission, transmitter systems, cellular networks, motor systems, learning and memory, sensory systems, orientation, echolocation.

Lecture and exercise, weekly

MSc, PhD

LMU, Munich

Busse, Grothe et al.
Winter, weekly, 2h + 2h per week / 5 ECTS
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Munich Theoretical Neuroscience Running lecture

The Neural Code

For more information please contact the lecturers Leibold and Wachtler

Lecture and exercise, weekly

MSc, PhD

LMU, Munich

Leibold, Wachtler et al.
Winter, weekly, 2h + 2h per week / 3 ECTS
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Munich Theoretical Neuroscience Running lecture

Theoretical Biophysics and Cellular Physiology

This course covers the mathematical foundations of cellular physiology, ranging from the ionic basis of the membrane potential to electrochemical signaling to the propagation of action potentials in axons and dendrites of neurons based on the Hodgkin-Huxley model of the squid giant axon.
Students learn the basics of dynamical systems theory and computational neuroscience.

Lecture and exercise, weekly

MSc, PhD

LMU, Munich

Leibold, Borst et al.
Winter, weekly, 2h + 2h per week /
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Oldenburg Data Analysis Block course

Computational Neuroscience - Introduction

This intense block course provides some theoretical background,
extensive hands-on programming exercises in Matlab and interpretation of the obtained modeling results. The course is structured in 6 weeks:
Weeks 1&2: Spike train analysis, statistical models. Weeks 3&4:
Biophysical models of single neurons. Weeks 5&6 Small network models

block course, including lecture, seminar, hands-on programming
exercises

MSc, PhD

programming experience (preferably Matlab)

University of Oldenburg, MSc Neuroscience, Oldenburg

Kretzberg, Hildebrandt, Greschner, Ashida
Winter, 6 weeks full-day, Dec-Jan / 12 ECTS
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Oldenburg Data Analysis Block course

Computational Neuroscience - Statistical Learning

This intense block course provides some theoretical background, extensive hands-on programming exercises in Matlab and interpretation of the obtained modeling results. Multi-channel neurophyisological data analysis: Data pre-processing, data analysis toolboxes, theory of multi-dimensional statistical methods, matlab implementation, visualization

block course, including lecture, seminar, hands-on programming exercises

MSc, PhD

Matlab programming experience,
Programming language: Matlab

University of Oldenburg, MSc Neuroscience

Kretzberg et al.
Summer, full-day block, 1.4.-26.4.2019 / 6 ECTS

Course is held in English

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Oldenburg Data Analysis Lab Rotation

Individual Project in Auditory Neuroscience group

Students perform an individual research project (lab rotation) in the Auditory Neuroscience Group. Topics and methods depend on the individual interests and backgrounds of the students. Vertebrate auditory system: Animal behavior, electrophysiology, anatomy, or data analysis project.

individual student research project, lab rotation

MSc, PhD

Matlab

University of Oldenburg, MSc Neuroscience

Hildebrandt
Winter and summer, 6-8 weeks, flexible timing / 15 ECTS

Course is held in English

Oldenburg Data Analysis Lab Rotation

Individual Project in Computational Neuroscience group

Students perform an individual research project (lab rotation) in the Computational Neuroscience Group. Topics and methods depend on the individual interests and backgrounds of the students. Invertebrate (leech) mechanosensory system: Electrophysiology, data analysis or modeling project. (Intracellular recordings and simulation techniques can be learned during the project.)  Vertebrate auditory system: Modeling project.

individual student research project, lab rotation

MSc, PhD

Matlab

University of Oldenburg, MSc Neuroscience

Kretzberg
Winter and summer, 6-8 weeks, flexible timing / 15 ECTS

Course is held in English

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Oldenburg Data Analysis Block course

Introduction to Biological Data Analysis in Matlab

Introduction to Matlab programming for students with little or no programming experience. In hands-on exercises, students first practice to use basic programming concepts (scripts and functions, data flow and control flow, loops, structures and cell arrays…) and then apply their knowledge to statistical analysis of biological (including neuroscientific) data. After the block course, students work independently on a programming task used for evaluation.

block course, including lecture, seminar, hands-on programming  exercises

BSc, MSc, PhD

Programming language used: Matlab

University of Oldenburg, 'Professionalisierungsbereich'
Oldenburg

Kretzberg
Summer, full-day block, 3 weeks in August / September / 6 ECTS

Course is held in German

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Oldenburg Data Analysis Block course

Invertebrate Neuroscience

This background module in neurophysiology consists of three weeks of seminar and hands-on lab exercises on intracellular recordings from leech neurons, as well as computer simulations to study the basis of membrane potential and action potential generation. The seminar covers the following topics:
• Invertebrate neuronal systems in comparison to vertebrate systems
• Ion channels, membrane potential and action potential generation
• Introduction to electrophysiological methods
• Introduction to data analysis methods In the practical exercises, portfolio assignments will be performed on:
• Qualitative electrophysiological classification of different cell types in the leech nervous system
• Quantitative analysis (stimulus - response relationship) of at least one cell type
• Action potential generation: Comparison of simulation and experiment

block course, including lecture, seminar, hands-on wet-lab and simulation exercises

