Search result: Catalogue data in Spring Semester 2015
Biology Master | ||||||
Elective Major Subject Areas | ||||||
Elective Major: Neurosciences | ||||||
Elective Compulsory Master Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
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227-1034-00L | Computational Vision | W | 6 credits | 2V + 1U | D. Kiper, K. A. Martin | |
Abstract | 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. | |||||
Learning objective | 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. | |||||
Content | 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. | |||||
Literature | 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. | |||||
227-1038-00L | Neurophysics | W | 6 credits | 2V + 1U | R. Hahnloser, J.‑P. Pfister | |
Abstract | The focus of this class is the neural code and its relation to behavior, ranging from birdsong to vision. We study neural encoding and decoding problems and theories of learning with emphasis on reinforcement learning, estimation theory of dynamical systems, sparse coding, and generalized linear models. This course is co-taught by Prof. Richard Hahnloser and Prof. Jean-Pascal Pfister | |||||
Learning objective | This class is an introduction to systems neuroscience research for students with a background in quantitative sciences such as physics, mathematics, and engineering sciences. Students who take this course learn about neurophysiology and state-of-art algorithms for typical estimation problems in neuroscience. 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 of the micro-structure of motor learning. | |||||
Content | - Reinforcement learning, experiments (classical conditioning, birdsong) - Reinforcement learning, theory (Sutton Barto, Kalman filter models, Particle filters) - Nernst equation, Action potential - Electrophysiology, Dopamine cells, place & grid cells, Time cells in birds, spike trains - Learning with Generalized linear models: link to Spike-Timing Dependent Plasticity and Hebbian learning - Introduction to statistical models of vision. Generative models, inference and learning. - Dimensionality reduction, PCA and its neuronal implementation - Sparse coding (Olsehausen and Field) - Slow Feature Analysis. Link to place cells. | |||||
Lecture notes | Original research articles will be distributed, and some lecture notes will be made available. | |||||
Literature | - Original research articles, to be selected. - 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. - Neuronal Dynamics - From single neurons to networks and models of Cognition by Gerstner, Kistler, Naud and Paninski. - Reinforcement Learning by Sutton and Barto - Natural Image Statistics: A probabilistic approach to early computational vision by Hyvarinen, Hurri and Hoyer. - Bayesian Reasoning and Machine Learning by Barber. | |||||
Prerequisites / Notice | Knowledge of standard methods in analysis, algebra and probability theory are highly desirable but not necessary. Students should have programming experience. | |||||
376-1414-00L | Current Topics in Brain Research | W | 1 credit | 1.5K | M. E. Schwab, F. Helmchen, I. Mansuy, O. L. D. Raineteau | |
Abstract | Different national and international scientific guests are invited to present and discuss their actual scientific results. | |||||
Learning objective | To exchange scientific knowledge and data and to promote communication and collaborations among researchers. Students taking the course participate at all seminars within 1 semester and write a critical report on 1 seminar. Prof. Martin Schwab/ Dr. Cecilia Nicoletti will send instructions for this report to students who have registered for the course. | |||||
Content | Different scientific guests working in the field of molecular cognition, neurochemistry, neuromorphology and neurophysiology present their latest scientific results. | |||||
Lecture notes | no handout | |||||
Literature | no literature | |||||
227-1046-00L | Computer Simulations of Sensory Systems | W | 3 credits | 2V + 1U | T. Haslwanter | |
Abstract | This course deals with computer simulations of the human auditory, visual, and balance system. The lecture will cover the physiological and mechanical mechanisms of these sensory systems. And in the exercises, the simulations will be implemented with Python (or Matlab). The simulations will be such that their output could be used as input for actual neuro-sensory prostheses. | |||||
Learning objective | Our sensory systems provide us with information about what is happening in the world surrounding us. Thereby they transform incoming mechanical, electromagnetic, and chemical signals into “action potentials”, the language of the central nervous system. The main goal of this lecture is to describe how our sensors achieve these transformations, how they can be reproduced with computational tools. For example, our auditory system performs approximately a “Fourier transformation” of the incoming sound waves; our early visual system is optimized for finding edges in images that are projected onto our retina; and our balance system can be well described with a “control system” that transforms linear and rotational movements into nerve impulses. In the exercises that go with this lecture, we will use Python to reproduce the transformations achieved by our sensory systems. The goal is to write programs whose output could be used as input for actual neurosensory prostheses: such prostheses have become commonplace for the auditory system, and are under development for the visual and the balance system. For the corresponding exercises, at least some basic programing experience is required. | |||||
