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 …
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 …
Loihi: A neuromorphic manycore processor with on-chip learning
Loihi is a 60-mm2 chip fabricated in Intels 14-nm process that advances the state-of-the-art
modeling of spiking neural networks in silicon. It integrates a wide range of novel features for …
modeling of spiking neural networks in silicon. It integrates a wide range of novel features for …
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 …
Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks
Spiking neural networks (SNNs) are nature's versatile solution to fault-tolerant, energy-
efficient signal processing. To translate these benefits into hardware, a growing number of …
efficient signal processing. To translate these benefits into hardware, a growing number of …
Differentiable spike: Rethinking gradient-descent for training spiking neural networks
Abstract Spiking Neural Networks (SNNs) have emerged as a biology-inspired method
mimicking the spiking nature of brain neurons. This bio-mimicry derives SNNs' energy …
mimicking the spiking nature of brain neurons. This bio-mimicry derives SNNs' energy …
Deep learning in spiking neural networks
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 …
[HTML][HTML] Deep learning with spiking neurons: opportunities and challenges
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 …
A solution to the learning dilemma for recurrent networks of spiking neurons
Recurrently connected networks of spiking neurons underlie the astounding information
processing capabilities of the brain. Yet in spite of extensive research, how they can learn …
processing capabilities of the brain. Yet in spite of extensive research, how they can learn …
Exploring neuromorphic computing based on spiking neural networks: Algorithms to hardware
Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving a lot of
attention lately due to its promise of reducing the computational energy, latency, as well as …
attention lately due to its promise of reducing the computational energy, latency, as well as …