Search result: Catalogue data in Spring Semester 2021

Neural Systems and Computation Master Information
Core Courses
Compulsory Core Courses
NumberTitleTypeECTSHoursLecturers
227-1031-00LJournal Club (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: INI702

Mind the enrolment deadlines at UZH:
https://www.uzh.ch/cmsssl/en/studies/application/deadlines.html
O2 credits1SG. Indiveri
AbstractThe Neuroinformatics Journal club is a weekly meeting during which students present current research papers.
The presentation last from 30 to 60 Minutes and is followed by a general discussion.
ObjectiveThe Neuroinformatics Journal club aims to train students to present cutting-edge research clealry and efficiently. It leads students to learn about current topics in neurosciences and neuroinformatics, to search the relevant literature and to critically and scholarly appraise published papers. The students learn to present complex concepts and answer critical questions.
ContentRelevant current papers in neurosciences and neuroinformatics are covered.
227-1043-00LNeuroinformatics - Colloquia (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: INI701

https://www.uzh.ch/cmsssl/en/studies/application/deadlines.html
W0 credits1KS.‑C. Liu, R. Hahnloser, V. Mante
AbstractThe colloquium in Neuroinformatics is a series of lectures given by invited experts. The lecture topics reflect the current themes in neurobiology and neuromorphic engineering that are relevant for our Institute.
ObjectiveThe goal of these talks is to provide insight into recent research results. The talks are not meant for the general public, but really aimed at specialists in the field.
ContentThe topics depend heavily on the invited speakers, and thus change from week to week. All topics concern neural computation and their implementation in biological or artificial systems.
Elective Core Courses
Systems Neurosciences
NumberTitleTypeECTSHoursLecturers
227-0395-00LNeural SystemsW6 credits2V + 1U + 1AR. Hahnloser, M. F. Yanik, B. Grewe
AbstractThis course introduces principles of information processing in neural systems. It covers basic neuroscience for engineering students, experiment techniques used in animal research and methods for inferring neural mechanisms. Students learn about neural information processing and basic principles of natural intelligence and their impact on artificially intelligent systems.
ObjectiveThis course introduces
- Basic neurophysiology and mathematical descriptions of neurons
- Methods for dissecting animal behavior
- Neural recordings in intact nervous systems and information decoding principles
- Methods for manipulating the state and activity in selective neuron types
- Neuromodulatory systems and their computational roles
- Reward circuits and reinforcement learning
- Imaging methods for reconstructing the synaptic networks among neurons
- Birdsong and language
- Neurobiological principles for machine learning.
ContentFrom active membranes to propagation of action potentials. From synaptic physiology to synaptic learning rules. From receptive fields to neural population decoding. From fluorescence imaging to connectomics. Methods for reading and manipulation neural ensembles. From classical conditioning to reinforcement learning. From the visual system to deep convolutional networks. Brain architectures for learning and memory. From birdsong to computational linguistics.
Prerequisites / NoticeBefore taking this course, students are encouraged to complete "Bioelectronics and Biosensors" (227-0393-10L).

As part of the exercises for this class, students are expected to complete a programming or literature review project to be defined at the beginning of the semester.
227-1034-00LComputational Vision (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: INI402

