Results 31 to 40 of about 28,095 (262)
Comparison of Artificial and Spiking Neural Networks on Digital Hardware
Frontiers in Neuroscience, 2021 Despite the success of Deep Neural Networks—a type of Artificial Neural Network (ANN)—in problem domains such as image recognition and speech processing, the energy and processing demands during both training and deployment are growing at an ...
Simon Davidson, Steve B. Furber
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Modeling spiking neural networks
Theoretical Computer Science, 2008 AbstractA notation for the functional specification of a wide range of neural networks consisting of temporal or non-temporal neurons, is proposed. The notation is primarily a mathematical framework, but it can also be illustrated graphically and can be extended into a language in order to be automated. Its basic building blocks are processing entities,
Ioannis D. Zaharakis, Achilles D. Kameas
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Neural Architecture Search for Spiking Neural Networks
, 2022 Spiking Neural Networks (SNNs) have gained huge attention as a potential energy-efficient alternative to conventional Artificial Neural Networks (ANNs) due to their inherent high-sparsity activation. However, most prior SNN methods use ANN-like architectures (e.g., VGG-Net or ResNet), which could provide sub-optimal performance for temporal sequence ...
Youngeun Kim +4 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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Integrating Non-spiking Interneurons in Spiking Neural Networks [PDF]
Frontiers in Neuroscience, 2020 Researchers working with neural networks have historically focused on either non-spiking neurons tractable for running on computers or more biologically plausible spiking neurons typically requiring special hardware. However, in nature homogeneous networks of neurons do not exist.
Beck Strohmer +3 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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Spiking neural networks for computer vision [PDF]
Interface Focus, 2018 Abstract State-of-the-art computer vision systems use frame-based cameras that sample the visual scene as a series of high-resolution images. These are then processed using convolutional neural networks using neurons with continuous outputs.
Michael Hopkins +3 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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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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Designing the Dynamics of Spiking Neural Networks [PDF]
Physical Review Letters, 2006 4 pages, 3 ...
Memmesheimer, R., Timme, M.
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