# Suchergebnis: Katalogdaten im Herbstsemester 2017

Neural Systems and Computation Master | ||||||

Kernfächer | ||||||

Obligatorische Kernfächer | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
---|---|---|---|---|---|---|

227-1039-00L | Basics of Instrumentation, Measurement, and Analysis (University of Zurich) No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: INI502 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/mobilitaet.html Registration in this class requires the permission of the instructors. Class size will be limited to available lab spots. Preference is given to students that require this class as part of their major. | O | 4 KP | 9S | S.‑C. Liu, T. Delbrück, R. Hahnloser, G. Indiveri, V. Mante, P. Pyk, W. von der Behrens | |

Kurzbeschreibung | Experimental data are always as good as the instrumentation and measurement, but never any better. This course provides the very basics of instrumentation relevant to neurophysiology and neuromorphic engineering, it consists of two parts: a common introductory part involving analog signals and their acquisition (Part I), and a more specialized second part (Part II). | |||||

Lernziel | The goal of Part I is to provide a general introduction to the signal acquisition process. Students are familiarized with basic lab equipment such as oscilloscopes, function generators, and data acquisition devices. Different electrical signals are generated, visualized, filtered, digitized, and analyzed using Matlab (Mathworks Inc.) or Labview (National Instruments). In Part II, the students are divided into small groups to work on individual measurement projects according to availability and interest. Students single-handedly solve a measurement task, making use of their basic knowledge acquired in the first part. Various signal sources will be provided. | |||||

Voraussetzungen / Besonderes | For each part, students must hand in a written report and present a live demonstration of their measurement setup to the respective supervisor. The supervisor of Part I is the teaching assistant, and the supervisor of Part II is task specific. Admission to Part II is conditional on completion of Part I (report + live demonstration). Reports must contain detailed descriptions of the measurement goal, the measurement procedure, and the measurement outcome. Either confidence or significance of measurements must be provided. Acquisition and analysis software must be documented. | |||||

227-1031-00L | Journal 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/mobilitaet.html | O | 2 KP | 1S | G. Indiveri | |

Kurzbeschreibung | The 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. | |||||

Lernziel | The 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. | |||||

Inhalt | Relevant current papers in neurosciences and neuroinformatics are covered. | |||||

227-1043-00L | Neuroinformatics - Colloquia (University of Zurich)No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: INI701 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/mobilitaet.html | W | 0 KP | 1K | S.‑C. Liu, R. Hahnloser, V. Mante | |

Kurzbeschreibung | Das Kolloquium der Neuroinformatik ist eine Vortragsserie eingeladener Experten. Die Vorträge spiegeln Schwerpunkte aus der Neurobiologie und des Neuromorphic Engineering wider, die speziell für unser Institut von Relevanz sind. | |||||

Lernziel | Die Vorträge informieren Studenten und Forscher über neueste Forschungsergebnisse. Dementsprechend sind die Vorträge primaer nicht fuer wissenschaftliche Laien, sondern für Forschungsspezialisten konzipiert. | |||||

Inhalt | Die Themen haengen stark von den eingeladenen Spezialisten ab und wechseln von Woche zu Woche. Alle Themen beschreiben aber 'Neural computation' und deren Implementierung in biologischen und kuenstlichen Systemen. | |||||

227-1045-00L | Readings in Neuroinformatics (University of Zurich)No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: INI431 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/mobilitaet.html | O | 3 KP | 1S | G. Indiveri, M. Cook, D. Kiper, K. A. Martin | |

Kurzbeschreibung | Thirteen major areas of research have been selected, which cover the key concepts that have led to our current ideas of how the nervous system is built and functions. We will read both original papers and explore the conceptual the links between them and discuss the 'sociology' of science, the pursuit of basic science questions over a century of research." | |||||

