Free-space optical spiking neural network [PDF]
PLoS ONENeuromorphic engineering has emerged as a promising avenue for developing brain-inspired computational systems. However, conventional electronic AI-based processors often encounter challenges related to processing speed and thermal dissipation.
Reyhane Ahmadi +2 more
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Research on Anti-Interference Performance of Spiking Neural Network Under Network Connection Damage [PDF]
Brain SciencesBackground: With the development of artificial intelligence, memristors have become an ideal choice to optimize new neural network architectures and improve computing efficiency and energy efficiency due to their combination of storage and computing ...
Yongqiang Zhang +5 more
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RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network [PDF]
Computer Vision and Pattern Recognition, 2020 Spiking Neural Networks (SNNs) have recently attracted significant research interest as the third generation of artificial neural networks that can enable low-power event-driven data analytics.
Bing Han +2 more
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Efficient and Accurate Conversion of Spiking Neural Network with Burst Spikes [PDF]
International Joint Conference on Artificial Intelligence, 2022 Spiking neural network (SNN), as a brain-inspired energy-efficient neural network, has attracted the interest of researchers. While the training of spiking neural networks is still an open problem. One effective way is to map the weight of trained ANN to
Yang Li, Yi Zeng
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Financial time series prediction using spiking neural networks. [PDF]
PLoS ONE, 2014 In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction.
David Reid +2 more
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A Bandwidth-Efficient Emulator of Biologically-Relevant Spiking Neural Networks on FPGA
IEEE Access, 2022 Closed-loop experiments involving biological and artificial neural networks would improve the understanding of neural cells functioning principles and lead to the development of new generation neuroprosthesis.
Gianluca Leone +2 more
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An unsupervised STDP-based spiking neural network inspired by biologically plausible learning rules and connections [PDF]
Neural Networks, 2022 The backpropagation algorithm has promoted the rapid development of deep learning, but it relies on a large amount of labeled data and still has a large gap with how humans learn.
Yi-Ting Dong +3 more
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Targeting operational regimes of interest in recurrent neural networks.
PLoS Computational Biology, 2023 Neural computations emerge from local recurrent neural circuits or computational units such as cortical columns that comprise hundreds to a few thousand neurons.
Pierre Ekelmans +2 more
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Sparse Compressed Spiking Neural Network Accelerator for Object Detection [PDF]
IEEE Transactions on Circuits and Systems Part 1: Regular Papers, 2022 Spiking neural networks (SNNs), which are inspired by the human brain, have recently gained popularity due to their relatively simple and low-power hardware for transmitting binary spikes and highly sparse activation maps.
Hong-Han Lien, Tian-Sheuan Chang
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IEEE transactions on neural systems and rehabilitation engineering, 2023 Electroencephalography (EEG) signals classification is essential for the brain-computer interface (BCI). Recently, energy-efficient spiking neural networks (SNNs) have shown great potential in EEG analysis due to their ability to capture the complex ...
Pei-Liang Gong +3 more
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