Suchergebnis: Katalogdaten im Herbstsemester 2019

Nummer	Titel	Typ	ECTS	Umfang	Dozierende
Neural Systems and Computation Master
Kernfächer
Obligatorische Kernfächer
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, D. Scaramuzza, 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	Z	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, Y. Sandamirskaya
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
227-0421-00L	Learning in Deep Artificial and Biological Neuronal Networks	W	4 KP	3G	B. Grewe
Kurzbeschreibung	Deep-Learning (DL) a brain-inspired weak for of AI allows training of large artificial neuronal networks (ANNs) that, like humans, can learn real-world tasks such as recognizing objects in images. However, DL is far from being understood and investigating learning in biological networks might serve again as a compelling inspiration to think differently about state-of-the-art ANN training methods.
Lernziel	The main goal of this lecture is to provide a comprehensive overview into the learning principles neuronal networks as well as to introduce a diverse skill set (e.g. simulating a spiking neuronal network) that is required to understand learning in large, hierarchical neuronal networks. To achieve this the lectures and exercises will merge ideas, concepts and methods from machine learning and neuroscience. These will include training basic ANNs, simulating spiking neuronal networks as well as being able to read and understand the main ideas presented in today’s neuroscience papers. After this course students will be able to: - read and understand the main ideas and methods that are presented in today’s neuroscience papers - explain the basic ideas and concepts of plasticity in the mammalian brain - implement alternative ANN learning algorithms to ‘error backpropagation’ in order to train deep neuronal networks. - use a diverse set of ANN regularization methods to improve learning - simulate spiking neuronal networks that learn simple (e.g. digit classification) tasks in a supervised manner.
Inhalt	Deep-learning a brain-inspired weak form of AI allows training of large artificial neuronal networks (ANNs) that, like humans, can learn real-world tasks such as recognizing objects in images. The origins of deep hierarchical learning can be traced back to early neuroscience research by Hubel and Wiesel in the 1960s, who first described the neuronal processing of visual inputs in the mammalian neocortex. Similar to their neocortical counterparts ANNs seem to learn by interpreting and structuring the data provided by the external world. However, while on specific tasks such as playing (video) games deep ANNs outperform humans (Minh et al, 2015, Silver et al., 2018), ANNs are still not performing on par when it comes to recognizing actions in movie data and their ability to act as generalizable problem solvers is still far behind of what the human brain seems to achieve effortlessly. Moreover, biological neuronal networks can learn far more effectively with fewer training examples, they achieve a much higher performance in recognizing complex patterns in time series data (e.g. recognizing actions in movies), they dynamically adapt to new tasks without losing performance and they achieve unmatched performance to detect and integrate out-of-domain data examples (data they have not been trained with). In other words, many of the big challenges and unknowns that have emerged in the field of deep learning over the last years are already mastered exceptionally well by biological neuronal networks in our brain. On the other hand, many facets of typical ANN design and training algorithms seem biologically implausible, such as the non-local weight updates, discrete processing of time, and scalar communication between neurons. Recent evidence suggests that learning in biological systems is the result of the complex interplay of diverse error feedback signaling processes acting at multiple scales, ranging from single synapses to entire networks.
Skript	The lecture slides will be provided as a PDF after each lecture.
Voraussetzungen / Besonderes	This advanced level lecture requires some basic background in machine/deep learning. Thus, students are expected to have a basic mathematical foundation, including linear algebra, multivariate calculus, and probability. The course is not to be meant as an extended tutorial of how to train deep networks in PyTorch or Tensorflow, although these tools used. The participation in the course is subject to the following conditions: 1) The number of participants is limited to 120 students (MSc and PhDs). 2) Students must have taken the exam in Deep Learning (263-3210-00L) or have acquired equivalent knowledge.
227-1037-00L	Introduction to Neuroinformatics	W	6 KP	2V + 1U	V. Mante, M. Cook, B. Grewe, G. Indiveri, D. Kiper, W. von der Behrens
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