Mind the enrolment deadlines at UZH:
https://www.uzh.ch/cmsssl/en/studies/application/deadlines.html
W6 credits2V + 1UD. Kiper
AbstractThis course focuses on neural computations that underlie visual perception. We study how visual signals are processed in the retina, LGN and visual cortex. We study the morpholgy and functional architecture of cortical circuits responsible for pattern, motion, color, and three-dimensional vision.
ObjectiveThis course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed.
The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will
be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered.
ContentThis course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed.
The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will
be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered.
LiteratureBooks: (recommended references, not required)
1. An Introduction to Natural Computation, D. Ballard (Bradford Books, MIT Press) 1997.
2. The Handbook of Brain Theorie and Neural Networks, M. Arbib (editor), (MIT Press) 1995.
Neural Computation and Theoretical Neurosciences
NumberTitleTypeECTSHoursLecturers
227-0395-00LNeural SystemsW6 credits2V + 1U + 1AR. Hahnloser, M. F. Yanik, B. Grewe
AbstractThis course introduces principles of information processing in neural systems. It covers basic neuroscience for engineering students, experiment techniques used in animal research and methods for inferring neural mechanisms. Students learn about neural information processing and basic principles of natural intelligence and their impact on artificially intelligent systems.
ObjectiveThis course introduces
- Basic neurophysiology and mathematical descriptions of neurons
- Methods for dissecting animal behavior
- Neural recordings in intact nervous systems and information decoding principles
- Methods for manipulating the state and activity in selective neuron types
- Neuromodulatory systems and their computational roles
- Reward circuits and reinforcement learning
- Imaging methods for reconstructing the synaptic networks among neurons
- Birdsong and language
- Neurobiological principles for machine learning.
ContentFrom active membranes to propagation of action potentials. From synaptic physiology to synaptic learning rules. From receptive fields to neural population decoding. From fluorescence imaging to connectomics. Methods for reading and manipulation neural ensembles. From classical conditioning to reinforcement learning. From the visual system to deep convolutional networks. Brain architectures for learning and memory. From birdsong to computational linguistics.
Prerequisites / NoticeBefore taking this course, students are encouraged to complete "Bioelectronics and Biosensors" (227-0393-10L).

As part of the exercises for this class, students are expected to complete a programming or literature review project to be defined at the beginning of the semester.
227-0973-00LTranslational NeuromodelingW8 credits3V + 2U + 1AK. Stephan
AbstractThis course provides a systematic introduction to Translational Neuromodeling (the development of mathematical models for diagnostics of brain diseases) and their application to concrete clinical questions (Computational Psychiatry/Psychosomatics). It focuses on a generative modeling strategy and teaches (hierarchical) Bayesian models of neuroimaging data and behaviour, incl. exercises.
ObjectiveTo obtain an understanding of the goals, concepts and methods of Translational Neuromodeling and Computational Psychiatry/Psychosomatics, particularly with regard to Bayesian models of neuroimaging (fMRI, EEG) and behavioural data.
ContentThis course provides a systematic introduction to Translational Neuromodeling (the development of mathematical models for inferring mechanisms of brain diseases from neuroimaging and behavioural data) and their application to concrete clinical questions (Computational Psychiatry/Psychosomatics). The first part of the course will introduce disease concepts from psychiatry and psychosomatics, their history, and clinical priority problems. The second part of the course concerns computational modeling of neuronal and cognitive processes for clinical applications. A particular focus is on Bayesian methods and generative models, for example, dynamic causal models for inferring neuronal processes from neuroimaging data, and hierarchical Bayesian models for inference on cognitive processes from behavioural data. The course discusses the mathematical and statistical principles behind these models, illustrates their application to various psychiatric diseases, and outlines a general research strategy based on generative models.

Lecture topics include:
1. Introduction to Translational Neuromodeling and Computational Psychiatry/Psychosomatics
2. Psychiatric nosology
3. Pathophysiology of psychiatric disease mechanisms
4. Principles of Bayesian inference and generative modeling
5. Variational Bayes (VB)
6. Bayesian model selection
7. Markov Chain Monte Carlo techniques (MCMC)
8. Bayesian frameworks for understanding psychiatric and psychosomatic diseases
9. Generative models of fMRI data
10. Generative models of electrophysiological data
11. Generative models of behavioural data
12. Computational concepts of schizophrenia, depression and autism
13. Model-based predictions about individual patients

