Search result: Catalogue data in Spring Semester 2023

Number	Title	Type	ECTS	Hours	Lecturers
MAS in Medical Physics
Specialisation in General Medical Physics
Major in Neuroinformatics
Electives
227-1046-00L	Computer Simulations of Sensory Systems	W	3 credits	3G	T. Haslwanter
Abstract	This 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.
Learning objective	Our 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!
Content	The 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 notes	For 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
Literature	Open 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 travel from Linz, Austria, to Zurich to give this lecture, I plan to hold this lecture online 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.
376-1792-00L	Introductory Course in Neuroscience II (University of Zurich) No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH as an incoming student. UZH Module Code: SPV0Y020 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/deadlines.html	W	2 credits	2V	University lecturers
Abstract	This course discusses behavioral aspects in neuroscience. Modern brain imaging methods are described. Clinical issues including diseases of the nervous system are studied. Sleep research and neuroimmunology are discussed. Finally, the course deals with the basic concepts in psychiatry.
Learning objective	This course discusses behavioral aspects in neuroscience. Modern brain imaging methods are described. Clinical issues including diseases of the nervous system are studied. Sleep research and neuroimmunology are discussed. Finally, the course deals with the basic concepts in psychiatry.
Prerequisites / Notice	Für Doktorierende des Zentrums für Neurowissenschaften Zürich.
551-0512-00L	Current Topics in Molecular and Cellular Neurobiology Does not take place this semester.	W	2 credits	1S	U. Suter
Abstract	The course is a literature seminar or "journal club". Each Friday a student, or a member of the Suter Lab in the Institute of Molecular Health Sciences, will present a paper from the recent literature.
Learning objective	The course introduces you to recent developments in the fields of cellular and molecular neurobiology. It also supports you to develop your skills in critically reading the scientific literature. You should be able to grasp what the authors wanted to learn e.g. their goals, why the authors chose the experimental approach they used, the strengths and weaknesses of the experiments and the data presented, and how the work fits into the wider literature in the field. You will present one paper yourself, which provides you with practice in public speaking.
Content	You will present one paper yourself. Give an introduction to the field of the paper, then show and comment on the main results (all the papers we present are available online, so you can show original figures with a beamer). Finish with a summary of the main points and a discussion of their significance. You are expected to take part in the discussion and to ask questions. To prepare for this you should read all the papers beforehand (they will be announced a week in advance of the presentation).
Lecture notes	Presentations will be made available after the seminars.
Literature	We cover a range of themes related to development and neurobiology. Before starting your preparations, you are required to check with Laura Montani (laura.montani@biol.ethz.ch), who helps you with finding an appropriate paper.
Prerequisites / Notice	You must attend at least 80% of the journal clubs, and give a presentation of your own. At the end of the semester there will be a 30 minute oral exam on the material presented during the semester. The grade will be based on the exam (45%), your presentation (45%), and a contribution based on your active participation in discussion of other presentations (10%).
227-0424-00L	Model- and Learning-Based Inverse Problems in Imaging	W	4 credits	2V + 1P	V. Vishnevskiy
Abstract	Reconstruction is an inverse problem which estimates images from noisy measurements. Model-based reconstructions use analytical models of the imaging process and priors. Data-based methods directly approximate inversion using training data. Combining these two approaches yields physics-aware neural nets and state-of-the-art imaging accuracy (MRI, US, CT, microscopy, non-destructive imaging).
Learning objective	The goal of this course is to introduce the mathematical models of imaging experiments and practice implementation of numerical methods to solve the corresponding inverse problem. Students will learn how to improve reconstruction accuracy by introducing prior knowledge in the form of regularization models and training data. Furthermore, students will practice incorporating imaging model knowledge into deep neural networks.
