2 citations · 3 across the 8 of their papers we have counts for
4 papers · 1 filter
Linearized Bregman Iterations for Sparse Spiking Neural Networks
Daniel Windhager, Bernhard A. Moser, Michael Lunglmayr
Spiking Neural Networks (SNNs) offer an energy efficient alternative to conventional Artificial Neural Networks (ANNs) but typically still require a large number of parameters. Thi…
Integrate-and-Fire from a Mathematical and Signal Processing Perspective
Bernhard A. Moser, Anna Werzi, Michael Lunglmayr
Integrate-and-Fire (IF) is an idealized model of the spike-triggering mechanism of a biological neuron. It is used to realize the bio-inspired event-based principle of information…
On the Sampling Sparsity of Neuromorphic Analog-to-Spike Conversion based on Leaky Integrate-and-Fire
Bernhard A. Moser, Michael Lunglmayr
In contrast to the traditional principle of periodic sensing neuromorphic engineering pursues a paradigm shift towards bio-inspired event-based sensing, where events are primarily…
SNN Architecture for Differential Time Encoding Using Decoupled Processing Time
Daniel Windhager, Bernhard A. Moser, Michael Lunglmayr
Spiking neural networks (SNNs) have gained attention in recent years due to their ability to handle sparse and event-based data better than regular artificial neural networks (ANNs…