Results 31 to 40 of about 27,240 (263)
Federated Learning With Spiking Neural Networks [PDF]
IEEE Transactions on Signal Processing, 2021 As neural networks get widespread adoption in resource-constrained embedded devices, there is a growing need for low-power neural systems. Spiking Neural Networks (SNNs)are emerging to be an energy-efficient alternative to the traditional Artificial Neural Networks (ANNs) which are known to be computationally intensive. From an application perspective,
Yeshwanth Venkatesha +3 more
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Deep learning in spiking neural networks [PDF]
Neural Networks, 2019 In recent years, deep learning has been a revolution in the field of machine learning, for computer vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is trained in a supervised manner using backpropagation.
Kheradpisheh, Saeed Reza +5 more
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Spiking Neural Networks and Their Applications: A Review
Brain Sciences, 2022 The past decade has witnessed the great success of deep neural networks in various domains. However, deep neural networks are very resource-intensive in terms of energy consumption, data requirements, and high computational costs.
Kashu Yamazaki +3 more
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Bifurcation Spiking Neural Network
, 2019 Spiking neural networks (SNNs) has attracted much attention due to its great potential of modeling time-dependent signals. The firing rate of spiking neurons is decided by control rate which is fixed manually in advance, and thus, whether the firing rate is adequate for modeling actual time series relies on fortune.
Zhang, Shao-Qun +2 more
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Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing
Sensors, 2021 This work presents a new approach based on a spiking neural network for sound preprocessing and classification. The proposed approach is biologically inspired by the biological neuron’s characteristic using spiking neurons, and Spike-Timing-Dependent ...
Moshe Bensimon +2 more
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Spiking neural network with local plasticity and sparse connectivity for audio classification [PDF]
Известия высших учебных заведений: Прикладная нелинейная динамикаPurpose. Studying the possibility of implementing a data classification method based on a spiking neural network, which has a low number of connections and is trained based on local plasticity rules, such as Spike-Timing-Dependent Plasticity.
Rybka, Roman Борисович +4 more
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Sa-SNN: spiking attention neural network for image classification [PDF]
PeerJ Computer ScienceSpiking neural networks (SNNs) are known as third generation neural networks due to their energy efficient and low power consumption. SNNs have received a lot of attention due to their biological plausibility. SNNs are closer to the way biological neural
Yongping Dan +3 more
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Phase diagram of spiking neural networks [PDF]
Frontiers in Computational Neuroscience, 2015 oscillations are studied in this ...
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Real Spike: Learning Real-Valued Spikes for Spiking Neural Networks
, 2022 Accepted by ...
Guo, Yufei +7 more
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Equivalence of Additive and Multiplicative Coupling in Spiking Neural Networks
IEEE Access, 2023 Spiking neural network models characterize the emergent collective dynamics of circuits of biological neurons and help engineer neuro-inspired solutions across fields. Most dynamical systems’ models of spiking neural networks typically exhibit one
Georg Borner +2 more
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