Results 11 to 20 of about 76,325 (312)
Spiking Neural Networks for Nonlinear Regression [PDF]
Proceedings of the Neuromorphic Materials, Devices, Circuits and Systems, 2023 Spiking neural networks (SNN), also often referred to as the third generation of neural networks, carry the potential for a massive reduction in memory and energy consumption over traditional, second-generation neural networks. Inspired by the undisputed efficiency of the human brain, they introduce temporal and neuronal sparsity, which can be ...
Alexander Henkes +2 more
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Spiking Neural Network Based on Multi-Scale Saliency Fusion for Breast Cancer Detection
Entropy, 2022 Deep neural networks have been successfully applied in the field of image recognition and object detection, and the recognition results are close to or even superior to those from human beings.
Qiang Fu, Hongbin Dong
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Attention Spiking Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 18 pages, 8 figures, Under ...
Man Yao +7 more
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Quantum superposition inspired spiking neural network [PDF]
iScience, 2021 Despite advances in artificial intelligence models, neural networks still cannot achieve human performance, partly due to differences in how information is encoded and processed compared to human brain. Information in an artificial neural network (ANN) is represented using a statistical method and processed as a fitting function, enabling handling of ...
Yinqian Sun, Yi Zeng, Tielin Zhang
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Neural Spike Sorting Using Binarized Neural Networks [PDF]
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021 This article presents the design and efficient hardware implementation of binarized neural networks (BNNs) for brain-implantable neural spike sorting. In contrast to the conventional artificial neural networks (ANNs), in which the weights and activation functions of neurons are represented using real values, the BNNs utilize binarized weights and ...
Daniel Valencia, Amir Alimohammad
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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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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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A Bandwidth-Efficient Emulator of Biologically-Relevant Spiking Neural Networks on FPGA
IEEE Access, 2022 Closed-loop experiments involving biological and artificial neural networks would improve the understanding of neural cells functioning principles and lead to the development of new generation neuroprosthesis.
Gianluca Leone +2 more
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Targeting operational regimes of interest in recurrent neural networks.
PLoS Computational Biology, 2023 Neural computations emerge from local recurrent neural circuits or computational units such as cortical columns that comprise hundreds to a few thousand neurons.
Pierre Ekelmans +2 more
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IEEE Access, 2022 In recent years, the use of artificial neural network applications to perform object classification and event prediction has increased, mainly from research about deep learning techniques running on hardware such as GPU and FPGA.
Francisco De Assis Pereira Januario +1 more
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