Results 21 to 30 of about 76,325 (312)
Fractional diffusion theory of balanced heterogeneous neural networks
Physical Review Research, 2021 Interactions of large numbers of spiking neurons give rise to complex neural dynamics with fluctuations occurring at multiple scales. Understanding the dynamical mechanisms underlying such complex neural dynamics is a long-standing topic of interest in ...
Asem Wardak, Pulin Gong
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Energy-Efficient Spiking Segmenter for Frame and Event-Based Images
Biomimetics, 2023 Semantic segmentation predicts dense pixel-wise semantic labels, which is crucial for autonomous environment perception systems. For applications on mobile devices, current research focuses on energy-efficient segmenters for both frame and event-based ...
Hong Zhang, Xiongfei Fan, Yu Zhang
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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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Exploiting Device Mismatch in Neuromorphic VLSI Systems to Implement Axonal Delays [PDF]
, 2012 Sheik S, Chicca E, Indiveri G. Exploiting Device Mismatch in Neuromorphic VLSI Systems to Implement Axonal Delays. Presented at the International Joint Conference on Neural Networks (IJCNN), Brisbane, Australia.Axonal delays are used in neural ...
Chicca, Elisabetta +2 more
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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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Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor [PDF]
, 2018 Neuromorphic computing is a new paradigm for design of both the computing hardware and algorithms inspired by biological neural networks. The event-based nature and the inherent parallelism make neuromorphic computing a promising paradigm for building ...
Glatz, Sebastian +4 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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