6 citations · 6 across the 4 of their papers we have counts for
4 papers
YANA: Bridging the Neuromorphic Simulation-to-Hardware Gap
Brian Pachideh, Sven Nitzsche, Moritz Neher +5
Spiking Neural Networks (SNNs) promise significant advantages over conventional Artificial Neural Networks (ANNs) for applications requiring real-time processing of temporally spar…
DendroNN: Dendrocentric Neural Networks for Energy-Efficient Classification of Event-Based Data
Jann Krausse, Zhe Su, Kyrus Mama +4
Spatiotemporal information is at the core of diverse sensory processing and computational tasks. Feed-forward spiking neural networks can be used to solve these tasks while offerin…
Realtime-Capable Hybrid Spiking Neural Networks for Neural Decoding of Cortical Activity
Jann Krausse, Alexandru Vasilache, Klaus Knobloch +1
Intra-cortical brain-machine interfaces (iBMIs) present a promising solution to restoring and decoding brain activity lost due to injury. However, patients with such neuroprostheti…
Hybrid Spiking Neural Networks for Low-Power Intra-Cortical Brain-Machine Interfaces
Alexandru Vasilache, Jann Krausse, Klaus Knobloch +1
Intra-cortical brain-machine interfaces (iBMIs) have the potential to dramatically improve the lives of people with paraplegia by restoring their ability to perform daily activitie…