Pruning of deep spiking neural networks through gradient rewiring
Spiking Neural Networks (SNNs) have been attached great importance due to their
biological plausibility and high energy-efficiency on neuromorphic chips. As these chips are …
biological plausibility and high energy-efficiency on neuromorphic chips. As these chips are …
Including STDP to eligibility propagation in multi-layer recurrent spiking neural networks
W van der Veen - arXiv preprint arXiv:2201.07602, 2022 - arxiv.org
Spiking neural networks (SNNs) in neuromorphic systems are more energy efficient
compared to deep learning-based methods, but there is no clear competitive learning …
compared to deep learning-based methods, but there is no clear competitive learning …
Progressive Layer-based Compression for Convolutional Spiking Neural Network
H Elbez, M Fatahi - 2023 - hal.science
Spiking neural networks (SNNs) have attracted interest in recent years due to their low
energy consumption and the increasing need for more power in real-life machine learning …
energy consumption and the increasing need for more power in real-life machine learning …
Interactive analysis of spiking neural networks simulation traces
H Elbez - 2022 - theses.hal.science
Neuromorphic architectures are one of the most promising ways to significantly reduce the
energy consumption of tomorrow's computers. They are inspired by the functioning of the …
energy consumption of tomorrow's computers. They are inspired by the functioning of the …
Including STDP to eligibility propagation in multi-layer recurrent spiking neural networks
W Veen - 2021 - fse.studenttheses.ub.rug.nl
Spiking neural networks (SNNs) in neuromorphic systems are more energy efficient
compared to deep learning–based methods, but there is no clear competitive learning …
compared to deep learning–based methods, but there is no clear competitive learning …