Lernziel | It is a commonplace that scientists rarely cite literature that is older than 10 years and when they do, they usually cite one paper that serves as the representative for a larger body of work that has long since been incorporated anonymously in textbooks. Worse than that, many authors have not even read the papers they cite in their own publications. This course, ‘Foundations of Neuroscience’ is one antidote. Thirteen major areas of research have been selected, which cover the key concepts that have led to our current ideas of how the nervous system is built and functions. Unusually, we will explore these areas of research by reading the original publications, instead of reading someone else’s digested summary from a textbook or review. By doing this, we will learn how the discoveries were made, what instrumentation was used, how the scientists interpreted their own findings, and how their work, often over many decades and linked together with related findings from many different scientists, generate the current views of mechanism and structure of the nervous system. To give one concrete example, in 1890 Roy and Sherrington showed that there was a neural activity-dependent regulation of blood flow in the brain. One hundred years later, Ogawa discovered that they could use Nuclear Magnetic Resonance (NMR) to measure a blood oxygen-level dependent (BOLD) signal, which they showed was neural activity-dependent. This discovery led to the development of human functional Magnetic Resonance Imaging (fMRI), which has revolutionized neuropsychology and neuropsychiatry. We will read both these original papers and explore the conceptual the links between them and discuss the ‘sociology’ of science, which in this case, the pursuit of basic science questions over a century of research, led to an explosion in applications. We will also explore the personalities of the scientists and the context in which they made their seminal discoveries. Each week the course members will be given original papers to read for homework, they will have to write a short abstract for each paper. We will then meet weekly with the course leader (KACM) and an assistant for an hour-or-so long interactive seminar. An intimate knowledge of the papers will be assumed so that the discussion does not center simply on an explication of the contents of the papers. Assessment will in the form of a written exam in which the students will be given a paper and asked to write a short abstract of the contents. | |||||

Inhalt | It is a commonplace that scientists rarely cite literature that is older than 10 years and when they do, they usually cite one paper that serves as the representative for a larger body of work that has long since been incorporated anonymously in textbooks. Worse than that many authors have not even read the papers they cite in their own publications. This course, ‘Foundations of Neuroscience’ is one antidote. Thirteen major areas of research have been selected, which cover the key concepts that have led to our current ideas of how the nervous system is built and functions. Unusually, we will explore these areas of research by reading the original publications, instead of reading someone else’s digested summary from a textbook or review. By doing this, we will learn how the discoveries were made, what instrumentation was used, how the scientists interpreted their own findings, and how their work, often over many decades and by many different scientists, linked together to generate the current view of mechanism and structure. We will also explore the personalities of the scientists and the context in which they made their seminal discoveries. To give one concrete example, in 1890 Roy and Sherrington showed that there was a neural activity-dependent regulation of blood flow in the brain. One hundred years later, Ogawa discovered that they could use Nuclear Magnetic Resonance (NMR) to measure a blood oxygen-level dependent (BOLD) signal, which they showed was neural activity-dependent. This discovery led to the development of human functional Magnetic Resonance Imaging (fMRI), which has revolutionized neuropsychology and neuropsychiatry. We will read both these original papers and explore the conceptual links between them and discuss the ‘sociology’ of science, which in this case, the pursuit of basic science questions over a century of research, led to an explosion in applications. Each week the course members will be given between 2 and 4 papers to read for homework and we will then meet weekly for an hour long interactive seminar. An intimate knowledge of the papers will be assumed so that the discussion does not center simply on an explication of the contents of the papers. Assessment will be done continuously as the individual students are asked to explain a figure, technique, or concept. | |||||

Wählbare Kernfächer | ||||||

Systemneurowissenschaften | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

227-1037-00L | Introduction to Neuroinformatics | W | 6 KP | 2V + 1U | V. Mante, M. Cook, B. Grewe, G. Indiveri, K. A. Martin | |

Kurzbeschreibung | The course provides an introduction to the functional properties of neurons. Particularly the description of membrane electrical properties (action potentials, channels), neuronal anatomy, synaptic structures, and neuronal networks. Simple models of computation, learning, and behavior will be explained. Some artificial systems (robot, chip) are presented. | |||||

Lernziel | Understanding computation by neurons and neuronal circuits is one of the great challenges of science. Many different disciplines can contribute their tools and concepts to solving mysteries of neural computation. The goal of this introductory course is to introduce the monocultures of physics, maths, computer science, engineering, biology, psychology, and even philosophy and history, to discover the enchantments and challenges that we all face in taking on this major 21st century problem and how each discipline can contribute to discovering solutions. | |||||

