Events

The MSNE program is organizing events and invited presentations open to students, staff, and members of the public. Members of the Elite Network of Bavaria are especially invited to this events. Attendance is free. Please register at the MSNE Events Mailinglist in order to receive upcoming event announcements by email. To register, please send an empty email to msne-events-join@lists.lrz.de

Upcoming Events

Archived Events

Dr. Caroline Ling Li - Investigating into methods for multidimensional biomedical data analysis (13 January 2017)

ENB Elite Master Program Neuroengineering (MSNE)
Invited Presentation

Abstract
This talk focuses on how the signal processing and data analysis methodologies can be applied into the biomedical signal analysis in the data fusion framework. In our analysis, brain activities are measured through Electroencephalography (EEG) signals which were obtained from intensive care unit in hospital. In order to identify the brain consciousness states of those patients, various signal processing and predictive analytics methods were used. In this talk, some advanced methods are presented which overcomes the weakness of traditional methods in both time and frequency domains for EEG analysis. Moreover, an online signal nature tracking method based on collaborative adaptive filter is also presented in order to monitoring the brain states in real time. We then discuss how these methodologies can be further extended as a general framework for studying human biological functions and performances, ranging from hand gesture recognition to sport sciences.

Biography
Dr. Caroline Ling Li has been a Lecturer in the school of Computing at the University of Kent since 2011. She is also the founding coordinator of Laboratory of Brain | Cognition | Computing (BC2 Lab) of the school responsible for coordinating multidisciplinary research between Computing, Sports and local NHS. In 2015, she organized the BIH’15 conference as the local chair, which is a gathering event for three of the biggest brain initiatives (Speakers include Prof Allan Jones, Prof Karlheinz Meier, Prof David Van Essen). Before she joined the University of Kent, she had six-year research experience at Imperial College London in signal processing with a focus of analyzing body sensor data (EEG, EMG, ECG, eAR-sensor, and etc.). She started her research study within the Department of Electrical and Electronic Engineering at Imperial and then worked as a research associate in the £6 million EPSRC “ESPRIT with Pervasive Sensing” project at the Department of Computing of Imperial College. She has been focused on developing advanced signal processing methods for understanding sensor data with biomedical applications such as EEG-based biomarker for brain diseases, EMG-controlled robotics, ECG pattern extraction, and human motion analysis.

Time and Venue

Friday, January 13th 2017, 14h15 , Karlstr. 45, 80333 Munich, Room 1025
Talk is hosted by the Professorship for Neuroscientific System Theory (Prof. Conradt).

Invitation Flyer

Dr. Michael Pfeiffer - Deep Spiking Neural Networks – Low-Latency, Low-Compute Classifiers for Neuromorphic Platforms (20 December 2016)

ENB Elite Master Program Neuroengineering (MSNE)
Invited Presentation

Abstract
Spiking neural networks (SNNs) originate from computational neuroscience, but in recent years there has been growing interest in using brain-inspired event-based  computing for real-time pattern recognition. In my talk I will present novel approaches that merge ideas from SNNs and Deep Learning, the currently most successful machine learning paradigm for computer vision, speech recognition, and many real-world applications. Deep SNNs are particularly attractive for implementation in neuromorphic hardware platforms, which emulate the operation of spiking neurons in hardware, and achieve significant savings in power and latency over conventional models. New algorithmic insights allow us to reach accuracy levels that match traditional networks, while exploiting the advantages of SNNs. Deep SNNs exhibit a performance-latency tradeoff, which allows them to produce good first guesses very quickly, even before all neurons in the network are updated. I will show recent results that demonstrate how latency and computing costs in Deep SNNs can be reduced significantly, making them attractive models for fast and power-efficient information processing on power-constrained systems.

Biography
Dr. Michael Pfeiffer is a research engineer at Robert Bosch Corporate Research, where he investigates Cognitive Systems and Deep Learning. In 2010 he obtained his PhD in mathematics and computer science from Graz University of Technology, Austria, investigating machine learning methods as tools to understand computations in nervous systems. He then joined the Institute of Neuroinformatics at the University of Zurich and ETH Zurich as a postdoc, working on theories of neural computation and learning and neuromorphic computing. In 2012 he became group leader and program coordinator of the MSc in Neural Systems and Computation, an interdisciplinary specialized masters program combining systems neuroscience, theoretical neuroscience, neurotechnologies, and neuromorphic engineering. He has made substantial contributions towards understanding synaptic plasticity models such as STDP in the framework of machine learning. His work on deep and spiking neural networks has been influential for transferring recent breakthroughs from machine learning onto novel neuromorphic computing platforms

Time and Venue

Tuesday, December 20th 2016, 11h30 , Karlstr. 45, 80333 Munich, Room 2026
Talk is hosted by the Professorship for Neuroscientific System Theory (Prof. Conradt).

Invitation Flyer

Prof. Maarten De Vos - Towards mobile and wearable brain monitoring
(25 November 2016)

ENB Elite Master Program Neuroengineering (MSNE)
Invited Presentation

Abstract

All non-invasive technologies for the study of human brain activity suffer from the requirement that only artificial, movement-constrained behavior is allowed. However, by reducing “normal” behavior to a min-imum the ecological validity of the results can be limited. To overcome these limitations, we developed a truly mobile EEG system suitable for field recordings and natural situations which allows to decode single-trial brain responses in outdoor situations. We also demonstrated that signal quality of the mobile EEG system is equivalent to that of a standard lab amplifier in a traditional BCI experiment. Besides mobility and robustness with respect to motion, the critical issue before introducing EEG routinely in large studies became the electrode, as ideal EEG electrode would allow high quality and concealed recordings that can be conveniently attached to the head. We will demonstrate that a newly introduced cEEGrid electrode concept fulfills all these requirements and allows to monitor auditory attention relia-bly over long amounts of time.

Biography

Maarten De Vos is Associate Professor at the IBME, in the University of Oxford, following a Junior Professorship at the University of Oldenburg, Germany. He obtained his Ph.D. in electrical engineering from KU Leuven, Belgium, focusing on tensor-based decomposition methods. His academic work focuses on innovative biomedical monitoring and signal analysis, in particular the derivation of biosignatures of patient health from data acquired via wearable sensors and the incorporation of smart analytics into unobtrusive systems. He pioneering research in the field of mobile real-life brain-monitoring, which was awarded with several innovation prizes. His work on neonatal brain monitoring also achieved impact in patient care through the Neo-guard implementation project. After successful completion of the Biodesign faculty training at Stanford University, he started the Oxford Biodesign program.

Time and Venue

Friday, November 25th 2016, 15h00 at Karlstr. 45, 80333 Munich, Room 1025
Talk is hosted by the Professorship for Neuroscientific System Theory (Prof. Conradt).

Invitation Flyer (PDF)