Practical exercises include mathematical derivations and the implementation of specific models and inference methods. In additional project work, students are required to use one of the examples discussed in the course as a basis for developing their own generative model and use it for simulations and/or inference in application to a clinical question. Group work (up to 3 students) is required.
LiteratureSee TNU website:
https://www.tnu.ethz.ch/en/teaching
Prerequisites / NoticeGood knowledge of principles of statistics, good programming skills (MATLAB, Julia, or Python)
252-1424-00LModels of Computation Information W6 credits2V + 2U + 1AM. Cook
AbstractThis course surveys many different models of computation: Turing Machines, Cellular Automata, Finite State Machines, Graph Automata, Circuits, Tilings, Lambda Calculus, Fractran, Chemical Reaction Networks, Hopfield Networks, String Rewriting Systems, Tag Systems, Diophantine Equations, Register Machines, Primitive Recursive Functions, and more.
ObjectiveThe goal of this course is to become acquainted with a wide variety of models of computation, to understand how models help us to understand the modeled systems, and to be able to develop and analyze models appropriate for new systems.
ContentThis course surveys many different models of computation: Turing Machines, Cellular Automata, Finite State Machines, Graph Automata, Circuits, Tilings, Lambda Calculus, Fractran, Chemical Reaction Networks, Hopfield Networks, String Rewriting Systems, Tag Systems, Diophantine Equations, Register Machines, Primitive Recursive Functions, and more.
Neurotechnologies and Neuromorphic Engineering
NumberTitleTypeECTSHoursLecturers
227-1032-00LNeuromorphic Engineering II Information
Information for UZH students:
Enrolment to this course unit only possible at ETH. No enrolment to module INI405 at UZH.

Please mind the ETH enrolment deadlines for UZH students: Link
W6 credits5GT. Delbrück, G. Indiveri, S.‑C. Liu
AbstractThis course teaches the basics of analog chip design and layout with an emphasis on neuromorphic circuits, which are introduced in the fall semester course "Neuromorphic Engineering I".
ObjectiveDesign of a neuromorphic circuit for implementation with CMOS technology.
ContentThis course teaches the basics of analog chip design and layout with an emphasis on neuromorphic circuits, which are introduced in the autumn semester course "Neuromorphic Engineering I".

The principles of CMOS processing technology are presented. Using a set of inexpensive software tools for simulation, layout and verification, suitable for neuromorphic circuits, participants learn to simulate circuits on the transistor level and to make their layouts on the mask level. Important issues in the layout of neuromorphic circuits will be explained and illustrated with examples. In the latter part of the semester students simulate and layout a neuromorphic chip. Schematics of basic building blocks will be provided. The layout will then be fabricated and will be tested by students during the following fall semester.
LiteratureS.-C. Liu et al.: Analog VLSI Circuits and Principles; software documentation.
Prerequisites / NoticePrerequisites: Neuromorphic Engineering I strongly recommended
227-1048-00LNeuromorphic Intelligence (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: INI508

