Results 291 to 300 of about 76,325 (312)

A framework for the integration of the Emotiv EEG System into the NeuCube spiking neural network environment for robotics control

, 2014
Jianfei Wang
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Spiking Neural Networks, Perspective

, 2010
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SPIKING NEURAL NETWORKS

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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Spiking neural network applications

2017 International Artificial Intelligence and Data Processing Symposium (IDAP), 2017
Spiking 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, 2021
Brain-inspired neural network architecture overcomes unsolved classical control theory problem for telerobotics.
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A regenerating spiking neural network

Neural Networks, 2005
Due 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 Systems
Spiking 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, Yifan Hu, Guoqi Li, Jing Pei, Lei Deng   +4 more
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Optoelectronic spiking neural network

SPIE Proceedings, 2013
Proposed 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, O. K. Kolesnytskyj, T. S. Lischenko, W. Wojcik, A. Sulemenov   +4 more
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Spiking Neural Networks and Mathematical Models

2023
Neural 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, Nikolaos, Matzakos, Panagiotis, Vlamos   +2 more
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Designing spiking neural networks

2016 13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET), 2016
The problem of design is the most important part of complex systems building. This is also true for spiking neural networks. In this paper, the next steps of SNN design are described: coding, selecting neuron model and learning algorithm, creating network architecture. Software and hardware solutions for simulating these networks are also discussed. We
Yaroslav Dorogyy, Vadym Kolisnichenko
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