Suchergebnis: Katalogdaten im Frühjahrssemester 2018

Nummer	Titel	Typ	ECTS	Umfang	Dozierende
Neural Systems and Computation Master
Kernfächer
Wählbare Kernfächer
Systemneurowissenschaften
227-0395-00L	Neural Systems	W	6 KP	2V + 1U + 1A	R. Hahnloser, M. F. Yanik, B. Grewe
Kurzbeschreibung	This course introduces principles of information processing in neural systems. It covers basic neuroscience on a level suitable for engineering students. The course introduces neuroscientific techniques used in studies of both animal behaviors and their underlying neural mechanisms. Students learn about neural signaling principles gained from experimental data.
Lernziel	This course introduces - Methods for monitoring of animal behaviors in complex environments - Information-theoretic principles of behavior - Methods for performing neurophysiological recordings in intact nervous systems - Methods for manipulating the state and activity in selective neuron types - Methods for reconstructing the synaptic networks among neurons - Information decoding from neural populations, and - Neurobiological principles for machine learning.
Inhalt	From active membranes to propagation of action potentials. From synaptic physiology to synaptic learning rules. From receptive fields to neural population decoding. From fluorescence imaging to connectomics. Methods for reading and manipulation neural ensembles. From classical conditioning to reinforcement learning. From the visual system to deep convolutional networks. Brain architectures for learning and memory. From birdsong to computational linguistics.
Voraussetzungen / Besonderes	Before taking this course, students are encouraged to complete "Bioelectronics and Biosensors" (227-0393-10L)
227-1034-00L	Computational Vision (University of Zurich) No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: INI402 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/mobilitaet.html	W	6 KP	2V + 1U	D. Kiper, K. A. Martin
Kurzbeschreibung	This course focuses on neural computations that underlie visual perception. We study how visual signals are processed in the retina, LGN and visual cortex. We study the morpholgy and functional architecture of cortical circuits responsible for pattern, motion, color, and three-dimensional vision.
Lernziel	This course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed. The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered.
Inhalt	This course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed. The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered.
Literatur	Books: (recommended references, not required) 1. An Introduction to Natural Computation, D. Ballard (Bradford Books, MIT Press) 1997. 2. The Handbook of Brain Theorie and Neural Networks, M. Arbib (editor), (MIT Press) 1995.