Mind the enrolment deadlines at UZH:
https://www.uzh.ch/cmsssl/en/studies/application/deadlines.htm
W6 credits2VG. Indiveri, E. Donati
AbstractIn this course we will study the computational properties of spiking neural networks implemented using analog "neuromorphic" electronic circuits. We will present network architectures and computational primitives that can use the dynamics of these circuits to exhibit intelligent behaviors. We will characterize these networks and validate them using full custom chips in laboratory experiments.
ObjectiveThe objective of this course is to introduce students to the field of “neuromorphic intelligence” with lectures on spiking neural network architectures implemented using mixed-signal silicon neuron and synapse circuits, and with laboratory sessions using neuromorphic chips to measure the computational properties of different spiking neural network architectures. Class projects will be proposed to validate the models presented in the lectures and carry out real-time signal processing and pattern recognition tasks on real-world sensory data.
ContentStudents will learn about the dynamical properties of adaptive integrate and fire neurons connected with each other via dynamic synapses. They will explore different neural circuits configured to implement computational primitives such as normalization, winner-take-all computation, selective amplification, and pattern discrimination. The experiments will consist of measuring the properties of real silicon neurons using full-custom neuromorphic processors, and configuring them to create neural architectures that can robustly process sensory signals and perform pattern discrimination despite, or thanks to, the limited resolution and large variability of their individual processing
element
Prerequisites / NoticeAccessible to NSC Master students.
It is recommended (but not mandatory) to have taken the Introduction to Neuroinformatics course (INI-401/227-1037-00).
Electives
NumberTitleTypeECTSHoursLecturers
227-0147-00LVLSI II: Design of Very Large Scale Integration Circuits Information W6 credits5GF. K. Gürkaynak, L. Benini
AbstractThis second course in our VLSI series is concerned with how to turn digital circuit netlists into safe, testable and manufacturable mask layout, taking into account various parasitic effects. Low-power circuit design is another important topic. Economic aspects and management issues of VLSI projects round off the course.
ObjectiveKnow how to design digital VLSI circuits that are safe, testable, durable, and make economic sense.
ContentThe second course begins with a thorough discussion of various technical aspects at the circuit and layout level before moving on to economic issues of VLSI. Topics include:
- The difficulties of finding fabrication defects in large VLSI chips.
- How to make integrated circuit testable (design for test).
- Synchronous clocking disciplines compared, clock skew, clock distribution, input/output timing.
- Synchronization and metastability.
- CMOS transistor-level circuits of gates, flip-flops and random access memories.
- Sinks of energy in CMOS circuits.
- Power estimation and low-power design.
- Current research in low-energy computing.
- Layout parasitics, interconnect delay, static timing analysis.
- Switching currents, ground bounce, IR-drop, power distribution.
- Floorplanning, chip assembly, packaging.
- Layout design at the mask level, physical design verification.
- Electromigration, electrostatic discharge, and latch-up.
- Models of industrial cooperation in microelectronics.
- The caveats of virtual components.
- The cost structures of ASIC development and manufacturing.
- Market requirements, decision criteria, and case studies.
- Yield models.
- Avenues to low-volume fabrication.
- Marketing considerations and case studies.
- Management of VLSI projects.

Exercises are concerned with back-end design (floorplanning, placement, routing, clock and power distribution, layout verification). Industrial CAD tools are being used.
Lecture notesH. Kaeslin: "Top-Down Digital VLSI Design, from Gate-Level Circuits to CMOS Fabrication", Lecture Notes Vol.2 , 2015.

All written documents in English.
LiteratureH. Kaeslin: "Top-Down Digital VLSI Design, from Architectures to Gate-Level Circuits and FPGAs", Elsevier, 2014, ISBN 9780128007303.
Prerequisites / NoticeHighlight:
Students are offered the opportunity to design a circuit of their own which then gets actually fabricated as a microchip! Students who elect to participate in this program register for a term project at the Integrated Systems Laboratory in parallel to attending the VLSI II course.

Prerequisites:
"VLSI I: from Architectures to Very Large Scale Integration Circuits and FPGAs" or equivalent knowledge.

