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Opportunities for neuromorphic computing algorithms and applications
Neuromorphic computing technologies will be important for the future of computing, but
much of the work in neuromorphic computing has focused on hardware development. Here …
much of the work in neuromorphic computing has focused on hardware development. Here …
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 …
Spikingjelly: An open-source machine learning infrastructure platform for spike-based intelligence
Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic
chips with high energy efficiency by introducing neural dynamics and spike properties. As …
chips with high energy efficiency by introducing neural dynamics and spike properties. As …
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 …
Moiré synaptic transistor with room-temperature neuromorphic functionality
Moiré quantum materials host exotic electronic phenomena through enhanced internal
Coulomb interactions in twisted two-dimensional heterostructures,,–. When combined with …
Coulomb interactions in twisted two-dimensional heterostructures,,–. When combined with …
Incorporating learnable membrane time constant to enhance learning of spiking neural networks
Abstract Spiking Neural Networks (SNNs) have attracted enormous research interest due to
temporal information processing capability, low power consumption, and high biological …
temporal information processing capability, low power consumption, and high biological …
Slayer: Spike layer error reassignment in time
Abstract Configuring deep Spiking Neural Networks (SNNs) is an exciting research avenue
for low power spike event based computation. However, the spike generation function is non …
for low power spike event based computation. However, the spike generation function is non …
[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 …
Neuromorphic engineering: from biological to spike‐based hardware nervous systems
The human brain is a sophisticated, high‐performance biocomputer that processes multiple
complex tasks in parallel with high efficiency and remarkably low power consumption …
complex tasks in parallel with high efficiency and remarkably low power consumption …
The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks
Brains process information in spiking neural networks. Their intricate connections shape the
diverse functions these networks perform. Yet how network connectivity relates to function is …
diverse functions these networks perform. Yet how network connectivity relates to function is …