# Search result: Catalogue data in Autumn Semester 2022

MAS in Medical Physics | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Specialisation in General Medical Physics | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Major in Neuroinformatics | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Core Courses | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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227-1037-00L | Introduction to Neuroinformatics | W | 6 credits | 2V + 1U + 1A | V. Mante, M. Cook, B. Grewe, G. Indiveri, D. Kiper, W. von der Behrens | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Abstract | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Objective | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Content | 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, simple neural architectures of feedforward and recurrent networks are discussed in the context of co-ordination, control, and integration of sensory and motor information. Connections to computer science and artificial intelligence are discussed, but the main focus of the course is on establishing the biological basis of computations in neurons. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

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

Abstract | The course introduces bioelectricity and the sensing concepts that enable obtaining information about neurons and their networks. The sources of electrical fields and currents in the context of biological systems are discussed. The fundamental concepts and challenges of measuring bioelectronic signals and the basic concepts to record optogenetically modified organisms are introduced. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Objective | During this course the students will: - learn the basic concepts in bioelectronics including the sources of bioelectronic signals and the methods to measure them - be able to solve typical problems in bioelectronics - learn about the remaining challenges in this field | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Content | Lecture topics: 1. Introduction Sources of bioelectronic signals 2. Membrane and Transport 3-4. Action potential and Hodgkin-Huxley Measuring bioelectronic signals 5. Detection and Noise 6. Measuring currents in solutions, nanopore sensing and patch clamp pipettes 7. Measuring potentials in solution and core conductance model 8. Measuring electronic signals with wearable electronics, ECG, EEG 9. Measuring mechanical signals with bioelectronics In vivo stimulation and recording 10. Functional electric stimulation 11. In vivo electrophysiology Optical recording and control of neurons (optogenetics) 12. Measuring neurons optically, fundamentals of optical microscopy 13. Fluorescent probes and scanning microscopy, optogenetics, in vivo microscopy 14. Measuring biochemical signals | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lecture notes | A detailed script is provided to each lecture including the exercises and their solutions. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

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

Prerequisites / Notice | 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, pressure, diffusion AND basic knowledge of biology and chemistry (e.g. understanding the concepts of concentration, valence, reactants-products, etc.). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Competencies |
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227-0421-00L | Deep Learning in Artificial and Biological Neuronal Networks | W | 4 credits | 3G | B. Grewe | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Abstract | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Objective | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Content | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Lecture notes | The lecture slides will be provided as a PDF after each lecture. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Prerequisites / Notice | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Practical Work | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

465-0800-00L | Practical Work Only for MAS in Medical Physics | O | 4 credits | external organisers | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Abstract | The practical work is designed to train the students in the solution of a specific problem and provides insights in the field of the selected MAS specialization. Tutors propose the subject of the project, the project plan, and the roadmap together with the student, as well as monitor the overall execution. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Objective | The practical work is aimed at training the student’s capability to apply and connect specific skills acquired during the MAS specialization program towards the solution of a focused problem. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Electives | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

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. Information for UZH students: Enrolment to this course unit only possible at ETH. No enrolment to module INI404 at UZH. Please mind the ETH enrolment deadlines for UZH students: Link | W | 6 credits | 2V + 3U | T. Delbrück, G. Indiveri, S.‑C. Liu | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Abstract | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

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

Content | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

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

Prerequisites / Notice | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

376-1791-00L | Introductory Course in Neuroscience I (University of Zurich) No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH as an incoming student. UZH Module Code: SPV0Y005 Mind the enrolment deadlines at UZH: Link | W | 2 credits | 2V | University lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Abstract | The course gives an introduction to human and comparative neuroanatomy, molecular, cellular and systems neuroscience. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Objective | The course gives an introduction to the development and anatomical structure of nervous systems. Furthermore, it discusses the basics of cellular neurophysiology and neuropharmacology. Finally, the nervous system is described on a system level. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Content | 1) Human Neuroanatomy I&II 2) Comparative Neuroanatomy 3) Building a central nervous system I,II 4) Synapses I,II 5) Glia and more 6) Excitability 7) Circuits underlying Emotion 8) Visual System 9) Auditory & Vestibular System 10) Somatosensory and Motor Systems 11) Learning in artificial and biological neural networks | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Prerequisites / Notice | For doctoral students of the Neuroscience Center Zurich (ZNZ). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

227-2037-00L | Physical Modelling and Simulation | W | 6 credits | 4G | J. Smajic | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Abstract | This module consists of (a) an introduction to fundamental equations of electromagnetics, mechanics and heat transfer, (b) a detailed overview of numerical methods for field simulations, and (c) practical examples solved in form of small projects. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Objective | Basic knowledge of the fundamental equations and effects of electromagnetics, mechanics, and heat transfer. Knowledge of the main concepts of numerical methods for physical modelling and simulation. Ability (a) to develop own simple field simulation programs, (b) to select an appropriate field solver for a given problem, (c) to perform field simulations, (d) to evaluate the obtained results, and (e) to interactively improve the models until sufficiently accurate results are obtained. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Content | The module begins with an introduction to the fundamental equations and effects of electromagnetics, mechanics, and heat transfer. After the introduction follows a detailed overview of the available numerical methods for solving electromagnetic, thermal and mechanical boundary value problems. This part of the course contains a general introduction into numerical methods, differential and integral forms, linear equation systems, Finite Difference Method (FDM), Boundary Element Method (BEM), Method of Moments (MoM), Multiple Multipole Program (MMP) and Finite Element Method (FEM). The theoretical part of the course finishes with a presentation of multiphysics simulations through several practical examples of HF-engineering such as coupled electromagnetic-mechanical and electromagnetic-thermal analysis of MEMS. In the second part of the course the students will work in small groups on practical simulation problems. For solving practical problems the students can develop and use own simulation programs or chose an appropriate commercial field solver for their specific problem. This practical simulation work of the students is supervised by the lecturers. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

227-1051-00L | Systems Neuroscience (University of Zurich) No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH as an incoming student. UZH Module Code: INI415 Mind the enrolment deadlines at UZH: Link | W | 6 credits | 2V + 1U | D. Kiper | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

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

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

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

Lecture notes | None | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

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

Prerequisites / Notice | none |

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