Results 31 to 40 of about 80,289 (279)

Tunnel junction based memristors as artificial synapses [PDF]

Frontiers in Neuroscience, 2015
We prepared magnesia, tantalum oxide, and barium titanate based tunnel junction structures and investigated their memristive properties. The low amplitudes of the resistance change in these types of junctions are the major obstacle for their use. Here, we increased the amplitude of the resistance change from 10% up to 100%.
Thomas, Andy, Niehörster, Stefan, Fabretti, Savio, Shepheard, Norman, Kuschel, Olga, Küpper, Karsten, Wollschläger, Joachim, Krzysteczko, Patryk, Chicca, Elisabetta   +8 more
openaire   +4 more sources

Plasma fibrinogen levels and all-cause and cause-specific mortality in an Italian adult population: results from the Moli-sani study

Bleeding, Thrombosis and Vascular Biology, 2023
Epidemiological data on the association between fibrinogen levels and mortality are scarse and controversial. Longitudinal analyses were performed, separately by sex, on 17,689 individuals from the Moli-sani study [53% women, ≥35 years, free from ...
Roberta Parisi, Simona Costanzo, Romy de Laat-Kremers, Augusto Di Castelnuovo, Amalia De Curtis, Teresa Panzera, Mariarosaria Persichillo, Chiara Cerletti, Giovanni de Gaetano, Maria Benedetta Donati, Licia Iacoviello, Bas de Laat , for the Moli-sani Study Investigators   +12 more
doaj   +1 more source

Thrombomodulin is a stronger indicator of combined oral contraceptives-induced activated protein C pathway resistance in the thrombin generation test than activated protein C

Frontiers in Cardiovascular Medicine
BackgroundThe mechanism by which combined oral contraceptives (COCs) lead to hypercoagulation is not fully understood, although activated protein C (APC) pathway resistance has been implicated.
Marisa Ninivaggi, Lily Sokolova, Lily Sokolova, Demy Donkervoort, Bas de Laat, Bas de Laat, Romy de Laat-Kremers   +6 more
doaj   +1 more source

3D Neuromorphic Hardware with Single Thin‐Film Transistor Synapses Over Single Thin‐Body Transistor Neurons by Monolithic Vertical Integration

Advanced Science, 2023
Neuromorphic hardware with a spiking neural network (SNN) can significantly enhance the energy efficiency for artificial intelligence (AI) functions owing to its event‐driven and spatiotemporally sparse operations.
Joon‐Kyu Han, Jung‐Woo Lee, Yeeun Kim, Young Bin Kim, Seong‐Yun Yun, Sang‐Won Lee, Ji‐Man Yu, Keon Jae Lee, Hyun Myung, Yang‐Kyu Choi   +9 more
doaj   +1 more source

Dynamic clamp with StdpC software [PDF]

, 2011
Dynamic clamp is a powerful method that allows the introduction of artificial electrical components into target cells to simulate ionic conductances and synaptic inputs.
A Szücs, A Szücs, A Szücs, A Szücs, AA Prinz, AA Sharp, AA Sharp, AD Dorval, AI Selverston, AJ Preyer, C Muñiz, C Muñiz, CJ Kros, DW Wheeler, Dávid Samu, EI Watanabe, G Berecki, György Kemenes, HDI Abarbanel, HP Robinson, HPC Robinson, Ildikó Kemenes, J-M Goaillard, JC Bettencourt, JP Horn, JS Rothman, K Staras, Kevin Staras, L Oltedal, LS Milescu, Michael Crossley, MN Economo, NS Desai, OP Hamill, P Chamorro, PHM Kullmann, R Bertram, R Brette, R Brette, R Wilders, R Wilders, RC Elson, RD Pinto, RJ Butera Jr., S Song, T Branco, T Nowotny, T Nowotny, TA Kinard, Thomas Nowotny, TI Netoff, Vincenzo Marra   +51 more
core   +1 more source

Organic Memristor‐Based Flexible Neural Networks with Bio‐Realistic Synaptic Plasticity for Complex Combinatorial Optimization

Advanced Science, 2023
Hardware neural networks with mechanical flexibility are promising next‐generation computing systems for smart wearable electronics. Several studies have been conducted on flexible neural networks for practical applications; however, developing systems ...
Hyeongwook Kim, Miseong Kim, Aejin Lee, Hea‐Lim Park, Jaewon Jang, Jin‐Hyuk Bae, In Man Kang, Eun‐Sol Kim, Sin‐Hyung Lee   +8 more
doaj   +1 more source

A Heterosynaptic Learning Rule for Neural Networks

, 2006
In this article we intoduce a novel stochastic Hebb-like learning rule for neural networks that is neurobiologically motivated. This learning rule combines features of unsupervised (Hebbian) and supervised (reinforcement) learning and is stochastic with ...
Bak P., Bosman R. J. C., Fitzsimonds R. M., FRANK EMMERT-STREIB, Hebb D. O., Hertz J., Holland J., Koch C., Otmakhova N. A.   +8 more
core   +1 more source

Biocompatible artificial synapses based on a zein active layer obtained from maize for neuromorphic computing

Scientific Reports, 2021
Artificial synaptic devices based on natural organic materials are becoming the most desirable for extending their fields of applications to include wearable and implantable devices due to their biocompatibility, flexibility, lightweight, and scalability.
Youngjin Kim, Chul Hyeon Park, Jun Seop An, Seung-Hye Choi, Tae Whan Kim   +4 more
doaj   +1 more source

Resistive switching memory based on polyvinyl alcohol-graphene oxide hybrid material for the visual perception nervous system

Materials & Design, 2022
Resistive random-access memory (RRAM) is a new memory technology that can not only realize high-density storage, but also can simulate the neural synapse for use in artificial intelligence applications. In this study, we propose an RRAM device that shows
Zhiliang Chen, Yating Zhang, Yu Yu, Yifan Li, Qingyan Li, Tengteng Li, Hongliang Zhao, Zhongyang Li, Pibin Bing, Jianquan Yao   +9 more
doaj   +1 more source

Synapse: Synthetic Application Profiler and Emulator [PDF]

, 2018
Motivated by the need to emulate workload execution characteristics on high-performance and distributed heterogeneous resources, we introduce Synapse. Synapse is used as a proxy application (or "representative application") for real workloads, with the ...
Ha, Ming Tai, Jha, Shantenu, Merzky, Andre, Turilli, Matteo   +3 more
core   +3 more sources