7 papers
SINRL: Socially Integrated Navigation with Reinforcement Learning using Spiking Neural Networks
Florian Tretter, Daniel Flögel, Alexandru Vasilache +3
Integrating autonomous mobile robots into human environments requires human-like decision-making and energy-efficient, event-based computation. Despite progress, neuromorphic metho…
Spiking Neural Networks for Low-Power Vibration-Based Predictive Maintenance
Alexandru Vasilache, Sven Nitzsche, Christian Kneidl +3
Advancements in Industrial Internet of Things (IIoT) sensors enable sophisticated Predictive Maintenance (PM) with high temporal resolution. For cost-efficient solutions, vibration…
Training Neural Networks by Optimizing Neuron Positions
Laura Erb, Tommaso Boccato, Alexandru Vasilache +2
The high computational complexity and increasing parameter counts of deep neural networks pose significant challenges for deployment in resource-constrained environments, such as e…
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…
A PyTorch-Compatible Spike Encoding Framework for Energy-Efficient Neuromorphic Applications
Alexandru Vasilache, Jona Scholz, Vincent Schilling +4
Spiking Neural Networks (SNNs) offer promising energy efficiency advantages, particularly when processing sparse spike trains. However, their incompatibility with traditional datas…
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…