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Third Generation Neural Networks: Spiking Neural Networks
2009Artificial Neural Networks (ANNs) are based on highly simplified brain dynamics and have been used as powerful computational tools to solve complex pattern recognition, function estimation, and classification problems. Throughout their development, ANNs have been evolving towards more powerful and more biologically realistic models. In the last decade,
Samanwoy Ghosh-Dastidar, Hojjat Adeli
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Spiking Neural Networks for Cortical Neuronal Spike Train Decoding
Neural Computation, 2010Recent investigation of cortical coding and computation indicates that temporal coding is probably a more biologically plausible scheme used by neurons than the rate coding used commonly in most published work. We propose and demonstrate in this letter that spiking neural networks (SNN), consisting of spiking neurons that propagate information by the ...
Fang, Huijuan, Wang, Yongji, He, Jiping
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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 neural network-based spike discriminator
Journal of Neuroscience Methods, 1994A software routine to reconstruct individual spike trains from multi-neuron, single-channel extracellular recordings was designed. Using a neural network algorithm that automatically clusters and sorts the spikes, the only user input needed is the threshold level for spike detection and the number of unit types present in the recording.
J S, Oghalai, W N, Street, W S, Rhode
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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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Simulating spiking neural networks on GPU
Network: Computation in Neural Systems, 2012Modern graphics cards contain hundreds of cores that can be programmed for intensive calculations. They are beginning to be used for spiking neural network simulations. The goal is to make parallel simulation of spiking neural networks available to a large audience, without the requirements of a cluster. We review the ongoing efforts towards this goal,
Romain, Brette, Dan F M, Goodman
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Evolving Spiking Neural Networks
2018Evolving SNN (eSNN) are a class of SNN and also a class of ECOS (Chap. 2) where spiking neurons are created (evolved) and merged in an incremental way to capture clusters and patterns from incoming data. This gives a new quality of the SNN systems to become adaptive, fast trained and to capture meaningful patterns from the data, departing the “curse of
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