Spiking neural networks: Applications to computing, algorithmics, and robotics
3rd HBP Curriculum Workshop Series - ICT for non-specialists
18 September 2019 | Technical University of Munich, Germany
FAQs & ALL YOU NEED TO KNOW
You have to expect the following expenses:
- Registration fee (€ 90.00)
- Breakfast & dinner
What we cover:
- Lunch & coffee breaks
- Up to 5 fee waivers
Find further information on the application process and support opportunities on the workshop overview site.
In order to get to TUM Munich – Garching Campus you have many possibilities.
If you are coming from Munich Central Station:
- Subway (U-Bahn)
You can take the U-Bahn U1 line in the direction of Mangfallplatz or U2 line in the direction Messestadt Ost or U7 line in the direction of Neuperlach Zentrum until next stop, Sendlinger Tor. From here take the U-Bahn line U6 in the direction of Garching-Forschungszentrum, end station.
Alternatively, every S-Bahn in the direction of Marienplatz or Ostbahnhof, and respectively only until Marienplatz (approx. 3 minutes). As of Marienplatz U-Bahn U6 line until Garching Forschungszentrum (approx. 30 minutes).
You can take the S-Bahn S8 until you pass Ismaning. From here take Bus Nr. 230 until you stop at Garching Forschungszentrum. Alternatively: S1 line until Neufahrn, then with the Bus 690, which stops also at Garching Forschungszentrum.
You can take the Buses Nr. 230 292 or 690 to Garching Forschungszentrum (Bus Nr. 230 only workdays in 40 minute intervals.) Buses wait up to 8 minutes for belated S-Bahn's.
If you are coming from Munich Airport:
- You can take the S-Bahn S8 line in the direction of Munich Central Station until the third stop, Ismaning (approx. 13 minutes). From here Bus Nr. 230 until Garching Forschungszentrum
Alternatively: S1 line until Neufahrn, then with the Bus 690.
We strongly recommend that you invest in travel insurance and inform yourself about treatment and reimbursement conditions before travelling to Munich, Germany. If you are a European citizen, you should bring your European Health Insurance Card, which is accepted in combination with an ID.
As health care and social security systems vary between EU countries, please check the details on unforeseen medical treatment abroad.
Please note that you might have to contact your health insurer for authorisation before being treated at the hospital.
Munich has continental climate. Summer is warm with average high temperatures of 23°C (73°F).
EDUROAM is available at Technical University of Munich.
The official language in Germany is German. Foreign languages, particularly English, are widely understood and spoken.
- Time zone:
Munich is in the Central European Time Zone (CET = GMT / UCT + 1).
- Electric current:
Electricity is supplied at 230 volts (alternating current). Type F plugs (CEE 7/4, CEE 7/7) are used.
Please note that the information provided on this site has been obtained from several different sources and therefore the organisers cannot accept any responsibility for errors therein.
Spiking neural networks (SNN) are a special class of artificial neural networks (ANN), in which the information is transmitted by means of pulses (or spikes) rather than by firing rates. As SNNs have shown to be excellent control systems for biological organisms, they have the potential to produce good control systems for autonomous robots. This workshop aims to bring together neuroscientists with roboticists and computational researchers developing biologically-inspired learning algorithms for scientific and industrial applications.
In order to enrich the discussions of SNN and its applications to computing, algorithmics and robotics, we will have a hands-on session on spiking neural networks on the Neurorobotics Platform.
The programme will combine lectures in the morning and hands-on sessions in the afternoon. The sessions will finish around 4 p.m. to allow participants to also attend the IEEE International Conference on Cyborg and Bionic Systems.
Extended application deadline: 4 September 2019
Download the event announcement: Workshop announcement - Spiking neural networks: Applications to computing, algorithmics, and robotics (860.1 KB)
PRELIMINARY SCIENTIFIC PROGRAMME
The basics of spiking neurons: Biological facts, models and computational properties | 30 min
Fabrice Morin | Technical University of Munich, Germany
Lessons from the brain for enhancing computing and learning capabilities of spiking neural networks | 40 min
Wolfgang Maass | University of Technology Graz, Austria
Application of neuromorphic control principles towards closed loop
SNN-based sensomotoric robot controls | 40 min
Rüdiger Dillmann | Karlsruhe Institute of Technology, Germany
Neuromorphic hardware for real-time real-world robots | 40 min
Jörg Conradt | KTH Royal Institute of Technology, Sweden
Hands-on session: Spiking neural networks on the Neurorobotics Platform | 120 min
Open lecture - prelude to CBS2019: Bionic exo-skeletons and exo-muscles for movement rehabilitation | 40 min
Robert Riener | ETH Zürich, Switzerland
This programme may be subject to change.