Further details:
https://vlsi2.ethz.ch
227-0427-10LAdvanced Signal Analysis, Modeling, and Machine Learning Information W6 credits4GH.‑A. Loeliger
AbstractThe course develops a selection of topics pivoting around graphical models (factor graphs), state space methods, sparsity, and pertinent algorithms.
ObjectiveThe course develops a selection of topics pivoting around factor graphs, state space methods, and pertinent algorithms:
- factor graphs and message passing algorithms
- hidden-​Markov models
- linear state space models, Kalman filtering, and recursive least squares
- Gaussian message passing
- Gibbs sampling, particle filter
- recursive local polynomial fitting & applications
- parameter learning by expectation maximization
- sparsity and spikes
- binary control and digital-​to-analog conversion
- duality and factor graph transforms
Lecture notesLecture notes
Prerequisites / NoticeSolid mathematical foundations (especially in probability, estimation, and linear algebra) as provided by the course "Introduction to Estimation and Machine Learning".
227-0395-00LNeural SystemsW6 credits2V + 1U + 1AR. Hahnloser, M. F. Yanik, B. Grewe
AbstractThis course introduces principles of information processing in neural systems. It covers basic neuroscience for engineering students, experiment techniques used in animal research and methods for inferring neural mechanisms. Students learn about neural information processing and basic principles of natural intelligence and their impact on artificially intelligent systems.
ObjectiveThis course introduces
- Basic neurophysiology and mathematical descriptions of neurons
- Methods for dissecting animal behavior
- Neural recordings in intact nervous systems and information decoding principles
- Methods for manipulating the state and activity in selective neuron types
- Neuromodulatory systems and their computational roles
- Reward circuits and reinforcement learning
- Imaging methods for reconstructing the synaptic networks among neurons
- Birdsong and language
- Neurobiological principles for machine learning.
ContentFrom active membranes to propagation of action potentials. From synaptic physiology to synaptic learning rules. From receptive fields to neural population decoding. From fluorescence imaging to connectomics. Methods for reading and manipulation neural ensembles. From classical conditioning to reinforcement learning. From the visual system to deep convolutional networks. Brain architectures for learning and memory. From birdsong to computational linguistics.
Prerequisites / NoticeBefore taking this course, students are encouraged to complete "Bioelectronics and Biosensors" (227-0393-10L).

As part of the exercises for this class, students are expected to complete a programming or literature review project to be defined at the beginning of the semester.
227-1032-00LNeuromorphic Engineering II Information
Information for UZH students:
Enrolment to this course unit only possible at ETH. No enrolment to module INI405 at UZH.

Please mind the ETH enrolment deadlines for UZH students: Link
W6 credits5GT. Delbrück, G. Indiveri, S.‑C. Liu
AbstractThis course teaches the basics of analog chip design and layout with an emphasis on neuromorphic circuits, which are introduced in the fall semester course "Neuromorphic Engineering I".
ObjectiveDesign of a neuromorphic circuit for implementation with CMOS technology.
ContentThis course teaches the basics of analog chip design and layout with an emphasis on neuromorphic circuits, which are introduced in the autumn semester course "Neuromorphic Engineering I".

The principles of CMOS processing technology are presented. Using a set of inexpensive software tools for simulation, layout and verification, suitable for neuromorphic circuits, participants learn to simulate circuits on the transistor level and to make their layouts on the mask level. Important issues in the layout of neuromorphic circuits will be explained and illustrated with examples. In the latter part of the semester students simulate and layout a neuromorphic chip. Schematics of basic building blocks will be provided. The layout will then be fabricated and will be tested by students during the following fall semester.
LiteratureS.-C. Liu et al.: Analog VLSI Circuits and Principles; software documentation.
Prerequisites / NoticePrerequisites: Neuromorphic Engineering I strongly recommended
227-1046-00LComputer Simulations of Sensory Systems Information W3 credits3GT. Haslwanter
AbstractThis course deals with computer simulations of the human auditory, visual, and balance system. The lecture will cover the physiological and mechanical mechanisms of these sensory systems. And in the exercises, the simulations will be implemented with Python. The simulations will be such that their output could be used as input for actual neuro-sensory prostheses.
ObjectiveOur sensory systems provide us with information about what is happening in the world surrounding us. Thereby they transform incoming mechanical, electromagnetic, and chemical signals into “action potentials”, the language of the central nervous system.
The main goal of this lecture is to describe how our sensors achieve these transformations, how they can be reproduced with computational tools. For example, our auditory system performs approximately a “Fourier transformation” of the incoming sound waves; our early visual system is optimized for finding edges in images that are projected onto our retina; and our balance system can be well described with a “control system” that transforms linear and rotational movements into nerve impulses.
In the exercises that go with this lecture, we will use Python to reproduce the transformations achieved by our sensory systems. The goal is to write programs whose output could be used as input for actual neurosensory prostheses: such prostheses have become commonplace for the auditory system, and are under development for the visual and the balance system. For the corresponding exercises, at least some basic programing experience is required!!
ContentThe following topics will be covered:
• Introduction into the signal processing in nerve cells.
• Introduction into Python.
• Simplified simulation of nerve cells (Hodgkins-Huxley model).
• Description of the auditory system, including the application of Fourier transforms on recorded sounds.
• Description of the visual system, including the retina and the information processing in the visual cortex. The corresponding exercises will provide an introduction to digital image processing.
• Description of the mechanics of our balance system, and the “Control System”-language that can be used for an efficient description of the corresponding signal processing (essentially Laplace transforms and control systems).
Lecture notesFor each module additional material will be provided on the e-learning platform "moodle". The main content of the lecture is also available as a wikibook, under http://en.wikibooks.org/wiki/Sensory_Systems
LiteratureOpen source information is available as wikibook http://en.wikibooks.org/wiki/Sensory_Systems