MSc, PhD

Matlab would be helpful

University of Oldenburg, MSc Neuroscience

Kretzberg
Summer, full-day block, 27.5.-14.6.2019 / 6 ECTS

Course is held in English

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Oldenburg Data Analysis Block course

Neuroscientific Data Analysis in Matlab

Introduction to Matlab, Readable Code: good practice, Data Import, basic statistics, frequency analysis, Continuous data (EEG/LFP), Behavioral  data, Flow control, Spike data, Logical Indexing, complex data types, Variables and their scope, Advanced plotting & Matrix operations, Images, Object-oriented programming, Individual projects, Version control, Wrap up, Principles of data analysis.

lecture and exercises

MSc, PhD

University of Oldenburg, MSc Neuroscience, Oldenburg

Jannis Hildebrandt
Winter, 7 weeks part-time, Oct - Nov / 6 ECTS

Course is held in English

Osnabrück Additional Topics Running seminar

Action & Cognition I

In this lecture, and its follow-up Action & Cognition II in the summer term, we discuss the physiological substrate of cognitive processes with an emphasis on their relation to behavior. On your journey through the brain we will meet object recognition, attention, decision processes, movement planning and consciousness. A bias will be on physiological mechanisms, but due attention to clinical aspects, theoretical analysis and information theoretic measures will be given.

Seminar, weekly

Master

Statistics, basic mathematics and programming skills

Institute of Cognitive Science,
Osnabrück

König et al.
Winter, weekly, 2 h/week + Tutorials / 4 ECTS
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Osnabrück Additional Topics Running seminar

Action & Cognition II

In this lecture, it is the follow-up of Action & Cognition I in the winter term, we discuss the physiological substrate of cognitive processes with an emphasis on their relation to behavior. On your journey through the brain we will meet object recognition, attention, decision processes, movement planning and consciousness. A bias will be on physiological mechanisms, but due attention to clinical aspects, theoretical analysis and information theoretic measures will be given.

Seminar, weekly

Master

Statistics, basic mathematics and programming skills

Institute of Cognitive Science,
Osnabrück

König et al.
Summer, weekly, 2 h/week + Tutorials / 4 ECTS

Action & Cognition I recommended

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Osnabrück Additional Topics Running seminar

Cognitive Human-Computer Interaction

The course focuses on the cognitive basis relevant for the design of user  interfaces, the development of user interfaces, and usability aspecs of user interfaces.

Seminar, weekly

Master

Statistics, basic mathematics and programming skills
Programming languages: Python, R

Institute of Cognitive Science, Osnabrück

Kühnberger et al.
Winter, weekly, 2h per week / 4 ECTS
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Osnabrück Additional Topics Running seminar

Colloquium of the Institute of Cognitive Science

Various topics

Colloquium, weekly

Bachelor, Master

Statistics, basic mathematics and programming skills
Programming languages: MatLab, Python

Institute of Cognitive Science/
Osnabrück

Jäkel, König et al.
weekly, 2h per week / 2 ECTS
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Osnabrück Additional Topics Block course

Colloquium of the PhD Programme

Various topics

Colloquium, weekly

PhD

Statistics, basic mathematics and programming skills

Institute of Cognitive Science, Osnabrück

Pipa et al.
weekly, 2h per week / 4 ECTS
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Osnabrück Computational Modelling Running seminar

Bayesian Statistics

Bayesian statistics and modeling, JAGS

Seminar, weekly

Master, PhD

Statistics, basic mathematics and programming skills
Programming languages: R, JAGS

Institute of Cognitive Science, Osnabrück

Pipa et al.
Summer, weekly, 2h per week / 4 ECTS
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Osnabrück Data Analysis Running seminar

Colloquium Computer Vision

The course starts with the basics of image processing and proceeds to computer vision, A focus is on object recognition.

Colloquium, weekly

Bachelor, Master

Statistics, basic mathematics and programming skills
Programming languages: MatLab, Python

Institute of Cognitive Science/
Osnabrück

Heidemann et al.
weekly, 2h per week / 8 + 4 ECTS
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Osnabrück Data Analysis Running lecture

Machine Learning I

The course gives an introduction to unsupervised and supervised techniques of machine learning and data mining: Decision trees, clustering, dimension reduction, classification and artificial neural networks.

Lecture + Practice

Bachelor, Master

Statistics, basic mathematics and programming skills
Programming languages: MatLab, Python

Institute of Cognitive Science,
Osnabrück

Heidemann et al.
weekly, 2h+2h+2h per week / 8 + 4 ECTS
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Osnabrück Data Analysis Running seminar

Machine Learning II

Important frameworks for advanced methods in Machine Learning are discussed in this course. This includes e.g. SVMs, probabilisitic methods, and reinforcement learning.

