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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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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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Quaternion Spike Neural Networks
2016This work presents a new type of Spike Neural Networks (SNN) developed in the quaternion algebra framework. This new neural structure based on SNN is developed using the quaternion algebra. The training algorithm was extended adjusting the weights according to the quaternion multiplication rule, which allows accurate results with a decreased network ...
Luis Lechuga-Gutiérrez +1 more
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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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A regenerating spiking neural network
Neural Networks, 2005Due to their distributed architecture, artificial neural networks often show a graceful performance degradation to the loss of few units or connections. Living systems also display an additional source of fault-tolerance obtained through distributed processes of self-healing: defective components are actively regenerated.
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Spike Attention Coding for Spiking Neural Networks
IEEE Transactions on Neural Networks and Learning SystemsSpiking neural networks (SNNs), an important family of neuroscience-oriented intelligent models, play an essential role in the neuromorphic computing community. Spike rate coding and temporal coding are the mainstream coding schemes in the current modeling of SNNs.
Jiawen Liu +4 more
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ON THE PROBABILISTIC OPTIMIZATION OF SPIKING NEURAL NETWORKS
International Journal of Neural Systems, 2010The construction of a Spiking Neural Network (SNN), i.e. the choice of an appropriate topology and the configuration of its internal parameters, represents a great challenge for SNN based applications. Evolutionary Algorithms (EAs) offer an elegant solution for these challenges and methods capable of exploring both types of search spaces ...
Stefan Schliebs +2 more
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Deep Spiking Neural Network with Ternary Spikes
2022 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2022Congyi Sun +3 more
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Photonic integrated neuro-synaptic core for convolutional spiking neural network
Opto-Electronic Advances, 2023Dianzhuang Zheng
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A multi-layer spiking neural network-based approach to bearing fault diagnosis
Reliability Engineering and System Safety, 2022Tangfan Xiahou +2 more
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