6th March 2020: We are sorry to announce that NICE 2020, scheduled to be held on March 17-20 2020, will be postponed to a later date. Please see here for the new date in March 2021
NEUROTECH event: Future Application Directions for Neuromorphic Computing Technologies: agenda and registration (free, but mandatory). A half-day event with special focus on potential application of neuromorphic computing.
Travel info:
Getting to the venue:
the nearest tram stop to the meeting venue is "Heidelberg Bunsengymnasium" (marked in the map linked above) [online timetable]https://reiseauskunft.bahn.de//bin/query.exe/en?Z=Neuenheim+Bunsengymnasium,+Heidelberg), provided by German Railway. Here you can also buy tickets online
via Railway from the train station directly attached to the airport "Frankfurt Flughafen Fernbahnhof": online timetable by German Railway (tickets are also sold online via this website)
via airport shuttle service directly to the hotel. We have good experience with TLS Heidelberg. A single, shared ride costs about 40 Euro / person / ride
Hotels:
These hotels are relatively close to the meeting venue (Kirchhoff-Institute for Physics, see the map above). A lot more hotels are listed in online hotel booking sites (e.g. on booking.com)
Lightning talk: Fast and deep neuromorphic learning with first-spike coding
Julian Göltz, Andreas Baumbach, Sebastian Billaudelle, Oliver Breitwieser, Laura Kriener, Akos Ferenc Kungl, Karlheinz Meier, Johannes Schemmel and Mihai Alexandru Petrovici
For a biological agent operating under environmental pressure, energy consumption and reaction times are of critical importance.
Similarly, engineered systems also strive for short time-to-solution and low energy-to-solution characteristics. At the level of neuronal implementation, this implies achieving the desired results with
as few and as early spikes as possible. In the time-to-first-spike coding framework, both of these goals are inherently emerging features of learning. Here, we describe a rigorous derivation of error-backpropagation-based learning for hierarchical networks of leaky integrate-and-fire neurons. This narrows the gap between previous existing models of first-spike-time learning and biological neuronal dynamics, thereby also enabling fast and energy-efficient inference on analog neuromorphic devices that inherit these dynamics from their biological archetypes.
Julian Göltz et al.
15:25‑15:35 (10+5 min)
Lightning talk: Neuromorphic Graph Algorithms: Extracting Longest ShortestPaths and Minimum Spanning Trees
NICE 2020, Tutorials day: NOTE: NICE will be POSTPONED!
The tutorial day can be booked as one of the registration options. On the tutorial day hands-on interactive tutorials with several different neuromorphic compute systems will be offered:
Intel Loihi platform tutorial (Lecture style. To follow along from your own laptop your need to engage with Intel’s Intel’s Neuromorphic Research Community beforehand (email inrc_interest@intel.com for more information).