Search result: Catalogue data in Spring Semester 2021

Neural Systems and Computation Master Information
Core Courses
Compulsory Core Courses
NumberTitleTypeECTSHoursLecturers
227-1031-00LJournal Club (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: INI702

Mind the enrolment deadlines at UZH:
Link
O2 credits1SG. Indiveri
AbstractThe Neuroinformatics Journal club is a weekly meeting during which students present current research papers.
The presentation last from 30 to 60 Minutes and is followed by a general discussion.
ObjectiveThe Neuroinformatics Journal club aims to train students to present cutting-edge research clealry and efficiently. It leads students to learn about current topics in neurosciences and neuroinformatics, to search the relevant literature and to critically and scholarly appraise published papers. The students learn to present complex concepts and answer critical questions.
ContentRelevant current papers in neurosciences and neuroinformatics are covered.
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

Link
W0 credits1KS.‑C. Liu, R. Hahnloser, V. Mante
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.
Elective Core Courses
Systems Neurosciences
NumberTitleTypeECTSHoursLecturers
227-0395-00LNeural SystemsW6 credits2V + 1U + 1AR. Hahnloser, M. F. Yanik, B. Grewe
AbstractThis course introduces principles of information processing in neural systems. It covers basic neuroscience for engineering students, experiment techniques used in animal research and methods for inferring neural mechanisms. Students learn about neural information processing and basic principles of natural intelligence and their impact on artificially intelligent systems.
ObjectiveThis course introduces
- Basic neurophysiology and mathematical descriptions of neurons
- Methods for dissecting animal behavior
- Neural recordings in intact nervous systems and information decoding principles
- Methods for manipulating the state and activity in selective neuron types
- Neuromodulatory systems and their computational roles
- Reward circuits and reinforcement learning
- Imaging methods for reconstructing the synaptic networks among neurons
- Birdsong and language
- Neurobiological principles for machine learning.
ContentFrom 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.
Prerequisites / NoticeBefore taking this course, students are encouraged to complete "Bioelectronics and Biosensors" (227-0393-10L).

As part of the exercises for this class, students are expected to complete a programming or literature review project to be defined at the beginning of the semester.
227-1034-00LComputational 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:
Link
W6 credits2V + 1UD. Kiper
AbstractThis 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.
ObjectiveThis 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.
ContentThis 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.
LiteratureBooks: (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.
Neural Computation and Theoretical Neurosciences
NumberTitleTypeECTSHoursLecturers
227-0395-00LNeural SystemsW6 credits2V + 1U + 1AR. Hahnloser, M. F. Yanik, B. Grewe
AbstractThis course introduces principles of information processing in neural systems. It covers basic neuroscience for engineering students, experiment techniques used in animal research and methods for inferring neural mechanisms. Students learn about neural information processing and basic principles of natural intelligence and their impact on artificially intelligent systems.
ObjectiveThis course introduces
- Basic neurophysiology and mathematical descriptions of neurons
- Methods for dissecting animal behavior
- Neural recordings in intact nervous systems and information decoding principles
- Methods for manipulating the state and activity in selective neuron types
- Neuromodulatory systems and their computational roles
- Reward circuits and reinforcement learning
- Imaging methods for reconstructing the synaptic networks among neurons
- Birdsong and language
- Neurobiological principles for machine learning.
ContentFrom 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.
Prerequisites / NoticeBefore taking this course, students are encouraged to complete "Bioelectronics and Biosensors" (227-0393-10L).

As part of the exercises for this class, students are expected to complete a programming or literature review project to be defined at the beginning of the semester.
227-0973-00LTranslational NeuromodelingW8 credits3V + 2U + 1AK. Stephan
AbstractThis 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 teaches (hierarchical) Bayesian models of neuroimaging data and behaviour, incl. exercises.
ObjectiveTo 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.
ContentThis course provides a systematic introduction to Translational Neuromodeling (the development of mathematical models for inferring mechanisms of brain diseases from neuroimaging and behavioural data) 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, for example, dynamic causal models for inferring neuronal processes from neuroimaging data, and hierarchical Bayesian models for inference on cognitive processes 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.

Lecture topics include:
1. Introduction to Translational Neuromodeling and Computational Psychiatry/Psychosomatics
2. Psychiatric nosology
3. Pathophysiology of psychiatric disease mechanisms
4. Principles of Bayesian inference and generative modeling
5. Variational Bayes (VB)
6. Bayesian model selection
7. Markov Chain Monte Carlo techniques (MCMC)
8. Bayesian frameworks for understanding psychiatric and psychosomatic diseases
9. Generative models of fMRI data
10. Generative models of electrophysiological data
11. Generative models of behavioural data
12. Computational concepts of schizophrenia, depression and autism
13. Model-based predictions about individual patients

Practical exercises include mathematical derivations and the implementation of specific models and inference methods. In additional project work, students are required to use one of the examples discussed in the course as a basis for developing their own generative model and use it for simulations and/or inference in application to a clinical question. Group work (up to 3 students) is required.
LiteratureSee TNU website:
Link
Prerequisites / NoticeGood knowledge of principles of statistics, good programming skills (MATLAB, Julia, or Python)
252-1424-00LModels of ComputationW6 credits2V + 2U + 1AM. Cook
AbstractThis 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.
ObjectiveThe 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.
ContentThis 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.
Neurotechnologies and Neuromorphic Engineering
NumberTitleTypeECTSHoursLecturers
227-1032-00LNeuromorphic Engineering II Information
Information for UZH students:
Enrolment to this course unit only possible at ETH. No enrolment to module INI405 at UZH.

Please mind the ETH enrolment deadlines for UZH students: Link
W6 credits5GT. Delbrück, G. Indiveri, S.‑C. Liu
AbstractThis 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".
ObjectiveDesign of a neuromorphic circuit for implementation with CMOS technology.
ContentThis 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.
LiteratureS.-C. Liu et al.: Analog VLSI Circuits and Principles; software documentation.
Prerequisites / NoticePrerequisites: Neuromorphic Engineering I strongly recommended
227-1048-00LNeuromorphic Intelligence (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: INI508

Mind the enrolment deadlines at UZH:
Link
W6 credits2VG. Indiveri, E. Donati
AbstractIn this course we will study the computational properties of spiking neural networks implemented using analog "neuromorphic" electronic circuits. We will present network architectures and computational primitives that can use the dynamics of these circuits to exhibit intelligent behaviors. We will characterize these networks and validate them using full custom chips in laboratory experiments.
ObjectiveThe objective of this course is to introduce students to the field of “neuromorphic intelligence” with lectures on spiking neural network architectures implemented using mixed-signal silicon neuron and synapse circuits, and with laboratory sessions using neuromorphic chips to measure the computational properties of different spiking neural network architectures. Class projects will be proposed to validate the models presented in the lectures and carry out real-time signal processing and pattern recognition tasks on real-world sensory data.
ContentStudents will learn about the dynamical properties of adaptive integrate and fire neurons connected with each other via dynamic synapses. They will explore different neural circuits configured to implement computational primitives such as normalization, winner-take-all computation, selective amplification, and pattern discrimination. The experiments will consist of measuring the properties of real silicon neurons using full-custom neuromorphic processors, and configuring them to create neural architectures that can robustly process sensory signals and perform pattern discrimination despite, or thanks to, the limited resolution and large variability of their individual processing
element
Prerequisites / NoticeAccessible to NSC Master students.
It is recommended (but not mandatory) to have taken the Introduction to Neuroinformatics course (INI-401/227-1037-00).
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