Results 21 to 30 of about 27,240 (263)
Unsupervised Spiking Neural Network with Dynamic Learning of Inhibitory Neurons
Sensors, 2023 A spiking neural network (SNN) is a type of artificial neural network that operates based on discrete spikes to process timing information, similar to the manner in which the human brain processes real-world problems.
Geunbo Yang +7 more
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SpikeGoogle: Spiking Neural Networks with GoogLeNet‐like inception module
CAAI Transactions on Intelligence Technology, 2022 Spiking Neural Network is known as the third‐generation artificial neural network whose development has great potential. With the help of Spike Layer Error Reassignment in Time for error back‐propagation, this work presents a new network called ...
Xuan Wang +6 more
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Long-Tailed Characteristics of Neural Activity Induced by Structural Network Properties
Frontiers in Applied Mathematics and Statistics, 2022 Over the past few decades, neuroscience studies have elucidated the structural/anatomical network characteristics in the brain and their associations with functional networks and the dynamics of neural activity.
Sou Nobukawa, Sou Nobukawa
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Spiking neural networks for computer vision [PDF]
Interface Focus, 2018 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. Biological vision systems use a quite different approach, where the eyes (cameras) sample the visual scene continuously ...
Michael Hopkins +3 more
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Models developed for spiking neural networks
MethodsX, 2023 Emergence of deep neural networks (DNNs) has raised enormous attention towards artificial neural networks (ANNs) once again. They have become the state-of-the-art models and have won different machine learning challenges. Although these networks are inspired by the brain, they lack biological plausibility, and they have structural differences compared ...
Shahriar Rezghi Shirsavar +2 more
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HF-SNN: High-Frequency Spiking Neural Network
IEEE Access, 2021 As the third generation of neural networks, spiking neural network (SNN) motivated by neurophysiology enjoys considerable advances due to integrating different information, such as time and space.
Jing Su, Jing Li
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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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An Implementation of Actor-Critic Algorithm on Spiking Neural Network Using Temporal Coding Method
Applied Sciences, 2022 Taking advantage of faster speed, less resource consumption and better biological interpretability of spiking neural networks, this paper developed a novel spiking neural network reinforcement learning method using actor-critic architecture and temporal ...
Junqi Lu +4 more
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Building Logistic Spiking Neuron Models Using Analytical Approach
IEEE Access, 2019 Spiking neuron models are inspired by biological neurons. They can simulate the neuronal activities of the mammalian brains, such as spiking (integrator) and periodic oscillation (resonator).
Lei Zhang
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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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