For good overviews of the neuroscience, I recommend:

• Principles of Neural Science (5th Ed, 2012), by Eric Kandel, James Schwartz, Thomas Jessell, Steven Siegelbaum, A.J. Hudspeth
ISBN 0071390111 / 9780071390118
THE standard textbook on neuroscience.
NOTE: The 6th edition will be released on February 5, 2021!
• L. R. Squire, D. Berg, F. E. Bloom, Lac S. du, A. Ghosh, and N. C. Spitzer. Fundamental Neuroscience, Academic Press - Elsevier, 2012 [ISBN: 9780123858702].
This book covers the biological components, from the functioning of an individual ion channels through the various senses, all the way to consciousness. And while it does not cover the computational aspects, it nevertheless provides an excellent overview of the underlying neural processes of sensory systems.

• G. Mather. Foundations of Sensation and Perception, 2nd Ed Psychology Press, 2009 [ISBN: 978-1-84169-698-0 (hardcover), oder 978-1-84169-699-7 (paperback)]
A coherent, up-to-date introduction to the basic facts and theories concerning human sensory perception.

• The best place to get started with Python programming are the https://scipy-lectures.org/

On signal processing with Python, my upcoming book
• Hands-on Signal Analysis with Python (Due: January 13, 2021
ISBN 978-3-030-57902-9, https://www.springer.com/gp/book/9783030579029)
will contain an explanation to all the required programming tools and packages.
Prerequisites / Notice• Since I have to gravel from Linz, Austria, to Zurich to give this lecture, I plan to hold this lecture in blocks (every 2nd week).
• In addition to the lectures, this course includes external lab visits to institutes actively involved in research on the relevant sensory systems.
227-1048-00LNeuromorphic Intelligence (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: INI508

