296 citations · 1.7k across the 65 of their papers we have counts for
6 papers · 1 filter
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…
Structural plasticity on an accelerated analog neuromorphic hardware system
Sebastian Billaudelle, Benjamin Cramer, Mihai A. Petrovici +4
In computational neuroscience, as well as in machine learning, neuromorphic devices promise an accelerated and scalable alternative to neural network simulations. Their neural conn…
Fast and energy-efficient neuromorphic deep learning with first-spike times
Julian Göltz, Laura Kriener, Andreas Baumbach +9
For a biological agent operating under environmental pressure, energy consumption and reaction times are of critical importance. Similarly, engineered systems are optimized for sho…
The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks
Benjamin Cramer, Yannik Stradmann, Johannes Schemmel +1
Spiking neural networks are the basis of versatile and power-efficient information processing in the brain. Although we currently lack a detailed understanding of how these network…
Brain-Inspired Hardware for Artificial Intelligence: Accelerated Learning in a Physical-Model Spiking Neural Network
Timo C. Wunderlich, Akos F. Kungl, Eric Müller +2
Future developments in artificial intelligence will profit from the existence of novel, non-traditional substrates for brain-inspired computing. Neuromorphic computers aim to provi…
Control of criticality and computation in spiking neuromorphic networks with plasticity
Benjamin Cramer, David Stöckel, Markus Kreft +4
The critical state is assumed to be optimal for any computation in recurrent neural networks, because criticality maximizes a number of abstract computational properties. We challe…