Content | The following topics will be covered: • Introduction into the signal processing in nerve cells. • Introduction into Python. • Simplified simulation of nerve cells (Hodgkins-Huxley model). • Description of the auditory system, including the application of Fourier transforms on recorded sounds. • Description of the visual system, including the retina and the information processing in the visual cortex. The corresponding exercises will provide an introduction to digital image processing. • Description of the mechanics of our balance system, and the “Control System”-language that can be used for an efficient description of the corresponding signal processing (essentially Laplace transforms and control systems). | |||||
Lecture notes | For each module additional material will be provided on the e-learning platform "moodle". The main content of the lecture is also available as a wikibook, under http://en.wikibooks.org/wiki/Sensory_Systems | |||||
Literature | Open source information is available as wikibook http://en.wikibooks.org/wiki/Sensory_Systems For good overviews I recommend: • L. R. Squire, D. Berg, F. E. Bloom, Lac S. du, A. Ghosh, and N. C. Spitzer. Fundamental Neuroscience, Academic Press - Elsevier, 2012 [ISBN: 9780123858702]. This book covers the biological components, from the functioning of an individual ion channels through the various senses, all the way to consciousness. And while it does not cover the computational aspects, it nevertheless provides an excellent overview of the underlying neural processes of sensory systems. • Principles of Neural Science (5th Ed, 2012), by Eric Kandel, James Schwartz, Thomas Jessell, Steven Siegelbaum, A.J. Hudspeth ISBN 0071390111 / 9780071390118 The standard textbook on neuroscience. • P Wallisch, M Lusignan, M. Benayoun, T. I. Baker, A. S. Dickey, and N. G. Hatsopoulos. MATLAB for Neuroscientists, Academic Press, 2009. Compactly written, it provides a short introduction to MATLAB, as well as a very good overview of MATLAB’s functionality, focusing on applications in different areas of neuroscience. • G. Mather. Foundations of Sensation and Perception, 2nd Ed Psychology Press, 2009 [ISBN: 978-1-84169-698-0 (hardcover), oder 978-1-84169-699-7 (paperback)] A coherent, up-to-date introduction to the basic facts and theories concerning human sensory perception. | |||||
Prerequisites / Notice | Since I have to gravel from Linz, Austria, to Zurich to give this lecture, I plan to hold this lecture in blocks (every 2nd week). | |||||
376-1428-00L | Comparative Behavioural Neuroscience | W | 4 credits | 2V | C. R. Pryce | |
Abstract | Brain function and emotional and cognitive behaviour in rodents, monkeys and humans. Similarities and differences in study methods used between species. Translation of evidence between species. From adaptive functioning to neuropsychiatric disorders. | |||||
Learning objective | Introduction to the integration of experimental psychology, neuroscience and psychiatry, to gain insight into how the mammalian brain regulates behaviour, and how animal evidence can be meaningfully translated to understand neuropsychiatric disorders and their treatment. | |||||
Content | Learning and Memory; Emotional and Cognitive Processing of the environment; Neuropsychiatry and Animal models; Psychopharmacology (target to therapy) | |||||
Lecture notes | Will be available via Moodle during the course. | |||||
Literature | Required reading will be communicated during the course. Students will review and discuss key papers as part of the course. Recommended texts: Nestler EJ, Hyman SE, Malenka RC (2009) Molecular Neuropharmacology: a foundation for clinical neuroscience. New York: McGraw Hill. Bouton ME (2007) Learning and Behavior: a contemporary synthesis. Sinauer Associates: Sunderland MA. | |||||
227-0390-00L | Elements of Microscopy | W | 4 credits | 3G | M. Stampanoni, G. Csúcs, R. A. Wepf | |
Abstract | The lecture reviews the basics of microscopy by discussing wave propagation, diffraction phenomena and aberrations. It gives the basics of light microscopy, introducing fluorescence, wide-field, confocal and multiphoton imaging. It further covers 3D electron microscopy and 3D X-ray tomographic micro and nanoimaging. | |||||
Learning objective | Solid introduction to the basics of microscopy, either with visible light, electrons or X-rays. | |||||
Content | It would be impossible to imagine any scientific activities without the help of microscopy. Nowadays, scientists can count on very powerful instruments that allow investigating sample down to the atomic level. The lecture includes a general introduction to the principles of microscopy, from wave physics to image formation. It provides the physical and engineering basics to understand visible light, electron and X-ray microscopy. During selected exercises in the lab, several sophisticated instrument will be explained and their capabilities demonstrated. | |||||
Literature | Available Online. | |||||
376-1306-00L | Clinical Neuroscience | W | 3 credits | 3G | M. E. Schwab, University lecturers | |
Abstract | The lecture series "Clinical Neuroscience" presents a comprehensive, condensed overview of the most important neurological diseases, their clinical presentation, diagnosis, therapy options and possible causes. Patient demonstrations (Übungen) follow every lecture that is dedicated to a particular disease. | |||||
Learning objective | By the end of this module students should be able to: - demonstrate their understanding and deep knowledge concerning the main neurological diseases -dentify and explain the different clinical presentation of these diseases, the methodology of diagnosis and the current therapies available - summarise and critically review scientific literature efficiently and effectively |
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