Towards spike-based machine intelligence with neuromorphic computing
Guided by brain-like 'spiking'computational frameworks, neuromorphic computing—brain-
inspired computing for machine intelligence—promises to realize artificial intelligence while …
inspired computing for machine intelligence—promises to realize artificial intelligence while …
Event-based vision: A survey
Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead
of capturing images at a fixed rate, they asynchronously measure per-pixel brightness …
of capturing images at a fixed rate, they asynchronously measure per-pixel brightness …
Training spiking neural networks using lessons from deep learning
The brain is the perfect place to look for inspiration to develop more efficient neural
networks. The inner workings of our synapses and neurons provide a glimpse at what the …
networks. The inner workings of our synapses and neurons provide a glimpse at what the …
Aegnn: Asynchronous event-based graph neural networks
S Schaefer, D Gehrig… - Proceedings of the IEEE …, 2022 - openaccess.thecvf.com
The best performing learning algorithms devised for event cameras work by first converting
events into dense representations that are then processed using standard CNNs. However …
events into dense representations that are then processed using standard CNNs. However …
[HTML][HTML] A survey of encoding techniques for signal processing in spiking neural networks
Biologically inspired spiking neural networks are increasingly popular in the field of artificial
intelligence due to their ability to solve complex problems while being power efficient. They …
intelligence due to their ability to solve complex problems while being power efficient. They …
Temporal-wise attention spiking neural networks for event streams classification
How to effectively and efficiently deal with spatio-temporal event streams, where the events
are generally sparse and non-uniform and have the us temporal resolution, is of great value …
are generally sparse and non-uniform and have the us temporal resolution, is of great value …
[HTML][HTML] Deep learning with spiking neurons: opportunities and challenges
M Pfeiffer, T Pfeil - Frontiers in neuroscience, 2018 - frontiersin.org
Spiking neural networks (SNNs) are inspired by information processing in biology, where
sparse and asynchronous binary signals are communicated and processed in a massively …
sparse and asynchronous binary signals are communicated and processed in a massively …
Rmp-snn: Residual membrane potential neuron for enabling deeper high-accuracy and low-latency spiking neural network
Abstract Spiking Neural Networks (SNNs) have recently attracted significant research
interest as the third generation of artificial neural networks that can enable low-power event …
interest as the third generation of artificial neural networks that can enable low-power event …
[HTML][HTML] Going deeper in spiking neural networks: VGG and residual architectures
Over the past few years, Spiking Neural Networks (SNNs) have become popular as a
possible pathway to enable low-power event-driven neuromorphic hardware. However, their …
possible pathway to enable low-power event-driven neuromorphic hardware. However, their …
Deep learning in spiking neural networks
A Tavanaei, M Ghodrati, SR Kheradpisheh… - Neural networks, 2019 - Elsevier
In recent years, deep learning has revolutionized the field of machine learning, for computer
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …