Spiking Neuron Networks (SNNs) are often referred to as the third generation of neural networks. Highly inspired by natural computing in the brain and recent advances in neurosciences, they derive their strength and interest from an accurate modeling of synaptic interactions between neurons, taking in...
B. Ruf, “Computing functions with spiking neurons in temporal coding”, Proc. of the Int. Work-Conference on Artificial and Natural Neural Networks IWANN'97 , Lecture Notes in Computer Science, vol. 1240, pp 265–272, Springer, Berlin, 1997....
Spiking neurons differ in essential aspects from the familiar computational units of common neural network models, suchas McCulloch-Pitts neuron... W Maass - MIT Press 被引量: 110发表: 0年 [Physics of Neural Networks] Models of Neural Networks IV || Paradigms for Computing with Spiking ...
Computing with spiking neuron networks 2012, Handbook of Natural Computing View all citing articles on ScopusHélène Paugam-Moisy obtained the French degree Agrégation de Mathématiques in 1987 and she received a Ph.D. in Computer Science in 1992 at University Lyon 1 and Ecole Normale Supérieur...
This paper presents new findings in the design and application of biologically plausible neural networks based on spiking neuron models, which represent a ... A Belatreche,LP Maguire,M Mcginnity - 《Soft Computing》 被引量: 115发表: 2007年 Supervised learning with spiking neural networks We der...
These 0 or 1 values represent input over time from some other spiking neuron. In other words, at time t = 0, the input value from the first spike train is 0; at time t = 1, the input is 0; at t = 2, the input is 1; and so on through t = 15 when the input value is ...
Seo J, Brezzo B, Liu Y, et al. A 45 nm CMOS neuromorphic chip with a scalable architecture for learning in networks of spiking neurons. In: Proceedings of IEEE Custom Integrated Circuits Conference (CICC), 2011. 1–4 Indiveri G, Linares-Barranco B, Hamilton T J, et al. Neuromorphic si...
A time delayed effect in a recurrent neural network model was investigated, where the model is constructed on the binary states of each neuron and on the d... Y Suemitsu,S Nara - 《Neural Computing & Applications》 被引量: 28发表: 2003年 Neural Assembly Computing Spiking neurons can realiz...
Information encoding in spikes and computations performed by spiking neurons are two sides of the same coin and should be consistent with each other. This study uses this consistency requirement to derive some new results for inter-spike interval (ISI) coding in networks of integrate and fire (IF...
Spiking neural networks are one of the most effective ways to simulate how a real neuron fires or “spikes” to relay a signal before going back to being silent. The end result is a system that uses much less power than artificial neural networks, which are currently employed in the ...