Seminar, weekly

Master, PhD

Statistics, basic mathematics and programming skills
Programming languages: MatLab, Python, R

Institute of Cognitive Science, Osnabrück

Kühnberger et al.
weekly, 2h+2h+2h per week / 4 ECTS
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Osnabrück Theoretical Neuroscience Running lecture

Neuro Dynamics (alternating with Neuro-informatics)

Modelling spiking neurons and network, principles of complex systems

Lecture + Seminar,
weekly

Master, PhD

Statistics, basic mathematics and programming skills
Programming languages: MatLab, Python

Institute of Cognitive Science,
Osnabrück

Pipa et al.
weekly, 2h+2h per week / 8+4 ECTS

alternating with  Neuro-informatics

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Osnabrück Theoretical Neuroscience Running lecture

Neuro-Informatics

In this lecture, we will discuss cutting edge approaches from the field of Neuroinformatics. The aim of the lecture is to get the students familiar with the concept of modelling and abstracting data, and the up to date knowledge about computational processes in the brain. After a short introduction that covers probability theory, and linear models for regression and classification, we will start a journey through the fields of graphical models and liquid computing. In the last part of the lecture we will conclude with an outlook to self-organization with the purpose to optimize information processing in complex systems like the brain. To link the knowledge acquired in this course with scientific question every second lecture a 30 min 3W session is offered. The three big W are: why should I learn this / what for can I use it / how can it be important my bachelor and master thesis . The lecture will be supplemented by a block seminar on decoding neuronal activity at the beginning of the semester break.

Lecture + Seminar,
weekly

Master

Statistics, basic mathematics and programming skills
Programming languages: Python, R

Institute of Cognitive Science, Osnabrück

Pipa et al.
weekly, 2h+2h per week / 12+4 ECTS

alternating with Neuro Dynamics

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Osnabrück Theoretical Neuroscience Running lecture

Probabilistic Modeling of Perception and Cognition

Probability theory, judgment and decision making, choice models, signal detection theory

Lecture and Tutorial

Master

Statistics, basic mathematics and programming skills
Programming language: Python, R

Institute of Cognitive Science,
Osnabrück

Jäkel et al.
weekly, 2h+2h per week / 8 ECTS
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Tübingen Additional Topics Running lecture

Neural Experimental Techniques

This course will provide a detailed overview of the experimental methods currently used in the Neurosciences to record (as well as modulate) neuronal activity – from the local activity with single-synapse resolution to population activity at the level of brain areas.

Lecture, weekly

Master, PhD

Math: none
Programming: none
Biology: basic

Graduate Training Center of Neuroscience, Tübingen

Euler, Zeck
Winter, 2h per week / 3 ECTS
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Tübingen Data Analysis Running lecture

Machine Learning I

This course will provide an introduction to important topics and algorithms in machine learning. A particular focus of this course will be on algorithms that have a clear statistical (and often Bayesian) interpretation.

Lecture, weekly

Master, PhD

Math: 3 semesters
Programming: fluent
Biology: none
Programming language: Matlab

Graduate Training Center of Neuroscience, Tübingen

Dijkstra
Winter, 2h per week / 4 ECTS
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Tübingen Data Analysis Running lecture

Machine Learning II

In this course, students will learn about important topics and techniques in machine learning, with a particular focus on probabilistic models. The course will cover supervised learning (linear regression algorithms, linear discriminants, logistic regression, nonlinear classification algorithms) and unsupervised learning (principal component analysis including several generalizations, k-means, mixture of Gaussians, Expectation-Maximization)

Lecture and exercises, weekly

Bachelor, Master

basic knowledge of linear algebra and probability theory,
basic familiarity with matlab

Graduate Training Center of Neuroscience, Tübingen

Dijkstra
Summer, 3h per week / 5 ECTS
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Tübingen Theoretical Neuroscience Running lecture

Models of neural systems

The lecture introduces models of neurons of different complexity from the detailed Hodgkin-Huxley models for action potential generation via
integrate-and-fire models to simple firing rate models. Based on these
specific examples basic concepts of differential equations, linear system theory, dynamical systems theory and stochastic systems are introduced. These tools are essential for modelling neural systems and other complex systems like, for example, signaling cascades and population dynamics.
Central to the module are the exercises that match the topics from the
lecture and repeat the necessary math basics.

Lecture + math tuorial with exercises, weekly

Master, Bachelor

Math 1 semester, programming none, neuroscience basic

Institute for Neurobiology, University of Tübingen

Benda
Winter, weekly, 4h per week / 6 ECTS

The lecture and exercises are tailored to biologists

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Tübingen Theoretical Neuroscience Running lecture

Neural Dynamics

This course treats the basic biophysics of the signal generation and
transmission in neurons and discusses how the underlying physical and physiological phenomena can be approximated by mathematical models. Typically, such models can be characterized as nonlinear dynamical systems.

Lecture, weekly

Master, PhD

Math: 3 semesters
Programming: fluent
Biology: basic
Programming language: Matlab

Graduate Training Center of Neuroscience, Tübingen

Giese
Winter, weekly, 2h per week / 6 ECTS
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