Inhalt | This course considers the structure and function of biological neural networks at different levels. The function of neural networks lies fundamentally in their wiring and in the electro-chemical properties of nerve cell membranes. Thus, the biological structure of the nerve cell needs to be understood if biologically-realistic models are to be constructed. These simpler models are used to estimate the electrical current flow through dendritic cables and explore how a more complex geometry of neurons influences this current flow. The active properties of nerves are studied to understand both sensory transduction and the generation and transmission of nerve impulses along axons. The concept of local neuronal circuits arises in the context of the rules governing the formation of nerve connections and topographic projections within the nervous system. Communication between neurons in the network can be thought of as information flow across synapses, which can be modified by experience. We need an understanding of the action of inhibitory and excitatory neurotransmitters and neuromodulators, so that the dynamics and logic of synapses can be interpreted. Finally, the neural architectures of feedforward and recurrent networks will be discussed in the context of co-ordination, control, and integration of sensory and motor information in neural networks. | |||||

227-1051-00L | Systems Neuroscience (University of Zurich) No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: INI415 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/mobilitaet.html | W | 6 KP | 2V + 1U | D. Kiper | |

Kurzbeschreibung | This course focuses on basic aspects of central nervous system physiology, including perception, motor control and cognitive functions. | |||||

Lernziel | To understand the basic concepts underlying perceptual, motor and cognitive functions. | |||||

Inhalt | Main emphasis sensory systems, with complements on motor and cognitive functions. | |||||

Skript | None | |||||

Literatur | "The senses", ed. H. Barlow and J. Mollon, Cambridge. "Principles of Neural Science", Kandel, Schwartz, and Jessel | |||||

Voraussetzungen / Besonderes | none | |||||

Neurotechnologie und Neuromorphe Ingenieurwissenschaften Kein Angebot in diesem Semester | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

227-0393-10L | Bioelectronics and Biosensors | W | 6 KP | 2V + 2U | J. Vörös, M. F. Yanik, T. Zambelli | |

Kurzbeschreibung | The course introduces the concepts of bioelectricity and biosensing. The sources and use of electrical fields and currents in the context of biological systems and problems are discussed. The fundamental challenges of measuring biological signals are introduced. The most important biosensing techniques and their physical concepts are introduced in a quantitative fashion. | |||||

Lernziel | During this course the students will: - learn the basic concepts in biosensing and bioelectronics - be able to solve typical problems in biosensing and bioelectronics - learn about the remaining challenges in this field | |||||

Inhalt | L1. Bioelectronics history, its applications and overview of the field - Volta and Galvani dispute - BMI, pacemaker, cochlear implant, retinal implant, limb replacement devices - Fundamentals of biosensing - Glucometer and ELISA L2. Fundamentals of quantum and classical noise in measuring biological signals L3. Biomeasurement techniques with photons L4. Acoustics sensors - Differential equation for quartz crystal resonance - Acoustic sensors and their applications L5. Engineering principles of optical probes for measuring and manipulating molecular and cellular processes L6. Optical biosensors - Differential equation for optical waveguides - Optical sensors and their applications - Plasmonic sensing L7. Basic notions of molecular adsorption and electron transfer - Quantum mechanics: Schrödinger equation energy levels from H atom to crystals, energy bands - Electron transfer: Marcus theory, Gerischer theory L8. Potentiometric sensors - Fundamentals of the electrochemical cell at equilibrium (Nernst equation) - Principles of operation of ion-selective electrodes L9. Amperometric sensors and bioelectric potentials - Fundamentals of the electrochemical cell with an applied overpotential to generate a faraday current - Principles of operation of amperometric sensors - Ion flow through a membrane (Fick equation, Nernst equation, Donnan equilibrium, Goldman equation) L10. Channels, amplification, signal gating, and patch clamp Y4 L11. Action potentials and impulse propagation L12. Functional electric stimulation and recording - MEA and CMOS based recording - Applying potential in liquid - simulation of fields and relevance to electric stimulation L13. Neural networks memory and learning | |||||

Literatur | Plonsey and Barr, Bioelectricity: A Quantitative Approach (Third edition) | |||||

Voraussetzungen / Besonderes | Supervised exercises solving real-world problems. Some Matlab based exercises in groups. | |||||

227-1033-00L | Neuromorphic Engineering I Registration in this class requires the permission of the instructors. Class size will be limited to available lab spots. Preference is given to students that require this class as part of their major. | W | 6 KP | 2V + 3U | T. Delbrück, G. Indiveri, S.‑C. Liu | |

