189 citations · 733 across the 35 of their papers we have counts for
6 papers · 1 filter
Gradient-based methods for spiking physical systems
Julian Göltz, Sebastian Billaudelle, Laura Kriener +5
Recent efforts have fostered significant progress towards deep learning in spiking networks, both theoretical and in silico. Here, we discuss several different approaches, includin…
Event-based Backpropagation for Analog Neuromorphic Hardware
Christian Pehle, Luca Blessing, Elias Arnold +2
Neuromorphic computing aims to incorporate lessons from studying biological nervous systems in the design of computer architectures. While existing approaches have successfully imp…
Autocorrelations from emergent bistability in homeostatic spiking neural networks on neuromorphic hardware
Benjamin Cramer, Markus Kreft, Sebastian Billaudelle +9
A unique feature of neuromorphic computing is that memory is an implicit part of processing through traces of past information in the system's collective dynamics. The extent of me…
Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate
Sebastian Billaudelle, Yannik Stradmann, Korbinian Schreiber +22
We present first experimental results on the novel BrainScaleS-2 neuromorphic architecture based on an analog neuro-synaptic core and augmented by embedded microprocessors for comp…
Six networks on a universal neuromorphic computing substrate
Thomas Pfeil, Andreas Grübl, Sebastian Jeltsch +7
In this study, we present a highly configurable neuromorphic computing substrate and use it for emulating several types of neural networks. At the heart of this system lies a mixed…
A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems
Daniel Brüderle, Mihai A. Petrovici, Bernhard Vogginger +31
In this paper we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems.…