Adiabatic superconducting cells for ultra-low-power artificial neural networks

• Andrey E. Schegolev,
• Nikolay V. Klenov,
• Igor I. Soloviev and
• Maxim V. Tereshonok

Beilstein J. Nanotechnol. 2016, 7, 1397–1403, doi:10.3762/bjnano.7.130

• . We optimize their parameters for application in three-layer perceptron and radial basis function networks. Keywords: adiabatic superconductor cells; artificial neural networks; energy efficiency; Josephson effect; superconductivity; Findings Artificial neural networks (ANNs) are famous for their

The eNanoMapper database for nanomaterial safety information

• Nina Jeliazkova,
• Charalampos Chomenidis,
• Philip Doganis,
• Bengt Fadeel,
• Roland Grafström,
• Barry Hardy,
• Janna Hastings,
• Markus Hegi,
• Vedrin Jeliazkov,
• Nikolay Kochev,
• Pekka Kohonen,
• Cristian R. Munteanu,
• Haralambos Sarimveis,
• Bart Smeets,
• Pantelis Sopasakis,
• Georgia Tsiliki,
• David Vorgrimmler and
• Egon Willighagen

Beilstein J. Nanotechnol. 2015, 6, 1609–1634, doi:10.3762/bjnano.6.165

Growth and structural discrimination of cortical neurons on randomly oriented and vertically aligned dense carbon nanotube networks

• Christoph Nick,
• Sandeep Yadav,
• Ravi Joshi,
• Christiane Thielemann and
• Jörg J. Schneider

Beilstein J. Nanotechnol. 2014, 5, 1575–1579, doi:10.3762/bjnano.5.169

• between the aggregated networks. These results are in line with the results after using micro-stamped structures of carbon nanotubes, which led to the self-assembly of neural networks [28] and to the formation of neural networks on islands of unordered CNTs [12]. After 21 days in vitro the neural network