Richard Hahnloser: Katalogdaten im Frühjahrssemester 2016 |
Name | Herr Prof. Dr. Richard Hahnloser |
Lehrgebiet | Neuroinformatik |
Adresse | Institut für Neuroinformatik ETH Zürich, Y55 G 27 Winterthurerstrasse 190 8057 Zürich SWITZERLAND |
Telefon | +41 44 635 30 51 |
hrichard@ethz.ch | |
Departement | Informationstechnologie und Elektrotechnik |
Beziehung | Ordentlicher Professor |
Nummer | Titel | ECTS | Umfang | Dozierende | |
---|---|---|---|---|---|
227-1036-01L | NSC 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: Link | 8 KP | 17A | R. Hahnloser | |
Kurzbeschreibung | Usually a student selects the topic of a Master Short Project in consultation with his or her mentor. | ||||
Lernziel | see above | ||||
227-1036-02L | NSC 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: Link | 8 KP | 17A | R. Hahnloser | |
Kurzbeschreibung | Usually a student selects the topic of a Master Short Project in consultation with his or her mentor. | ||||
Lernziel | see above | ||||
227-1038-00L | Neurophysics | 6 KP | 2V + 1U | J.‑P. Pfister, R. Hahnloser | |
Kurzbeschreibung | The focus of this course is on statistical approaches in neuroscience. The emphasis of this course is on both the mathematical methods as well as their applications to the modelling and analysis of electrophysiological recordings. This course is taught by Prof. Jean-Pascal Pfister (2 lectures will be given by Prof. Richard Hahnloser) | ||||
Lernziel | This class is an introduction to computational neuroscience research for students with a strong background in quantitative sciences such as physics, mathematics, and engineering sciences. Students who take this course learn about mathematical methods that are widely applied in neuroscience. In particular, they will learn about graphical models, dynamical systems, stochastic dynamical systems as well as probabilistic filtering. Those methods will be applied in the context of single neuronal dynamics, synaptic plasticity, neural network dynamics. Part of the exercices will be performed in Matlab (Mathworks Inc.). | ||||
Inhalt | 1. Introduction to dynamical systems a. single neuron models (Fitzug-Nagumo model) b. synaptic plasticity (Hebbian learning, Oja's rule, BCM learning rule) 2. Graphical models a. Bayesian inference, cue combination tasks b. parameter learning (Expectation-Maximisation algorithm) 3. Stochastic dynamical systems (Fokker-Planck equation) 4. Probabilistic filtering a. Kushner equation b. Kalman-Bucy filter c. particle filter 5. Point emission processes (spiking neurons) a. Spiking network dynamics (Generalised Linear Model - GLM) b. Learning with the Generalised Linear Model, link to Spike-Timing dependent plasticity c. Reward-based learning | ||||
Skript | Original research articles will be distributed. Specific pointers to textbooks will be provided. | ||||
Literatur | Gerstner et al. (2014). Neuronal Dynamics - From single neurons to networks and models of Cognition Barber (2012). Bayesian Reasoning and Machine Learning Rieke et al. (1999) Spikes: Exploring the neural code Bain, A., & Crisan, D. (2009). Fundamentals of stochastic filtering (Vol. 3). | ||||
Voraussetzungen / Besonderes | Knowledge of standard methods in analysis, algebra and probability theory are highly desirable but not necessary. Students should have programming experience. | ||||
227-1041-01L | NSC Master 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: Link 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 KP | 96D | R. Hahnloser | |
Kurzbeschreibung | The Master thesis concludes the study programme. Thesis work should prove the students' ability to independent, structured and scientific working. | ||||
Lernziel | see above | ||||
Voraussetzungen / Besonderes | Application 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. | ||||
227-1041-02L | NSC Master 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: Link 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 KP | 62D | R. Hahnloser | |
Kurzbeschreibung | The Master thesis concludes the study programme. Thesis work should prove the students' ability to independent, structured and scientific working. | ||||
Lernziel | see above | ||||
Voraussetzungen / Besonderes | Application 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. | ||||
227-1043-00L | Neuroinformatics - Colloquia | 0 KP | 1K | S.‑C. Liu, R. Hahnloser, V. Mante, K. A. Martin | |
Kurzbeschreibung | The 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. | ||||
Lernziel | The 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. | ||||
Inhalt | The 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. |