Giacomo Indiveri: Catalogue data in Spring Semester 2021

Name	Prof. Dr. Giacomo Indiveri
Field	Neuromorphic Cognitive Systems
Address	Universität Zürich Winterthurerstr. 190 Neuroinformatik 8057 Zürich SWITZERLAND
Telephone	044 635 30 39
E-mail	giacomo@ethz.ch
URL	https://www.ini.uzh.ch/en/institute/people?uname=giacomo
Department	Information Technology and Electrical Engineering
Relationship	Full Professor

Number	Title	ECTS	Hours	Lecturers
227-0085-09L	Projects & Seminars: Spiking Neural Network on Neuromorphic Processors Does not take place this semester. Only for Electrical Engineering and Information Technology BSc. The course unit can only be taken once. Repeated enrollment in a later semester is not creditable.	3 credits	3P	G. Indiveri
Abstract	The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work.
Learning objective	Machine Learning – Spiking Neural Network – DVS Cameras - Programming Neuromoripch processors – Intel Loihi - Final Project with a presentation. Compared to the “traditional” artificial neural network, the spiking neural network (SNN) can provided both latency and energy efficiency. Moreover, SNN has demonstrated in previous works a better performance in processing physiological information of small sample size, and only the output layer of the spiking neural network needs to be trained, which results in a fast training rate. This couse focuses on giving the bases of spiking neural networks and neuromorphic processors. Students will learn the tools to implement SNN algorithm in both academic processors and Intel Loihi using data from Event-based Vision camera and biomedical sensors (i.e. ECG and EEG). The course will end with 4 weeks project where the students can target a specif application scenario. The course will be taught in English.
227-1031-00L	Journal 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: https://www.uzh.ch/cmsssl/en/studies/application/deadlines.html	2 credits	1S	G. Indiveri
Abstract	The 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.
Learning objective	The 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.
Content	Relevant current papers in neurosciences and neuroinformatics are covered.
227-1032-00L	Neuromorphic Engineering II 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	6 credits	5G	T. Delbrück, G. Indiveri, S.‑C. Liu
Abstract	This 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".
Learning objective	Design of a neuromorphic circuit for implementation with CMOS technology.
Content	This 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.
Literature	S.-C. Liu et al.: Analog VLSI Circuits and Principles; software documentation.
Prerequisites / Notice	Prerequisites: Neuromorphic Engineering I strongly recommended
227-1048-00L	Neuromorphic 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: https://www.uzh.ch/cmsssl/en/studies/application/deadlines.htm	6 credits	2V	G. Indiveri, E. Donati
Abstract	In 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.
Learning objective	The 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.
Content	Students 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 / Notice	Accessible to NSC Master students. It is recommended (but not mandatory) to have taken the Introduction to Neuroinformatics course (INI-401/227-1037-00).