Results 21 to 30 of about 3,555,912 (299)
Spiking Neural Network Model for Brain-like Computing and Progress of Its Learning Algorithm [PDF]
Jisuanji kexue, 2023 With the increasingly prominent limitations of deep neural networks in practical applications,brain-like computing spiking neural networks with biological interpretability have become the focus of research.The uncertainty and complex diversity of ...
HUANG Zenan, LIU Xiaojie, ZHAO Chenhui, DENG Yabin, GUO Donghui
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Quantization in Spiking Neural Networks
CoRR, 2023 arXiv admin note: text overlap with arXiv:2305 ...
Bernhard Alois Moser, Michael Lunglmayr
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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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Molecular Toxicity Virtual Screening Applying a Quantized Computational SNN-Based Framework
Molecules, 2023 Spiking neural networks are biologically inspired machine learning algorithms attracting researchers’ attention for their applicability to alternative energy-efficient hardware other than traditional computers.
Mauro Nascimben, Lia Rimondini
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Expressivity of Spiking Neural Networks
CoRR, 2023 The synergy between spiking neural networks and neuromorphic hardware holds promise for the development of energy-efficient AI applications. Inspired by this potential, we revisit the foundational aspects to study the capabilities of spiking neural networks where information is encoded in the firing time of neurons.
Manjot Singh +2 more
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Stochasticity and robustness in spiking neural networks [PDF]
Neurocomputing, 2021 Artificial neural networks normally require precise weights to operate, despite their origins in biological systems, which can be highly variable and noisy. When implementing artificial networks which utilize analog 'synaptic' devices to encode weights, however, inherent limits are placed on the accuracy and precision with which these values can be ...
Wilkie Olin-Ammentorp +4 more
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Exploring the Connection Between Binary and Spiking Neural Networks
Frontiers in Neuroscience, 2020 On-chip edge intelligence has necessitated the exploration of algorithmic techniques to reduce the compute requirements of current machine learning frameworks.
Sen Lu, Abhronil Sengupta
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Spiking Neural Networks: A Survey
IEEE Access, 2022 The field of Deep Learning (DL) has seen a remarkable series of developments with increasingly accurate and robust algorithms. However, the increase in performance has been accompanied by an increase in the parameters, complexity, and training and inference time of the models, which means that we are rapidly reaching a point where DL may no longer be ...
João D. Nunes +3 more
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Agreement in Spiking Neural Networks
Journal of Computational Biology, 2022 We study the problem of binary agreement in a spiking neural network (SNN). We show that binary agreement on n inputs can be achieved with O(n) of auxiliary neurons. Our simulation results suggest that agreement can be achieved in our network in O(logn) time. We then describe a subclass of SNNs with a biologically plausible property, which we call size-
Kunev, Martin +2 more
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Event-based backpropagation can compute exact gradients for spiking neural networks
Scientific Reports, 2021 Spiking neural networks combine analog computation with event-based communication using discrete spikes. While the impressive advances of deep learning are enabled by training non-spiking artificial neural networks using the backpropagation algorithm ...
Timo C. Wunderlich, Christian Pehle
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