Kurzbeschreibung | This course covers analog circuits with emphasis on neuromorphic engineering: MOS transistors in CMOS technology, static circuits, dynamic circuits, systems (silicon neuron, silicon retina, silicon cochlea) with an introduction to multi-chip systems. The lectures are accompanied by weekly laboratory sessions. | |||||

Lernziel | Understanding of the characteristics of neuromorphic circuit elements. | |||||

Inhalt | Neuromorphic circuits are inspired by the organizing principles of biological neural circuits. Their computational primitives are based on physics of semiconductor devices. Neuromorphic architectures often rely on collective computation in parallel networks. Adaptation, learning and memory are implemented locally within the individual computational elements. Transistors are often operated in weak inversion (below threshold), where they exhibit exponential I-V characteristics and low currents. These properties lead to the feasibility of high-density, low-power implementations of functions that are computationally intensive in other paradigms. Application domains of neuromorphic circuits include silicon retinas and cochleas for machine vision and audition, real-time emulations of networks of biological neurons, and the development of autonomous robotic systems. This course covers devices in CMOS technology (MOS transistor below and above threshold, floating-gate MOS transistor, phototransducers), static circuits (differential pair, current mirror, transconductance amplifiers, etc.), dynamic circuits (linear and nonlinear filters, adaptive circuits), systems (silicon neuron, silicon retina and cochlea) and an introduction to multi-chip systems that communicate events analogous to spikes. The lectures are accompanied by weekly laboratory sessions on the characterization of neuromorphic circuits, from elementary devices to systems. | |||||

Literatur | S.-C. Liu et al.: Analog VLSI Circuits and Principles; various publications. | |||||

Voraussetzungen / Besonderes | Particular: The course is highly recommended for those who intend to take the spring semester course 'Neuromorphic Engineering II', that teaches the conception, simulation, and physical layout of such circuits with chip design tools. Prerequisites: Background in basics of semiconductor physics helpful, but not required. | |||||

227-1037-00L | Introduction to Neuroinformatics | W | 6 KP | 2V + 1U | V. Mante, M. Cook, B. Grewe, G. Indiveri, K. A. Martin | |

Kurzbeschreibung | The course provides an introduction to the functional properties of neurons. Particularly the description of membrane electrical properties (action potentials, channels), neuronal anatomy, synaptic structures, and neuronal networks. Simple models of computation, learning, and behavior will be explained. Some artificial systems (robot, chip) are presented. | |||||

Lernziel | Understanding computation by neurons and neuronal circuits is one of the great challenges of science. Many different disciplines can contribute their tools and concepts to solving mysteries of neural computation. The goal of this introductory course is to introduce the monocultures of physics, maths, computer science, engineering, biology, psychology, and even philosophy and history, to discover the enchantments and challenges that we all face in taking on this major 21st century problem and how each discipline can contribute to discovering solutions. | |||||

Inhalt | This course considers the structure and function of biological neural networks at different levels. The function of neural networks lies fundamentally in their wiring and in the electro-chemical properties of nerve cell membranes. Thus, the biological structure of the nerve cell needs to be understood if biologically-realistic models are to be constructed. These simpler models are used to estimate the electrical current flow through dendritic cables and explore how a more complex geometry of neurons influences this current flow. The active properties of nerves are studied to understand both sensory transduction and the generation and transmission of nerve impulses along axons. The concept of local neuronal circuits arises in the context of the rules governing the formation of nerve connections and topographic projections within the nervous system. Communication between neurons in the network can be thought of as information flow across synapses, which can be modified by experience. We need an understanding of the action of inhibitory and excitatory neurotransmitters and neuromodulators, so that the dynamics and logic of synapses can be interpreted. Finally, the neural architectures of feedforward and recurrent networks will be discussed in the context of co-ordination, control, and integration of sensory and motor information in neural networks. | |||||

Wahlfächer | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |

401-0151-00L | Lineare Algebra | W | 4 KP | 3G + 2U | V. C. Gradinaru | |

Kurzbeschreibung | Inhalt: Lineare Gleichungssysteme - der Algorithmus von Gauss, Matrizen - LR-Zerlegung, Determinanten, Vektorräume, Ausgleichsrechnung - QR-Zerlegung, Lineare Abbildungen, Eigenwertproblem, Normalformen -Singulärwertzerlegung; numerische Aspekte; Einführung in MATLAB. | |||||