Mind the enrolment deadlines at UZH:
https://www.uzh.ch/cmsssl/en/studies/application/deadlines.htm
W6 credits2VG. Indiveri, E. Donati
AbstractIn this course we will study the computational properties of spiking neural networks implemented using analog "neuromorphic" electronic circuits. We will present network architectures and computational primitives that can use the dynamics of these circuits to exhibit intelligent behaviors. We will characterize these networks and validate them using full custom chips in laboratory experiments.
ObjectiveThe objective of this course is to introduce students to the field of “neuromorphic intelligence” with lectures on spiking neural network architectures implemented using mixed-signal silicon neuron and synapse circuits, and with laboratory sessions using neuromorphic chips to measure the computational properties of different spiking neural network architectures. Class projects will be proposed to validate the models presented in the lectures and carry out real-time signal processing and pattern recognition tasks on real-world sensory data.
ContentStudents will learn about the dynamical properties of adaptive integrate and fire neurons connected with each other via dynamic synapses. They will explore different neural circuits configured to implement computational primitives such as normalization, winner-take-all computation, selective amplification, and pattern discrimination. The experiments will consist of measuring the properties of real silicon neurons using full-custom neuromorphic processors, and configuring them to create neural architectures that can robustly process sensory signals and perform pattern discrimination despite, or thanks to, the limited resolution and large variability of their individual processing
element
Prerequisites / NoticeAccessible to NSC Master students.
It is recommended (but not mandatory) to have taken the Introduction to Neuroinformatics course (INI-401/227-1037-00).
402-0673-00LPhysics in Medical Research: From Humans to CellsW6 credits2V + 1UB. K. R. Müller
AbstractThe aim of this lecture series is to introduce the role of physics in state-of-the-art medical research and clinical practice. Topics to be covered range from applications of physics in medical implant technology and tissue engineering, through imaging technology, to its role in interventional and non-interventional therapies.
ObjectiveThe lecture series is focused on applying knowledge from physics in diagnosis, planning, and therapy close to clinical practice and fundamental medical research. Beside a general overview, the lectures give a deep insight into a very few selected techniques, which will help the students to apply the knowledge to a broad range of related techniques.

In particular, the lectures will elucidate the physics behind the X-ray imaging currently used in clinical environment and contemporary high-resolution developments. It is the goal to visualize and quantify (sub-)microstructures of human tissues and implants as well as their interface.

Ultrasound is not only used for diagnostic purposes but includes therapeutic approaches such as the control of the blood-brain barrier under MR-guidance.

Physicists in medicine are working on modeling and simulation. Based on the vascular structure in cancerous and healthy tissues, the characteristic approaches in computational physics to develop strategies against cancer are presented. In order to deliberately destroy cancerous tissue, heat can be supplied or extracted in different manner: cryotherapy (heat conductivity in anisotropic, viscoelastic environment), radiofrequency treatment (single and multi-probe), laser application, and proton therapy.

Medical implants play an important role to take over well-defined tasks within the human body. Although biocompatibility is here of crucial importance, the term is insufficiently understood. The aim of the lectures is the understanding of biocompatibility performing well-defined experiments in vitro and in vivo. Dealing with different classes of materials (metals, ceramics, polymers) the influence of surface modifications (morphology and surface coatings) are key issues for implant developments, which might be bio-inspired.

Mechanical stimuli can drastically influence soft and hard tissue behavior. The students should realize that a physiological window exists, where a positive tissue response is expected and how the related parameter including strain, frequency, and resting periods can be selected and optimized for selected tissues such as bone.

For the treatment of severe incontinence, we are developing artificial smart muscles. The students should have a critical look at promising solutions and the selection procedure as well as realize the time-consuming and complex way to clinical practice.

The course will be completed by relating the numerous examples and a common round of questions.
ContentThis lecture series will cover the following topics:
Introduction: Imaging the human body down to individual cells and beyond
Development of artificial muscles for incontinence treatment
X-ray-based computed tomography in clinics and related medical research
High-resolution micro computed tomography
Phase tomography using hard X-rays in biomedical research
Metal-based implants and scaffolds
Natural and synthetic ceramics for implants and regenerative medicine
Biomedical simulations
Polymers for medical implants
From open surgery to non-invasive interventions - Physical approaches in medical imaging
Dental research
Focused Ultrasound and its clinical use
Applying physics in medicine: Benefitting patients
Lecture noteshttp://www.bmc.unibas.ch/education/ETH_Zurich.phtml

login and password to be provided during the lecture
Prerequisites / NoticeStudents from other departments are very welcome to join and gain insight into a variety of sophisticated techniques for the benefit of patients.
No special knowledge is required. Nevertheless, gaps in basic physical knowledge will require additional efforts.
701-1418-00LModelling Course in Population and Evolutionary Biology Information Restricted registration - show details
Number of participants limited to 20.

