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BayesianSpikeFusion: accelerating spiking neural network inference via Bayesian fusion of early prediction. [PDF]
Front NeurosciHabara T, Sato T, Awano H.
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Estimating orientation in natural scenes: A spiking neural network model of the insect central complex. [PDF]
PLoS Comput BiolStentiford R +4 more
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An efficient intrusion detection model based on convolutional spiking neural network. [PDF]
Sci RepWang Z +4 more
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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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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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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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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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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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Deep Spiking Neural Network with Neural Oscillation and Spike-Phase Information
Proceedings of the AAAI Conference on Artificial Intelligence, 2021Deep spiking neural network (DSNN) is a promising computational model towards artificial intelligence. It benefits from both the DNNs and SNNs through a hierarchy structure to extract multiple levels of abstraction and the event-driven computational manner to provide ultra-low-power neuromorphic implementation, respectively. However, how to efficiently
Yi Chen +3 more
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