Opportunities for neuromorphic computing algorithms and applications

CD Schuman, SR Kulkarni, M Parsa… - Nature Computational …, 2022‏ - nature.com
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 …

Towards spike-based machine intelligence with neuromorphic computing

K Roy, A Jaiswal, P Panda - Nature, 2019‏ - nature.com
Guided by brain-like 'spiking'computational frameworks, neuromorphic computing—brain-
inspired computing for machine intelligence—promises to realize artificial intelligence while …

Spikingjelly: An open-source machine learning infrastructure platform for spike-based intelligence

W Fang, Y Chen, J Ding, Z Yu, T Masquelier… - Science …, 2023‏ - science.org
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 …

Training spiking neural networks using lessons from deep learning

JK Eshraghian, M Ward, EO Neftci… - Proceedings of the …, 2023‏ - ieeexplore.ieee.org
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 …

Moiré synaptic transistor with room-temperature neuromorphic functionality

X Yan, Z Zheng, VK Sangwan, JH Qian, X Wang… - Nature, 2023‏ - nature.com
Moiré quantum materials host exotic electronic phenomena through enhanced internal
Coulomb interactions in twisted two-dimensional heterostructures,,–. When combined with …

Incorporating learnable membrane time constant to enhance learning of spiking neural networks

W Fang, Z Yu, Y Chen, T Masquelier… - Proceedings of the …, 2021‏ - openaccess.thecvf.com
Abstract Spiking Neural Networks (SNNs) have attracted enormous research interest due to
temporal information processing capability, low power consumption, and high biological …

Slayer: Spike layer error reassignment in time

SB Shrestha, G Orchard - Advances in neural information …, 2018‏ - proceedings.neurips.cc
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 …

[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 …

Neuromorphic engineering: from biological to spike‐based hardware nervous systems

JQ Yang, R Wang, Y Ren, JY Mao, ZP Wang… - Advanced …, 2020‏ - Wiley Online Library
The human brain is a sophisticated, high‐performance biocomputer that processes multiple
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

F Zenke, TP Vogels - Neural computation, 2021‏ - direct.mit.edu
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 …