Lernziel | Einführung in die Lineare Algebra für Ingenieure unter Berücksichtigung numerischer Aspekte | |||||

Skript | K. Nipp / D. Stoffer, Lineare Algebra, vdf Hochschulverlag, 5. Auflage 2002 | |||||

Literatur | K. Nipp / D. Stoffer, Lineare Algebra, vdf Hochschulverlag, 5. Auflage 2002 | |||||

401-0603-00L | Stochastik | W | 4 KP | 2V + 1U | M. Schweizer | |

Kurzbeschreibung | Die Vorlesung deckt folgende Themenbereiche ab: Zufallsvariablen, Wahrscheinlichkeit und Wahrscheinlichkeitsverteilungen, gemeinsame und bedingte Wahrscheinlichkeiten und Verteilungen, das Gesetz der Grossen Zahlen, der zentrale Grenzwertsatz, deskriptive Statistik, schliessende Statistik, Statistik bei normalverteilten Daten, Punktschätzungen, und Vergleich zweier Stichproben. | |||||

Lernziel | Kenntnis der Grundlagen der Wahrscheinlichkeitstheorie und Statistik. | |||||

Inhalt | Einführung in die Wahrscheinlichkeitstheorie, einige Grundbegriffe der mathematischen Statistik und Methoden der angewandten Statistik. | |||||

Skript | Vorlesungsskript | |||||

Literatur | Vorlesungsskript | |||||

402-0811-00L | Programming Techniques for Scientific Simulations I | W | 5 KP | 4G | R. Käppeli | |

Kurzbeschreibung | This lecture provides an overview of programming techniques for scientific simulations. The focus is on advances C++ programming techniques and scientific software libraries. Based on an overview over the hardware components of PCs and supercomputer, optimization methods for scientific simulation codes are explained. | |||||

Lernziel | ||||||

402-0809-00L | Introduction to Computational Physics | W | 8 KP | 2V + 2U | H. J. Herrmann | |

Kurzbeschreibung | Diese Vorlesung bietet eine Einführung in Computersimulationsmethoden für physikalische Probleme und deren Implementierung auf PCs und Supercomputern: klassische Bewegungsgleichungen, partielle Differentialgleichungen (Wellengleichung, Diffussionsgleichung, Maxwell-Gleichungen), Monte Carlo Simulation, Perkolation, Phasenübergänge | |||||

Lernziel | ||||||

Inhalt | Einführung in die rechnergestützte Simulation physikalischer Probleme. Anhand einfacher Modelle aus der klassischen Mechanik, Elektrodynamik und statistischen Mechanik sowie interdisziplinären Anwendungen werden die wichtigsten objektorientierten Programmiermethoden für numerische Simulationen (überwiegend in C++) erläutert. Daneben wird eine Einführung in die Programmierung von Vektorsupercomputern und parallelen Rechnern, sowie ein Überblick über vorhandene Softwarebibliotheken für numerische Simulationen geboten. | |||||

Voraussetzungen / Besonderes | Vorlesung und Uebung in Englisch, Pruefung wahlweise auf Deutsch oder Englisch | |||||

327-0703-00L | Electron Microscopy in Material Science | W | 4 KP | 2V + 2U | K. Kunze, R. Erni, S. Gerstl, F. Gramm, A. Käch, F. Krumeich | |

Kurzbeschreibung | A comprehensive understanding of the interaction of electrons with condensed matter and details on the instrumentation and methods designed to use these probes in the structural and chemical analysis of various materials. | |||||

Lernziel | A comprehensive understanding of the interaction of electrons with condensed matter and details on the instrumentation and methods designed to use these probes in the structural and chemical analysis of various materials. | |||||

Inhalt | This course provides a general introduction into electron microscopy of organic and inorganic materials. In the first part, the basics of transmission- and scanning electron microscopy are presented. The second part includes the most important aspects of specimen preparation, imaging and image processing. In the third part, recent applications in materials science, solid state physics, structural biology, structural geology and structural chemistry will be reported. | |||||