Priority is given to MSc Biology and Environmental Sciences students.
W4 credits6PS. Bonhoeffer, V. Müller
AbstractThis course provides a "hands-on" introduction into mathematical/computational modelling of biological processes with particular emphasis on evolutionary and population-biological questions. The models are developed using the Open Source software R.
ObjectiveThe aim of this course is to provide a practical introduction into the modelling of fundamental biological questions. The participants will receive guidance to develop mathematical/computational models in small teams. The participants chose two modules with different levels of difficulty from a list of projects.

The participant shall get a sense of the utility of modelling as a tool to investigate biological problems. The simpler modules are based mostly on examples from the earlier lecture "Ecology and evolution: populations" (script accessible at the course webpage). The advanced modules address topical research questions. Although being based on evolutionary and population biological methods and concepts, these modules also address topics from other areas of biology.
Contentsee www.tb.ethz.ch/education/learningmaterials/modelingcourse.html
Lecture notesDetailed handouts describing both the modelling and the biological background are available to each module at the course website. In addition, the script of the earlier lecture "Ecology and evolution: populations" can also be downloaded, and contains further background information.
Prerequisites / NoticeThe course is based on the open source software R. Experience with R is useful but not required for the course. Similarly, the course 701-1708-00L Infectious Disease Dynamics is useful but not required.
GESS Science in Perspective
» see GESS Compulsory Electives: Type A: Enhancement of Reflection Capability
» Recommended GESS compulsory elective courses (Type B) for D-ITET
» see GESS Compulsory Electives: Language Courses ETH/UZH
Master's Thesis and Semester Papers/Seminars
Option 1: Long Master Thesis
NumberTitleTypeECTSHoursLecturers
227-1041-01LNSC Master's Thesis (long) and Exam (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: INI503

Mind the enrolment deadlines at UZH:
https://www.uzh.ch/cmsssl/en/studies/application/deadlines.html

Only students who fulfil the following criteria are allowed to begin with their master thesis:
a. successful completion of the bachelor programme;
b. fulfilling of any additional requirements necessary to gain admission to the master programme.
W45 credits96DR. Hahnloser
AbstractThe Master thesis concludes the study programme. Thesis work should prove the students' ability to independent, structured and scientific working.
Objectivesee above
Prerequisites / NoticeApplication forms can be downloaded at http://www.nsc.uzh.ch/?id=21602&master=10511&top=10532. Note: the oral part of the exam must be completed before the written part.
Option 2: Short Master's Thesis and Semester Papers/Seminars
Short Master Thesis
NumberTitleTypeECTSHoursLecturers
227-1041-02LNSC Master's Thesis (short) and Exam (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: INI504

Mind the enrolment deadlines at UZH:
https://www.uzh.ch/cmsssl/en/studies/application/deadlines.html
W29 credits62DR. Hahnloser
AbstractThe Master thesis concludes the study programme. Thesis work should prove the students' ability to independent, structured and scientific working.
Objectivesee above
Prerequisites / NoticeApplication forms can be downloaded at http://www.nsc.uzh.ch/?id=21602&master=10511&top=10532. Note: the oral part of the exam must be completed before the written part.
Semester Papers/Seminars
NumberTitleTypeECTSHoursLecturers
227-1036-01LNSC Master Short Project I (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: INI505

Mind the enrolment deadlines at UZH:
https://www.uzh.ch/cmsssl/en/studies/application/deadlines.html
W8 credits17AR. Hahnloser
AbstractUsually a student selects the topic of a Master Short Project in consultation with his or her mentor.
Objectivesee above
227-1036-02LNSC Master Short Project II (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: INI506

Mind the enrolment deadlines at UZH:
https://www.uzh.ch/cmsssl/en/studies/application/deadlines.html
W8 credits17AR. Hahnloser
AbstractUsually a student selects the topic of a Master Short Project in consultation with his or her mentor.
Objectivesee above