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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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Review on spiking neural network-based ECG classification methods for low-power environments. [PDF]
Biomed Eng LettChoi H, Park J, Lee J, Sim D.
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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.
Samanwoy, Ghosh-Dastidar, 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.
Samanwoy, Ghosh-Dastidar, Hojjat, Adeli
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Spiking neural network applications
2017 International Artificial Intelligence and Data Processing Symposium (IDAP), 2017Spiking Neural Network (SNN) are 3rd Generation Artificial Neural Networks (ANN) models. The fact that time information is processed in the form of spikes and there are multiple synapses between cells (neurons) are the most important features that distinguish SNN from previous generations.
Celik, Gaffari, Talu, M. Fatih
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Spiking neural networks take control
Science Robotics, 2021Brain-inspired neural network architecture overcomes unsolved classical control theory problem for telerobotics.
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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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Optoelectronic spiking neural network
SPIE Proceedings, 2013Proposed by the authors compact optoelectronic implementation of a spiking neural network uses a matrix of semiconductor lasers. Also proposed the implementation of neural element on bispin-device, which is able to manage bya range of lasers in the array. Described the principles of the network in the training and operation mode.
V. P. Kozemiako +4 more
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Spiking Neural Networks and Mathematical Models
2023Neural networks are applied in various scientific fields such as medicine, engineering, pharmacology, etc. Investigating operations of neural networks refers to estimating the relationship among single neurons and their contributions to the network as well.
Mirto M, Gasparinatou +2 more
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