Skript | will be distributed in English | |||||

Literatur | Goodhew, Humphreys, Beanland: Electron Microscopy and Analysis, 3rd. Ed., CRC Press, 2000 Thomas, Gemming: Analytical Transmission Electron Microscopy - An Introduction for Operators, Springer, Berlin, 2014 Thomas, Gemming: Analytische Transmissionselektronenmikroskopie: Eine Einführung für den Praktiker, Springer, Berlin, 2013 Williams, Carter: Transmission Electron Microscopy, Plenum Press, 1996 Reimer, Kohl: Transmission Electron Microscopy, 5th Ed., Berlin, 2008 Erni: Aberration-corrected imaging in transmission electron microscopy, Imperial College Press (2010, and 2nd ed. 2015) | |||||

227-0147-00L | VLSI II: Design of Very Large Scale Integration Circuits Findet dieses Semester nicht statt. | W | 6 KP | 5G | L. Benini | |

Kurzbeschreibung | Diese weiterführende Lehrveranstaltung in der Reihe VLSI behandelt alle Aspekte des Entwurfs digitaler ASICs von der Netzliste bis zum fertigen Layout unter Berücksichtigung diverser parasitärer Effekte. Besondere Aufmerksamkeit gilt dabei den Aspekten Funktionssicherheit, Testbarkeit und Energieeffizienz. Die Themen Wirtschaftlichkeit und Leitung von VLSI Projekten runden den Kurs ab. | |||||

Lernziel | Digitale VLSI-Schaltungen zu entwerfen wissen, welche funktionssicher, testbar, langlebig, und wirtschaftlich sinnvoll sind. | |||||

Inhalt | Diese weiterführende Lehrveranstaltung behandelt sowohl technische Aspekte auf Schaltungs- und Layout-Niveau als auch ökonomische Fragen hochintegrierter Schaltungen. Behandelt werden: - Grundzüge des Prüfens hochintegrierter Schaltungen auf Fabrikationsdefekte. - Testgerechter Schaltungsentwurf (Design for Test). - Synchrone Taktungsdisziplinen im Vergleich, Clock Skew, Taktverteilung, Input/Output Timing. - Synchronisation und Metastabilität. - Schaltungstechnik von CMOS Gattern, Flip-Flops und RAM Speichern auf Transistorniveau. - Wozu benötigen CMOS Schaltungen überhaupt Energie? - Leistungsabschätzung und Low-Power Design. - Forschungsrichtungen für energieeffizienteres Rechnen. - Layoutbedingte parasitäre Effekte, Leitungsverzögerung, statische Timing Analyse. - Schaltströme, induktiv sowie resistiv bedingte Spannungsabfälle, Speisungsverteilung. - Floorplanning, Chip Assembly, Packaging. - Layout-Entwurf auf Masken-Niveau, Layoutverifikation. - Elektromigration, ESD und Latch-up. - Formen der industriellen Zusammenarbeit in der Mikroelektronik. - Worauf man beim Einsatz Virtueller Komponenten achten muss. - Kostenstrukturen der ASIC Entwicklung und Herstellung. - Anforderungen der Märkte, Entscheidungskriterien sowie Fallbeispiele. - Ausbeutemodelle. - Wege zur Fabrikation kleiner Stückzahlen. - Marktüberlegungen mit Fallbeispielen. - Leitung von VLSI Projekten. Die Übungen führen durch den physischen Entwurf (Floorplanning, Plazierung, Verdrahtung, Takt- und Speisungsverteilung, Layoutverifikation) bis zu den verifizierten GDS II Fabrikationsdaten. Dabei gelangen industrielle CAD-Tools zum Einsatz. | |||||

Skript | H. Kaeslin: "Top-Down Digital VLSI Design, from Gate-Level Circuits to CMOS Fabrication", Lecture Notes Vol.2, 2015. Sämtliche Unterlagen in englischer Sprache. | |||||

Literatur | H. Kaeslin: "Top-Down Digital VLSI Design, from Architectures to Gate-Level Circuits and FPGAs", Elsevier, 2014, ISBN 9780128007303. | |||||

Voraussetzungen / Besonderes | Highlight: Es wird die Möglichkeit geboten einen Chip nach eigenen Ideen zu entwickeln, welcher anschliessend tatsächlich fabriziert wird! Ein solches Projekt wird in Form einer Semesterarbeit am Institut für Integrierte Systeme parallel zum Besuch von VLSI II durchgeführt. Voraussetzungen: "VLSI I: von Architektur zu hochintegrierter Schaltung und FPGA" oder gleichwertige Kenntnisse. Weiterführende Informationen: https://iis-students.ee.ethz.ch/lectures/vlsi-ii/ | |||||

