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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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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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Neural encoding and spike generation for Spiking Neural Networks implemented in FPGA
2015 International Conference on Electronics, Communications and Computers (CONIELECOMP), 2015In this article a new digital system for generating spike pulses is presented. In real time, the system can convert digital values into artificial neural spikes for Spiking Neural Networks (SNN). The digital system can perform three basic functions: to generate spikes, to convert digital data values into pulse trains and, additionally, to encode spike ...
José Rodrigues de Oliveira-Neto +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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Spiking-NeRF: Spiking Neural Network for Energy-Efficient Neural Rendering
ACM Journal on Emerging Technologies in Computing SystemsArtificial Neural Networks (ANNs) have achieved remarkable performance in many artificial intelligence tasks. As the application scenarios become more sophisticated, the computation and energy consumption of ANNs are also constantly increasing, which poses a challenge for deploying ANNs on energy-constrained devices.
Ziwen Li +3 more
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The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks
IEEE Transactions on Neural Networks and Learning Systems, 2022Benjamin Cramer +2 more
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Photonic Spiking Neural Networks and Graphene-on-Silicon Spiking Neurons
Journal of Lightwave Technology, 2022Aashu Jha +2 more
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