Results 251 to 260 of about 60,411 (286)
Correction: Paired competing neurons improving STDP supervised local learning in spiking neural networks. [PDF]
Front NeurosciGoupy G, Tirilly P, Bilasco IM.
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Noise and Dynamical Synapses as Optimization Tools for Spiking Neural Networks. [PDF]
Entropy (Basel)Garipova Y, Yonekura S, Kuniyoshi Y.
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Three-stage hybrid spiking neural networks fine-tuning for speech enhancement. [PDF]
Front NeurosciAbuhajar N +7 more
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Research on target detection for autonomous driving based on ECS-spiking neural networks. [PDF]
Sci RepJin M, Wang X, Guo C, Yang S.
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Attention Spiking Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 18 pages, 8 figures, Under ...
Man Yao, Guangshe Zhao, Hengyu Zhang
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International Journal of Neural Systems, 2009
Most current Artificial Neural Network (ANN) models are based on highly simplified brain dynamics. They have been used as powerful computational tools to solve complex pattern recognition, function estimation, and classification problems. ANNs have been evolving towards more powerful and more biologically realistic models.
Hojjat Adeli
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Most current Artificial Neural Network (ANN) models are based on highly simplified brain dynamics. They have been used as powerful computational tools to solve complex pattern recognition, function estimation, and classification problems. ANNs have been evolving towards more powerful and more biologically realistic models.
Hojjat Adeli
exaly +3 more sources
A spiking recurrent neural network
IEEE Computer Society Annual Symposium on VLSI, 2004A spiking recurrent neural network implementing an associative memory is proposed. The circuit including four integrate-and-fire (IF) and Willshaw-type binary synapses is designed with the AMI 0.5/spl mu/m CMOS process. A large-scale network is simulated with Matlab and its storage capacity is calculated and analyzed.
Yuan Li, John G. Harris
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Applications of spiking neural networks
Information Processing Letters, 2005We are pleased to introduce this issue of Information Processing Letters pre-senting state-of-the-art articles on Applications of Spiking Neural Networks.Spiking neural networks are a class of neural networks that is increasinglyreceiving attention as both a computationally powerful and biologically moreplausible model of distributed computation.
Sander M. Bohté, Joost N. Kok
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Fuzzification of Spiked Neural Networks
2008 Second UKSIM European Symposium on Computer Modeling and Simulation, 2008Biological systems are slow, wide and messy whereas computer systems are fast, deep and precise. Fuzzy neural networks use fuzzy logic to implement higher level reasoning and incorporate expert knowledge into the system while neural networks deal with the low level computational structures capable of learning and adaptation.
David C. Reid, Maybin K. Muyeba
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Spiking Neural Network Architecture
Computer, 2015This installment of Computer’s series highlighting the work published in IEEE Computer Society journals comes from the IEEE Transactions on Computers.
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