Das Herbstsemester 2020 findet in einer gemischten Form aus Online- und Präsenzunterricht statt.
Bitte lesen Sie die publizierten Informationen zu den einzelnen Lehrveranstaltungen genau.

Richard Hahnloser: Katalogdaten im Herbstsemester 2018

NameHerr Prof. Dr. Richard Hahnloser
LehrgebietNeuroinformatik
Adresse
Institut für Neuroinformatik
ETH Zürich, Y55 G 27
Winterthurerstrasse 190
8057 Zürich
SWITZERLAND
Telefon+41 44 635 30 51
E-Mailhrichard@ethz.ch
DepartementInformationstechnologie und Elektrotechnik
BeziehungOrdentlicher Professor

NummerTitelECTSUmfangDozierende
227-1036-01LNSC Master Short Project I (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: INI505

Mind the enrolment deadlines at UZH:
https://www.uzh.ch/cmsssl/en/studies/application/mobilitaet.html
8 KP17AR. Hahnloser
KurzbeschreibungUsually a student selects the topic of a Master Short Project in consultation with his or her mentor.
Lernzielsee above
227-1036-02LNSC Master Short Project II (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: INI506

Mind the enrolment deadlines at UZH:
https://www.uzh.ch/cmsssl/en/studies/application/mobilitaet.html
8 KP17AR. Hahnloser
KurzbeschreibungUsually a student selects the topic of a Master Short Project in consultation with his or her mentor.
Lernzielsee above
227-1039-00LBasics of Instrumentation, Measurement, and Analysis (University of Zurich) Belegung eingeschränkt - Details anzeigen
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.
4 KP9SS.‑C. Liu, T. Delbrück, R. Hahnloser, G. Indiveri, V. Mante, P. Pyk, W. von der Behrens
KurzbeschreibungExperimental 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).
LernzielThe 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 / BesonderesFor 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.
227-1041-01LNSC Master's Theses (long) and Exam (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: INI503

Mind the enrolment deadlines at UZH:
https://www.uzh.ch/cmsssl/en/studies/application/mobilitaet.html

Only students who fulfil the following criteria are allowed to begin with their master thesis:
a. successful completion of the bachelor programme;
b. fulfilling of any additional requirements necessary to gain admission to the master programme.
45 KP96DR. Hahnloser
KurzbeschreibungThe Master thesis concludes the study programme. Thesis work should prove the students' ability to independent, structured and scientific working.
Lernzielsee above
227-1041-02LNSC Master's Thesis and Exam (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: INI504

Mind the enrolment deadlines at UZH:
https://www.uzh.ch/cmsssl/en/studies/application/mobilitaet.html

Only students who fulfil the following criteria are allowed to begin with their master thesis:
a. successful completion of the bachelor programme;
b. fulfilling of any additional requirements necessary to gain admission to the master programme.
29 KP62DR. Hahnloser
KurzbeschreibungThe Master thesis concludes the study programme. Thesis work should prove the students' ability to independent, structured and scientific working.
Lernzielsee above
227-1043-00LNeuroinformatics - 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
0 KP1KS.‑C. Liu, R. Hahnloser, V. Mante
KurzbeschreibungDas 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.
LernzielDie Vorträge informieren Studenten und Forscher über neueste Forschungsergebnisse. Dementsprechend sind die Vorträge primaer nicht fuer wissenschaftliche Laien, sondern für Forschungsspezialisten konzipiert.
InhaltDie 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.