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: Natural gradient learning for spiking neurons
Elena Kreutzer, Mihai Alexandru Petrovici and Walter Senn
Due to their simplicity and success in machine learning, gradient-based learning rules represent a popular choice for synaptic plasticity models. While they have been linked to biological observations, it is often ignored that their predictions generally depend on a specific representation of the synaptic strength. In a neuron, the impact of a synapse can be described using the state of many different observables such as neutortransmitter release rates or membrane potential changes. Which one of these is chosen when deriving a learning rule can drastically change the predictions of the model.
This is doubly unsatisfactory, both with respect to optimality and from a conceptual point of view. By following the gradient on the manifold of the neuron’s firing distributions instead of one that is relative to some arbitrary synaptic weight parametrization, natural gradient descent provides a solution to both these problems. While the computational advantages of natural gradient are well-studied in ANNs, its predictive power as a model for in-vivo synaptic plasticity has not yet been assessed. By formulating natural gradient learning in the context of spiking interactions, we demonstrate how it can improve the convergence speed of spiking networks. Furthermore, our approach provides a unified, normative framework for both homo- and heterosynaptic plasticity in structured neurons and predicts a number of related biological phenomena.
Elena Kreutzer et al.
12:35‑12:45 (10+5 min)
Lightning talk: The Computational Capacity of Mem-LRC Reservoirs
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).