SPEAKERS AND ABSTRACTS
Jörg Conradt is an Associate Professor at Computational Science and Technology, KTH, Stockholm. Understanding and applying the computational principles behind how brains turn perception into behavior is one of the most challenging research questions for the upcoming decades. His research investigates theory, models, and implementations of distributed neuronal information processing, to (a) discover key principles by which large networks of neurons operate and to (b) implement those in engineered systems to enhance their real-world and real-time performance. He holds an M.S. degree in Computer Science/Robotics from the University of Southern California, a Diploma in Computer Engineering from TU Berlin, and a Ph.D. in Physics/Neuroscience from ETH Zurich.
Lecture title: Neuromorphic hardware for real-time real-world robots
Spiking Neuronal Networks (SNN) offer a powerful promise to control intricate future robotic systems. Such SNNs need to be executed on reasonable power budgets in real time for any real-world robotic application. The more challenging and complex sensory inputs and control settings become, the more questionable is the execution of (spiking) neuronal networks in real time. Neuromorphic hardware, such as Manchester’s SpiNNaker, IBM’s TrueNorth, or Intel’s Loihi, offer efficient execution of SNNs, but provide various challenges and limitations in software, interfacing, and up-scaling. This talk will highlight several available neuromorphic computing platforms, and present benefits of connecting such to real-time real-world robot scenarios.
No picture, bio and abstract provided yet.
Wolfgang Maass holds a PhD in Mathematics from Ludwig Maximilians University Munich. He did research at MIT, the University of Chicago and the University of California at Berkeley as Heisenberg Fellow of the Deutsche Forschungsgemeinschaft. From 1982–1986, he was Associate Professor and from 1986-1993 Professor of Computer Science at the University of Illinois in Chicago and since 1991, he has been Professor of Computer Science at the Graz University of Technology in Austria. In 2002/3 and 2012 he also held a professorship at the Brain-Mind Institute at EPFL, Lausanne, Switzerland. Since 2005 he has been an Adjunct Fellow of the Frankfurt Institute of Advanced Studies (FIAS), from 2008 until 2012 he was Member of the Board of Governors of the International Neural Network Society and since 2013, Prof. Maass has been a Member of the Academia Europea. Also, in 2018 he was a co-organiser of the Special Semester "The Brain and Computation" at the Simons Institute, University of California at Berkeley.
Lecture title: Lessons from the brain for enhancing computing and learning capabilities of spiking neural networks
Wolfgang Maass will sketch recent progress in boosting the computing capability of spiking neural networks for temporal processing tasks. He will also sketch a recently discovered new learning method for recurrent networks of spiking neurons, called e-prop, which may explain how synaptic plasticity is organised in such networks in the brain. In addition, e-prop enables on-chip learning for neuromorphic chips. Both methods were the result of having a closer look at experimental data on spiking neural networks in the brain.
Details can be found in G. Bellec, D. Salaj, A. Subramoney, R. Legenstein, and W. Maass. Long short-term memory and learning-to-learn in networks of spiking neurons. 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montreal, Canada, 2018. https://igi-web.tugraz.at/PDF/243.pdf
and G. Bellec, F. Scherr, E. Hajek, D. Salaj, R. Legenstein, and W. Maass. A solution of the learning dilemma for recurrent networks of spiking neurons. Biorxiv, August 2019.
No picture, bio and abstract provided yet.
Alois Knoll | TUM
Sylvia Aßlaber | MUI
Judith Kathrein | MUI
Fabrice Morin | TUM
This face-to-face workshop is based on the content of the HBP Curriculum online lectures.
ABOUT THE VENUE
TECHNICAL UNIVERSITY OF MUNICH
The university was founded in 1868 to provide the state of Bavaria with a center of learning dedicated to the natural sciences. The university played a vital role in Bavaria's transition from an agricultural to an industrial state – and accelerated the pace of technological advancement across Europe.
Now the Technical University of Munich is one of Europe's top universities. It is committed to excellence in research and teaching, interdisciplinary education and the active promotion of promising young scientists. The university also forges strong links with companies and scientific institutions across the world. TUM was one of the first universities in Germany to be named a University of Excellence. Moreover, TUM regularly ranks among the best European universities in international rankings.