54 citations · 89 across the 16 of their papers we have counts for
5 papers · 1 filter
Real-time processing of analog signals on accelerated neuromorphic hardware
Yannik Stradmann, Johannes Schemmel, Mihai A. Petrovici +1
Sensory processing with neuromorphic systems is typically done by using either event-based sensors or translating input signals to spikes before presenting them to the neuromorphic…
The Role of Temporal Hierarchy in Spiking Neural Networks
Filippo Moro, Pau Vilimelis Aceituno, Laura Kriener +1
Spiking Neural Networks (SNNs) have the potential for rich spatio-temporal signal processing thanks to exploiting both spatial and temporal parameters. The temporal dynamics such a…
DelGrad: Exact event-based gradients for training delays and weights on spiking neuromorphic hardware
Julian Göltz, Jimmy Weber, Laura Kriener +5
Spiking neural networks (SNNs) inherently rely on the timing of signals for representing and processing information. Incorporating trainable transmission delays, alongside synaptic…
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…
An Accelerated Analog Neuromorphic Hardware System Emulating NMDA- and Calcium-Based Non-Linear Dendrites
Johannes Schemmel, Laura Kriener, Paul Müller +1
This paper presents an extension of the BrainScaleS accelerated analog neuromorphic hardware model. The scalable neuromorphic architecture is extended by the support for multi-comp…