Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Sumedha Gandharava"'
Publikováno v:
SN Applied Sciences, Vol 3, Iss 5, Pp 1-16 (2021)
Abstract This study uses advanced modeling and simulation to explore the effects of external events such as radiation interactions on the synaptic devices in an electronic spiking neural network. Specifically, the networks are trained using the spike
Externí odkaz:
https://doaj.org/article/c7c47fb25ab34c4e971c8fda259a7d97
Autor:
Bhoj R. Singh, Richa Gandharva, R. Karthikeyan, Shiv Varan Singh, Akanksha Yadav, Vinodh Kumar O.R., Dharmendra K. Sinha, Varsha Jayakumar, Kuldeep Dhama, Dharmender Kumar, Sumedha Gandharava
Publikováno v:
Journal of Pure and Applied Microbiology, Vol 14, Iss suppl 1, Pp 1007-1016 (2020)
This study analyzed the determinants of morbidity, mortality, and case fatality rate (CFR) of the ongoing pandemic of severe acute respiratory syndrome coronavirus-2 disease 2019 (COVID-19). Data for 210 countries and territories available in publi
Externí odkaz:
https://doaj.org/article/5ac99530423a4e16bab4c2dadc106bc4
Publikováno v:
SN Applied Sciences, Vol 3, Iss 5, Pp 1-16 (2021)
This study uses advanced modeling and simulation to explore the effects of external events such as radiation interactions on the synaptic devices in an electronic spiking neural network. Specifically, the networks are trained using the spike-timing-d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1037d292856637d1659c861fe8c9c7a8
https://doaj.org/article/c7c47fb25ab34c4e971c8fda259a7d97
https://doaj.org/article/c7c47fb25ab34c4e971c8fda259a7d97
Publikováno v:
Electronics; Volume 11; Issue 9; Pages: 1392
Biological neural networks demonstrate remarkable resilience and the ability to compensate for neuron losses over time. Thus, the effects of neural/synaptic losses in the brain go mostly unnoticed until the loss becomes profound. This study analyses
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8e4fee388b921abb81ad8fc264debcfb
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 31:4206-4216
Spike-timing-dependent plasticity (STDP) is a fundamental synaptic learning rule observed in biology that leads to numerous behavioral and cognitive outcomes. Emulating STDP in electronic spiking neural networks with high-density memristive synapses
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::26b1422aeda3daece8e1411ec938b637
https://doi.org/10.1109/tnnls.2019.2952768
https://doi.org/10.1109/tnnls.2019.2952768
Autor:
Sumedha Gandharava Dahl
In this dissertation, memristor-based spiking neural networks (SNNs) are used to analyze the effect of radiation on the spatio-temporal pattern recognition (STPR) capability of the networks. Two-terminal resistive memory devices (memristors) are used
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e54514ba602b512ef7ba716764abfe70
https://doi.org/10.18122/td/1713/boisestate
https://doi.org/10.18122/td/1713/boisestate
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
MWSCAS
In this work, a memristor-based spiking neural network with a many-to-one feed-forward topology is designed for spatio-temporal pattern learning (25-pixel character ‘B’). A TiO 2 non-linear drift behavioral memristor model is used for simulation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2e8c2ceeb59316512104d989e9d69884
https://doi.org/10.1109/mwscas.2019.8885288
https://doi.org/10.1109/mwscas.2019.8885288
Publikováno v:
ICONS
In this paper, a feed-forward spiking neural network with memristive synapses is designed to learn a spatio-temporal pattern representing the 25-pixel character 'B' by separating correlated and uncorrelated afferents. The network uses spike-timing-de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b3496afcd7f9f73295b6f5ddfc017829
https://doi.org/10.1145/3354265.3354272
https://doi.org/10.1145/3354265.3354272