Richard Hahnloser: Catalogue data in Spring Semester 2012

Name Prof. Dr. Richard Hahnloser
FieldNeuroinformatics
Address
Institut für Neuroinformatik
ETH Zürich, Y55 G 27
Winterthurerstrasse 190
8057 Zürich
SWITZERLAND
Telephone+41 44 635 30 51
E-mailhrichard@ethz.ch
DepartmentInformation Technology and Electrical Engineering
RelationshipFull Professor

NumberTitleECTSHoursLecturers
402-0820-01LNSC Master Short Project I Restricted registration - show details 8 credits16AR. Hahnloser
AbstractUsually a student selects the topic of a Master Short Project in consultation with his or her mentor.
Objective
402-0820-02LNSC Master Short Project II Restricted registration - show details 8 credits16AR. Hahnloser
AbstractUsually a student selects the topic of a Master Short Project in consultation with his or her mentor.
Objective
402-0823-00LNeurophysics6 credits2V + 1UR. Hahnloser
AbstractThe focus of this class is the neural code. The goal is to master computational solutions of the neural encoding and decoding problems. Students will develop and apply algorithms on spike data recorded in behaving zebra finches (birds).
ObjectiveThis course is an introduction to systems neuroscience research for students with a background in quantitative sciences such as physics, mathematics, or engineering sciences. Students who take this course learn about neurophysiology and state-of-art algorithms for analysis of high-resolution brain activity. Programming will be performed in Matlab (Mathworks Inc.).

We investigate how stimulus information is encoded in the spike trains of nerve cells by creating models that predict neural responses to sensory stimuli (encoding problem, sensory systems), as well as models that infer stimulus properties or behavioral features from neural data (decoding problem, motor systems).
ContentDecoding Problem: We have one or more spike trains and want to predict features of the motor behavior that caused by these spikes. In general, predicting the motor output from only a small number of spike trains is very difficult.

Encoding Problem: Based on a sensory stimulus we want to predict the spike response to it, i.e., we want to derive generative models for neural responses.

Content:
-Introduction to sensory (auditory) and motor coding in single neurons
- probability and estimation theory
- generative and advanced statistical models of brain function (principal component analysis, Hidden Markov Models)
- correlation and spectral analysis
- forward and inverse models (control theory)
- Hebbian learning and reinforcement learning
Lecture notesExtensive lecture notes will be made available. Original research articles will be distributed.
Literature- Theoretical Neuroscience by Peter Dayan and Larry Abbott.
- Biophysics of Computation by Chritoph Koch.
- Spikes: Exploring the neural code by Fred Rieke and David Warland et al.
- Spiking Neuron Models by Wulfram Gerstner and Werner Kistler.
- Original research articles, to be selected.
Prerequisites / NoticeKnowledge of standard methods in analysis, algebra and probability theory are highly desirable but not necessary. Students should have programming experience.

Former course title: "Theoretical Neuroscience"
402-0899-00LNeuroinformatics - Colloquia Information 0 credits1KR. J. Douglas, R. Hahnloser, D. Kiper, S.‑C. Liu, K. A. Martin
AbstractThe colloquium in Neuroinformatics is a series of lectures given by invited experts. The lecture topics reflect the current themes in neurobiology and neuromorphic engineering that are relevant for our Institute.
ObjectiveThe goal of these talks is to provide insight into recent research results. The talks are not meant for the general public, but really aimed at specialists in the field.
ContentThe topics depend heavily on the invited speakers, and thus change from week to week. All topics concern neural computation and their implementation in biological or artificial systems.
402-0900-01LNSC Master Thesis and Exam Restricted registration - show details
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.

Every thesis must be registered with us via the corresponding form before the thesis begins. Collect the registration form from the pigeon holes in front of the student administration offices HG G 33.1 and HG G 33.2.
Further information Link
45 credits90DR. Hahnloser
AbstractThe Master thesis concludes the study programme. Thesis work should prove the students' ability to independent, structured and scientific working.
Objective
Prerequisites / NoticeApplication forms can be downloaded at http://www.nsc.uzh.ch/?id=21602&master=10511&top=10532. Note: the oral part of the exam must be completed before the written part.
402-0900-02LNSC Master Thesis and Exam Restricted registration - show details
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.

Every thesis must be registered with us via the corresponding form before the thesis begins. Collect the registration form from the pigeon holes in front of the student administration offices HG G 33.1 and HG G 33.2.
Further information Link
29 credits58DR. Hahnloser
AbstractThe Master thesis concludes the study programme. Thesis work should prove the students' ability to independent, structured and scientific working.
Objective
Prerequisites / NoticeApplication forms can be downloaded at http://www.nsc.uzh.ch/?id=21602&master=10511&top=10532. Note: the oral part of the exam must be completed before the written part.