Theoretische und Computergestützte Neurowissenschaften
Nummer	Titel	Typ	ECTS	Umfang	Dozierende
227-0395-00L	Neural Systems	W	6 KP	2V + 1U + 1A	R. Hahnloser, M. F. Yanik, B. Grewe
Kurzbeschreibung	This course introduces principles of information processing in neural systems. It covers basic neuroscience on a level suitable for engineering students. The course introduces neuroscientific techniques used in studies of both animal behaviors and their underlying neural mechanisms. Students learn about neural signaling principles gained from experimental data.
Lernziel	This course introduces - Methods for monitoring of animal behaviors in complex environments - Information-theoretic principles of behavior - Methods for performing neurophysiological recordings in intact nervous systems - Methods for manipulating the state and activity in selective neuron types - Methods for reconstructing the synaptic networks among neurons - Information decoding from neural populations, and - Neurobiological principles for machine learning.
Inhalt	From active membranes to propagation of action potentials. From synaptic physiology to synaptic learning rules. From receptive fields to neural population decoding. From fluorescence imaging to connectomics. Methods for reading and manipulation neural ensembles. From classical conditioning to reinforcement learning. From the visual system to deep convolutional networks. Brain architectures for learning and memory. From birdsong to computational linguistics.
Voraussetzungen / Besonderes	Before taking this course, students are encouraged to complete "Bioelectronics and Biosensors" (227-0393-10L)
227-0973-00L	Translational Neuromodeling	W	6 KP	4G	K. Stephan
Kurzbeschreibung	This course provides a systematic introduction to Translational Neuromodeling (the development of mathematical models for diagnostics of brain diseases) and their application to concrete clinical questions (Computational Psychiatry/Psychosomatics). It focuses on a generative modeling strategy and discusses (hierarchical) Bayesian models of neuroimaging data and behaviour in detail.
Lernziel	To obtain an understanding of the goals, concepts and methods of Translational Neuromodeling and Computational Psychiatry/Psychosomatics, particularly with regard to Bayesian models of neuroimaging (fMRI, EEG) and behavioural data.
Inhalt	This course provides a systematic introduction to Translational Neuromodeling (the development of mathematical models for diagnostics of brain diseases) and their application to concrete clinical questions (Computational Psychiatry/Psychosomatics). The first part of the course will introduce disease concepts from Psychiatry and Psychosomatics, their history, and clinical priority problems. The second part of the course concerns computational modeling of neuronal and cognitive processes for clinical applications. A particular focus is on Bayesian methods and generative models, e.g. dynamic causal models (DCMs) for inferring neuronal mechanisms from neuroimaging data, and hierarchical Bayesian models for inference on cognitive mechanisms from behavioural data. The course discusses the mathematical and statistical principles behind these models, illustrates their application to various psychiatric diseases, and outlines a general research strategy based on generative models. In the practical exercises, students are asked to program their own generative model (in MATLAB) and use it for simulations and inference from real fMRI or behavioural data.
Literatur	See TNU website: https://www.tnu.ethz.ch/en/teaching.html
Voraussetzungen / Besonderes	Basic statistical knowledge, MATLAB programming skills
252-1424-00L	Models of Computation	W	6 KP	2V + 2U + 1A	M. Cook
Kurzbeschreibung	This course surveys many different models of computation: Turing Machines, Cellular Automata, Finite State Machines, Graph Automata, Circuits, Tilings, Lambda Calculus, Fractran, Chemical Reaction Networks, Hopfield Networks, String Rewriting Systems, Tag Systems, Diophantine Equations, Register Machines, Primitive Recursive Functions, and more.
Lernziel	The goal of this course is to become acquainted with a wide variety of models of computation, to understand how models help us to understand the modeled systems, and to be able to develop and analyze models appropriate for new systems.
Inhalt	This course surveys many different models of computation: Turing Machines, Cellular Automata, Finite State Machines, Graph Automata, Circuits, Tilings, Lambda Calculus, Fractran, Chemical Reaction Networks, Hopfield Networks, String Rewriting Systems, Tag Systems, Diophantine Equations, Register Machines, Primitive Recursive Functions, and more.

Neurotechnologie und Neuromorphe Ingenieurwissenschaften
Nummer	Titel	Typ	ECTS	Umfang	Dozierende
227-1032-00L	Neuromorphic Engineering II Information für UZH Studierende: Die Lerneinheit kann nur an der ETH belegt werden. Die Belegung des Moduls INI405 ist an der UZH nicht möglich. Beachten Sie die Einschreibungstermine an der ETH für UZH Studierende: Link	W	6 KP	5G	T. Delbrück, G. Indiveri, S.‑C. Liu
Kurzbeschreibung	This course teaches the basics of analog chip design and layout with an emphasis on neuromorphic circuits, which are introduced in the fall semester course "Neuromorphic Engineering I".
Lernziel	Design of a neuromorphic circuit for implementation with CMOS technology.
Inhalt	This course teaches the basics of analog chip design and layout with an emphasis on neuromorphic circuits, which are introduced in the autumn semester course "Neuromorphic Engineering I". The principles of CMOS processing technology are presented. Using a set of inexpensive software tools for simulation, layout and verification, suitable for neuromorphic circuits, participants learn to simulate circuits on the transistor level and to make their layouts on the mask level. Important issues in the layout of neuromorphic circuits will be explained and illustrated with examples. In the latter part of the semester students simulate and layout a neuromorphic chip. Schematics of basic building blocks will be provided. The layout will then be fabricated and will be tested by students during the following fall semester.
Literatur	S.-C. Liu et al.: Analog VLSI Circuits and Principles; software documentation.
Voraussetzungen / Besonderes	Prerequisites: Neuromorphic Engineering I strongly recommended

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