Content	The course is based on following fundamental fields: (i) numerical linear algebra, (ii) mathematical statistics and learning theory, (iii) convex optimization and (iv) signal processing. The first part of the course introduces classical linear and nonlinear methods for image reconstruction. The second part considers data-based regularization and covers modern deep learning approaches to inverse problems in imaging. Finally, we introduce advances in the actively developing field of experimental design in biomedical imaging (i.e. how to conduct an experiment in a way to enable the most accurate reconstruction). 1. Introduction: Examples of inverse problems, general introduction. Refresh prerequisites. 2. Linear algebra in imaging: Refresh prerequisites. Demonstrate properties of operators employed in imaging. 3. Linear inverse problems and regularization: Classical theory of inverse problems. Introduce notion of ill-posedness and regularization. 3. Compressed sensing: Sparsity, basis-CS, TV-CS. Notion of analysis and synthesis forms of reconstruction problems. Application of PGD and ADMM to reconstruction. 4. Advanced priors and model selection: Total generalized variation, GMM priors, vectorial TV, low-rank, and tensor models. Stein's unbiased risk estimator. 5. Dictionary and prior learning: Classical dictionary learning. Gentle intro to machine learning. A lot of technical details about patch-models. 6. Deep learning in image reconstruction: Generic convolutional-NN models (automap, residual filtering, u-nets). Talk about the data generation process. Characterized difference between model- and data-based reconstruction methods. Mode averaging. 7. Loop unrolling and physics-aware networks for reconstruction: Autograd, Variational Networks, a lot of examples and intuition. Show how to use them efficiently, e.g. adding preconditioners, attention, etc. 8. Generative models and uncertainty quantification: Amortized posterior, variational autoencoders, adversarial learning. Estimation uncertainty quantification. 9. Inversible networks for estimation: Gradient flows in networks, inversible neural networks for estimation problems. 10. Experimental design in imaging: Acquisition optimization for continuous models. How far can we exploit autograd? 11. Signal sampling optimization in MRI. Reinforcement learning: Acquisition optimization for discrete models. Reinforce and policy gradients, variance minimization for discrete variables (RELAX, REBAR). Cartesian under-sampling pattern design 12. Summary and exam preparation.
Lecture notes	Lecture slides with references will be provided during the course.
Prerequisites / Notice	Students are expected to know the basics of (i) numerical linear algebra, (ii) applied methods of convex optimization, (iii) computational statistics, (iv) Matlab and Python.
376-0022-00L	Imaging and Computing in Medicine	W	6 credits	4G	R. Müller, C. Jutzeler
Abstract	Imaging and computing methods are key to advances and innovation in medicine. This course introduces established fundamentals as well as modern techniques and methods of imaging and computing in medicine.
Learning objective	The learning objectives include 1. Understanding and practical implementation of biosignal processes methods for imaging; 2. Understanding of imaging techniques including radiation imaging, radiographic imaging systems, computed tomography imaging, diagnostic ultrasound imaging, and magnetic resonance imaging; 3. Knowledge of computing, programming, modelling and simulation fundamentals; 4. Computational and systems thinking as well as scripting and programming skills; 5. Understanding and practical implementation of emerging computational methods and their application in medicine including artificial intelligence, deep learning, big data, and complexity; 6. Understanding of the emerging concept of personalised and in silico medicine; 7. Encouragement of critical thinking and creating an environment for independent and self-directed studying.
Content	Imaging and computing methods are key to advances and innovation in medicine. This course introduces established fundamentals as well as modern techniques and methods of imaging and computing in medicine. For the imaging portion of the course, biosignal processing, radiation imaging, radiographic imaging systems, computed tomography imaging, diagnostic ultrasound imaging, and magnetic resonance imaging are covered. For the computing portion of the course, computing, programming, and modelling and simulation fundamentals are covered as well as their application in artificial intelligence and deep learning; complexity and systems medicine; big data and personalised medicine; and computational physiology and in silico medicine. The course is structured as a seminar in three parts of 45 minutes with video lectures and a flipped classroom setup. In the first part (TORQUEs: Tiny, Open-with-Restrictions courses focused on QUality and Effectiveness), students study the basic concepts in short, interactive video lectures on the online learning platform Moodle. Students are able to post questions at the end of each video lecture or the Moodle forum that will be addressed in the second part of the lectures using a flipped classroom concept. For the flipped classroom, the lecturers may prepare additional teaching material to answer the posted questions (Q&A). Following the Q&A, the students will form small groups to acquire additional knowledge using online, python-based activities via JupyterHub or additionally distributed material and discuss their findings in teams. Learning outcomes will be reinforced with weekly Moodle assignments to be completed during the flipped classroom portion.
Lecture notes	Stored on Moodle.
Prerequisites / Notice	Lectures will be given in English.
376-0202-00L	Neural Control of Movement and Motor Learning	W	4 credits	3G	N. Wenderoth, M. Altermatt, S. Gerritzen, C. Lustenberger
Abstract	This course extends the students' knowledge regarding the neural control of movement and motor learning. Particular emphasis will be put on those methods and experimental findings that have shaped current knowledge of this area including fMRI, EEG, TMS, electrical brain stimulation and classical behavioural experiments.
Learning objective	Knowledge of the neurophysiological basis underlying the neural control of movement and motor learning. One central element is that students have first hands-on experience in the lab where small experiments are independently executed, analysed and interpreted.

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