Search result: Catalogue data in Spring Semester 2023
Biomedical Engineering Master  | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Bioelectronics | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Track Core Courses During the Master program, a minimum of 12 CP must be obtained from track core courses. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||
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| 227-1032-00L | Neuromorphic Engineering II 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 | W | 6 credits | 5G | T. Delbrück, G. Indiveri, S.‑C. Liu | |||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This 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". | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Design of a neuromorphic circuit for implementation with CMOS technology. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | This 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | S.-C. Liu et al.: Analog VLSI Circuits and Principles; software documentation. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Prerequisites: Neuromorphic Engineering I strongly recommended | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0427-10L | Model-Based Estimation and Signal Analysis | W | 6 credits | 4G | H.‑A. Loeliger | |||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course develops a selection of topics pivoting around state space models, factor graphs, and pertinent algorithms for estimation, model fitting, and learning. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The course develops a selection of topics pivoting around state space methods, factor graphs, and pertinent algorithms: - hidden-Markov models - factor graphs and message passing algorithms - linear state space models, Kalman filtering, and recursive least squares - Gibbs sampling, particle filter - recursive local polynomial fitting for signal analysis - parameter learning by expectation maximization - linear-model fitting beyond least squares: sparsity, Lp-fitting and regularization, jumps - binary, M-level, and half-plane constraints in control and communications | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Lecture notes | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Solid mathematical foundations (especially in probability, estimation, and linear algebra) as provided by the course "Introduction to Estimation and Machine Learning". | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0973-00L | Translational Neuromodeling  | W | 8 credits | 3V + 2U + 1A | K. Stephan | |||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course provides an introduction to Translational Neuromodeling (the development of computational assays of neuronal and cognitive processes) 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 and project work. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | To 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | This course provides a systematic introduction to Translational Neuromodeling (the development of computational assays of neuronal and cognitive processes) 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 and depression 13. Generative embedding: 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 either develop a novel generative model (and demonstrate its properties in simulations) or devise novel applications of an existing model to empirical data in order to address a clinical question. Group work (up to 3 students) is required. Please note that some of the exercises involve the use of open source software in Matlab. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | See TNU website: https://www.tnu.ethz.ch/en/teaching | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Good knowledge of principles of statistics, good programming skills (the majority of the open source software tools used is in MATLAB; for project work, Julia can also be used) | |||||||||||||||||||||||||||||||||||||||||||||||||||||
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