Theoretische und Computergestützte Neurowissenschaften
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, D. Kiper, W. von der Behrens
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.
227-0421-00L	Learning in Deep Artificial and Biological Neuronal Networks	W	4 KP	3G	B. Grewe
Kurzbeschreibung	Deep-Learning (DL) a brain-inspired weak for of AI allows training of large artificial neuronal networks (ANNs) that, like humans, can learn real-world tasks such as recognizing objects in images. However, DL is far from being understood and investigating learning in biological networks might serve again as a compelling inspiration to think differently about state-of-the-art ANN training methods.
Lernziel	The main goal of this lecture is to provide a comprehensive overview into the learning principles neuronal networks as well as to introduce a diverse skill set (e.g. simulating a spiking neuronal network) that is required to understand learning in large, hierarchical neuronal networks. To achieve this the lectures and exercises will merge ideas, concepts and methods from machine learning and neuroscience. These will include training basic ANNs, simulating spiking neuronal networks as well as being able to read and understand the main ideas presented in today’s neuroscience papers. After this course students will be able to: - read and understand the main ideas and methods that are presented in today’s neuroscience papers - explain the basic ideas and concepts of plasticity in the mammalian brain - implement alternative ANN learning algorithms to ‘error backpropagation’ in order to train deep neuronal networks. - use a diverse set of ANN regularization methods to improve learning - simulate spiking neuronal networks that learn simple (e.g. digit classification) tasks in a supervised manner.
Inhalt	Deep-learning a brain-inspired weak form of AI allows training of large artificial neuronal networks (ANNs) that, like humans, can learn real-world tasks such as recognizing objects in images. The origins of deep hierarchical learning can be traced back to early neuroscience research by Hubel and Wiesel in the 1960s, who first described the neuronal processing of visual inputs in the mammalian neocortex. Similar to their neocortical counterparts ANNs seem to learn by interpreting and structuring the data provided by the external world. However, while on specific tasks such as playing (video) games deep ANNs outperform humans (Minh et al, 2015, Silver et al., 2018), ANNs are still not performing on par when it comes to recognizing actions in movie data and their ability to act as generalizable problem solvers is still far behind of what the human brain seems to achieve effortlessly. Moreover, biological neuronal networks can learn far more effectively with fewer training examples, they achieve a much higher performance in recognizing complex patterns in time series data (e.g. recognizing actions in movies), they dynamically adapt to new tasks without losing performance and they achieve unmatched performance to detect and integrate out-of-domain data examples (data they have not been trained with). In other words, many of the big challenges and unknowns that have emerged in the field of deep learning over the last years are already mastered exceptionally well by biological neuronal networks in our brain. On the other hand, many facets of typical ANN design and training algorithms seem biologically implausible, such as the non-local weight updates, discrete processing of time, and scalar communication between neurons. Recent evidence suggests that learning in biological systems is the result of the complex interplay of diverse error feedback signaling processes acting at multiple scales, ranging from single synapses to entire networks.
Skript	The lecture slides will be provided as a PDF after each lecture.
Voraussetzungen / Besonderes	This advanced level lecture requires some basic background in machine/deep learning. Thus, students are expected to have a basic mathematical foundation, including linear algebra, multivariate calculus, and probability. The course is not to be meant as an extended tutorial of how to train deep networks in PyTorch or Tensorflow, although these tools used. The participation in the course is subject to the following conditions: 1) The number of participants is limited to 120 students (MSc and PhDs). 2) Students must have taken the exam in Deep Learning (263-3210-00L) or have acquired equivalent knowledge.

Neurotechnologie und Neuromorphe Ingenieurwissenschaften
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, D. Kiper, W. von der Behrens
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.
227-1033-00L	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.
227-0393-10L	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	The course requires an open attitude to the interdisciplinary approach of bioelectronics. In addition, it requires undergraduate entry-level familiarity with electric & magnetic fields/forces, resistors, capacitors, electric circuits, differential equations, calculus, probability calculus, Fourier transformation & frequency domain, lenses / light propagation / refractive index, Michaelis-Menten equation, pressure, diffusion AND basic knowledge of biology and chemistry (e.g. understanding the concepts of concentration, valence, reactants-products, etc.).

Wahlfächer
Nummer	Titel	Typ	ECTS	Umfang	Dozierende
401-0151-00L	Lineare Algebra	W	5 KP	3V + 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	C. Czichowsky
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 basic and advanced 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	L. Böttcher
Kurzbeschreibung	Diese Vorlesung bietet eine Einführung in Computersimulationsmethoden für physikalische Probleme und deren Implementierung auf PCs und Supercomputern. Die betrachteten Themen beinhalten: klassische Bewegungsgleichungen, partielle Differentialgleichungen (Wellengleichung, Diffussionsgleichung, Maxwell-Gleichungen), Monte-Carlo Simulationen, Perkolation, Phasenübergänge und komplexe Netzwerke.
Lernziel	Studenten lernen die folgenden Methoden anzuwenden: Prinzipien zur Erstellung von Zufallszahlen, Berechnung von kritischen Exponenten am Beispiel von Perkolation, Numerische Lösung von Problemen aus der klassichen Mechanik und Elektrodynamik, Kanonische Monte-Carlo Simulationen zur numerischen Betrachtung von magnetischen Systemen. Studenten lernen auch die Verwendung verschiedener Programmiersprachen und Bibliotheken zur Lösung physikalischer Probleme kennen. Zusätzlich lernen Studenten verschiedene numerische Verfahren zu unterscheiden und gezielt zur Lösung eines gegebenen physikalischen Problems einzusetzen.
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 ein Überblick über vorhandene Softwarebibliotheken für numerische Simulationen geboten.
Skript	Skript und Folien sind online verfügbar und werden bei Bedarf verteilt.
Literatur	Literaturempfehlungen und Referenzen sind im Skript enthalten.
Voraussetzungen / Besonderes	Vorlesung und Übung in Englisch, Prüfung 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, M. Willinger
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)
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
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. Recently, ellipsometry has been introduced to on-line monitor film thickness, and roughness with sub-nanometer precision. These characterisation techniques are vital for optimising the preparation of medical implants. 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. X rays are more and more often used to characterise the human tissues down to the nanometer level. The combination of highly intense beams only some micrometers in diameter with scanning enables spatially resolved measurements and the determination of tissue's anisotropies of biopsies.

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