402-0341-00L | Medical Physics I | W | 6 KP | 2V + 1U | P. Manser | |

Kurzbeschreibung | Introduction to the fundamentals of medical radiation physics. Functional chain due to radiation exposure from the primary physical effect to the radiobiological and medically manifest secondary effects. Dosimetric concepts of radiation protection in medicine. Mode of action of radiation sources used in medicine and its illustration by means of Monte Carlo simulations. | |||||

Lernziel | Understanding the functional chain from primary physical effects of ionizing radiation to clinical radiation effects. Dealing with dose as a quantitative measure of medical exposure. Getting familiar with methods to generate ionizing radiation in medicine and learn how they are applied for medical purposes. Eventually, the lecture aims to show the students that medical physics is a fascinating and evolving discipline where physics can directly be used for the benefits of patients and the society. | |||||

Inhalt | The lecture is covering the basic principles of ionzing radiation and its physical and biological effects. The physical interactions of photons as well as of charged particles will be reviewed and their consequences for medical applications will be discussed. The concept of Monte Carlo simulation will be introduced in the excercises and will help the student to understand the characteristics of ionizing radiation in simple and complex situations. Fundamentals in dosimetry will be provided in order to understand the physical and biological effects of ionizing radiation. Deterministic as well as stochastic effects will be discussed and fundamental knowledge about radiation protection will be provided. In the second part of the lecture series, we will cover the generation of ionizing radiation. By this means, the x-ray tube, the clinical linear accelarator, and different radioactive sources in radiology, radiotherapy and nuclear medicine will be addressed. Applications in radiolgoy, nuclear medicine and radiotherapy will be described with a special focus on the physics underlying these applications. | |||||

Skript | A script will be provided. | |||||

227-1047-00L | Consciousness: From Philosophy to Neuroscience (University of Zurich) No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: INI410 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/mobilitaet.html | W | 3 KP | 2V | D. Kiper, A. Gamma | |

Kurzbeschreibung | This seminar reviews the philosophical and phenomenological as well as the neurobiological aspects of consciousness. The subjective features of consciousness are explored, and modern research into its neural substrate, particularly in the visual domain, is explained. Emphasis is placed on students developing their own thinking through a discussion-centered course structure. | |||||

Lernziel | The course's goal is to give an overview of the contemporary state of consciousness research, with emphasis on the contributions brought by modern cognitive neuroscience. We aim to clarify concepts, explain their philosophical and scientific backgrounds, and to present experimental protocols that shed light on on a variety of consciousness related issues. | |||||

Inhalt | The course includes discussions of scientific as well as philosophical articles. We review current schools of thought, models of consciousness, and proposals for the neural correlate of consciousness (NCC). | |||||

Skript | None | |||||

Literatur | We display articles pertaining to the issues we cover in the class on the course's webpage. | |||||

Voraussetzungen / Besonderes | Since we are all experts on consciousness, we expect active participation and discussions! | |||||

402-0674-00L | Physics in Medical Research: From Atoms to Cells | W | 6 KP | 2V + 1U | B. K. R. Müller | |

Kurzbeschreibung | Scanning probe and diffraction techniques allow studying activated atomic processes during early stages of epitaxial growth. For quantitative description, rate equation analysis, mean-field nucleation and scaling theories are applied on systems ranging from simple metallic to complex organic materials. The knowledge is expanded to optical and electronic properties as well as to proteins and cells. | |||||

