227-0085-09L  P&S: Spiking Neural Network on Neuromorphic Processors

SemesterAutumn Semester 2023
LecturersG. Indiveri
Periodicityevery semester recurring course
CourseDoes not take place this semester.
Language of instructionEnglish
CommentThe course unit can only be taken once. Repeated enrollment in a later semester is not creditable.


AbstractThe 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 objectiveMachine 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.