Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Sylvain Saighi"'
Publikováno v:
IEEE Access, Vol 11, Pp 146103-146121 (2023)
The massive deployment of the Internet of Things (IoT) combined with the need to reduce its impact on global energy consumption calls for the design of intelligent sensors. These sensors must be able to process information in situ to reduce the amoun
Externí odkaz:
https://doaj.org/article/0ac96f77a358410c8bfb509999d05ea2
Publikováno v:
IEEE Access, Vol 11, Pp 120654-120665 (2023)
Spiking Neural Networks (SNNs) are promising candidates for low-power and low-latency embedded artificial intelligence. However, those networks require event-based data produced by neuromorphic sensors which are not widely available, except for a few
Externí odkaz:
https://doaj.org/article/0de104579a714d9581c7c8d5c4c6fc0b
Publikováno v:
2022 IEEE Biomedical Circuits and Systems Conference (BioCAS).
Autor:
Sören Boyn, Julie Grollier, Gwendal Lecerf, Bin Xu, Nicolas Locatelli, Stéphane Fusil, Stéphanie Girod, Cécile Carrétéro, Karin Garcia, Stéphane Xavier, Jean Tomas, Laurent Bellaiche, Manuel Bibes, Agnès Barthélémy, Sylvain Saïghi, Vincent Garcia
Publikováno v:
Nature Communications, Vol 8, Iss 1, Pp 1-7 (2017)
Accurate modelling of memristor dynamics is essential for the development of autonomous learning in artificial neural networks. Through a combined theoretical and experimental study of the polarization switching process in ferroelectric memristors, B
Externí odkaz:
https://doaj.org/article/4eca89cea8fb41d595066d4cec248c7a
Publikováno v:
Frontiers in Neuroscience, Vol 13 (2019)
Neurological diseases can be studied by performing bio-hybrid experiments using a real-time biomimetic Spiking Neural Network (SNN) platform. The Hodgkin-Huxley model offers a set of equations including biophysical parameters which can serve as a bas
Externí odkaz:
https://doaj.org/article/18d3a0f919a04192aeeefe0dfbae5982