Lernziel | The lecture series is motivated by an overview covering the skin of the crystals, roughness analysis, contact angle measurements, protein absorption/activity and monocyte behaviour. As the first step, real structures on clean surfaces including surface reconstructions and surface relaxations, defects in crystals are presented, before the preparation of clean metallic, semiconducting, oxidic and organic surfaces are introduced. The atomic processes on surfaces are activated by the increase of the substrate temperature. They can be studied using scanning tunneling microscopy (STM) and atomic force microscopy (AFM). The combination with molecular beam epitaxy (MBE) allows determining the sizes of the critical nuclei and the other activated processes in a hierarchical fashion. The evolution of the surface morphology is characterized by the density and size distribution of the nanostructures that could be quantified by means of the rate equation analysis, the mean-field nucleation theory, as well as the scaling theory. The surface morphology is further characterized by defects and nanostructure's shapes, which are based on the strain relieving mechanisms and kinetic growth processes. High-resolution electron diffraction is complementary to scanning probe techniques and provides exact mean values. Some phenomena are quantitatively described by the kinematic theory and perfectly understood by means of the Ewald construction. Other phenomena need to be described by the more complex dynamical theory. Electron diffraction is not only associated with elastic scattering but also inelastic excitation mechanisms that reflect the electronic structure of the surfaces studied. Low-energy electrons lead to phonon and high-energy electrons to plasmon excitations. Both effects are perfectly described by dipole and impact scattering. Thin-films of rather complex organic materials are often quantitatively characterized by photons with a broad range of wavelengths from ultra-violet to infra-red light. Asymmetries and preferential orientations of the (anisotropic) molecules are verified using the optical dichroism and second harmonic generation measurements. These characterization techniques are vital for optimizing the preparation of medical implants and the determination of tissue's anisotropies within the human body. Cell-surface interactions are related to the cell adhesion and the contractile cellular forces. Physical means have been developed to quantify these interactions. Other physical techniques are introduced in cell biology, namely to count and sort cells, to study cell proliferation and metabolism and to determine the relation between cell morphology and function. 3D scaffolds are important for tissue augmentation and engineering. Design, preparation methods, and characterization of these highly porous 3D microstructures are also presented. Visiting clinical research in a leading university hospital will show the usefulness of the lecture series. | |||||

227-0427-00L | Signal and Information Processing: Modeling, Filtering, Learning | W | 6 KP | 4G | H.‑A. Loeliger | |

Kurzbeschreibung | Fundamentals in signal processing, detection/estimation, and machine learning. I. Linear signal representation and approximation: Hilbert spaces, LMMSE estimation, regularization and sparsity. II. Learning linear and nonlinear functions and filters: kernel methods, neural networks. III. Structured statistical models: hidden Markov models, factor graphs, Kalman filter, parameter estimation. | |||||

Lernziel | The course is an introduction to some basic topics in signal processing, detection/estimation theory, and machine learning. | |||||

Inhalt | Part I - Linear Signal Representation and Approximation: Hilbert spaces, least squares and LMMSE estimation, projection and estimation by linear filtering, learning linear functions and filters, L2 regularization, L1 regularization and sparsity, singular-value decomposition and pseudo-inverse, principal-components analysis. Part II - Learning Nonlinear Functions: fundamentals of learning, neural networks, kernel methods. Part III - Structured Statistical Models and Message Passing Algorithms: hidden Markov models, factor graphs, Gaussian message passing, Kalman filter and recursive least squares, Monte Carlo methods, parameter estimation, expectation maximization, sparse Bayesian learning. | |||||

Skript | Lecture notes. | |||||

Voraussetzungen / Besonderes | Prerequisites: - local bachelors: course "Discrete-Time and Statistical Signal Processing" (5. Sem.) - others: solid basics in linear algebra and probability theory | |||||

252-0535-00L | Machine Learning | W | 8 KP | 3V + 2U + 2A | J. M. Buhmann | |

Kurzbeschreibung | Machine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects. | |||||

Lernziel | Students will be familiarized with the most important concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. A machine learning project will provide an opportunity to test the machine learning algorithms on real world data. | |||||

Inhalt | The theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data. Topics covered in the lecture include: - Bayesian theory of optimal decisions - Maximum likelihood and Bayesian parameter inference - Classification with discriminant functions: Perceptrons, Fisher's LDA and support vector machines (SVM) - Ensemble methods: Bagging and Boosting - Regression: least squares, ridge and LASSO penalization, non-linear regression and the bias-variance trade-off - Non parametric density estimation: Parzen windows, nearest nieghbour - Dimension reduction: principal component analysis (PCA) and beyond | |||||

Skript | No lecture notes, but slides will be made available on the course webpage. | |||||

Literatur | C. Bishop. Pattern Recognition and Machine Learning. Springer 2007. R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley & Sons, second edition, 2001. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2001. L. Wasserman. All of Statistics: A Concise Course in Statistical Inference. Springer, 2004. | |||||

Voraussetzungen / Besonderes | The course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments. Students should at least have followed one previous course offered by the Machine Learning Institute (e.g., CIL or LIS) or an equivalent course offered by another institution. |

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