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: Implementing Backpropagation for Learning on Neuromorphic Spiking Hardware
Andrew Sornborger, Alpha Renner, Forrest Sheldon, Anatoly Zlotnik and Louis Tao
Many contemporary advances in the theory and practice of neural networks are inspired by our understanding of how information is processed by natural neural systems. However, the basis of modern deep neural networks remains the error backpropagation algorithm [1], which though founded in rigorous mathematical optimization theory, has not been successfully demonstrated in a neurophysiologically realistic circuit. In a recent study, we proposed a neuromorphic architecture for learning that tunes the propagation of information forward and backwards through network layers using an endogenous timing mechanism controlled by thresholding of intensities [2]. This mechanism was demonstrated in simulation of analog currents, which represent the mean fields of spiking neuron populations. In this follow-on study, we present a modified architecture that includes several new mechanisms that enable
implementation of the backpropagation algorithm using neuromorphic spiking units. We demonstrate the function of this architecture in learning mapping examples, both in event-based simulation as well as a true hardware implementation.
[1] D.E. Rumelhart, G.E. Hinton, and R.J. Williams. Learning representations by back-propagating errors. Nature, pages 533–536, 1986.
[2] Andrew Sornborger, Louis Tao, Jordan Snyder, and Anatoly Zlotnik. A pulse-gated, neural implementation of the backpropagation algorithm. In Proceedings of the 7th Annual Neuro-inspired Computational Elements Workshop, page 10. ACM, 2019.
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).