COGNET
Cognitive Networks for Intelligent Materials and Devices
| Coordinatore | THE PROVOST, FELLOWS, FOUNDATION SCHOLARS & THE OTHER MEMBERS OF BOARD OF THE COLLEGE OF THE HOLY & UNDIVIDED TRINITY OF QUEEN ELIZABETH NEAR DUBLIN
Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie. |
| Nazionalità Coordinatore | Ireland [IE] |
| Totale costo | 2˙497˙125 € |
| EC contributo | 2˙497˙125 € |
| Programma | FP7-IDEAS-ERC
Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) |
| Code Call | ERC-2012-ADG_20120216 |
| Funding Scheme | ERC-AG |
| Anno di inizio | 2013 |
| Periodo (anno-mese-giorno) | 2013-06-01 - 2018-05-31 |
Partecipanti
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|---|---|---|---|---|
| 1 |
THE PROVOST, FELLOWS, FOUNDATION SCHOLARS & THE OTHER MEMBERS OF BOARD OF THE COLLEGE OF THE HOLY & UNDIVIDED TRINITY OF QUEEN ELIZABETH NEAR DUBLIN
Organization address
address: College Green - contact info |
IE (DUBLIN) | hostInstitution | 2˙497˙125.00 |
| 2 |
THE PROVOST, FELLOWS, FOUNDATION SCHOLARS & THE OTHER MEMBERS OF BOARD OF THE COLLEGE OF THE HOLY & UNDIVIDED TRINITY OF QUEEN ELIZABETH NEAR DUBLIN
Organization address
address: College Green - contact info |
IE (DUBLIN) | hostInstitution | 2˙497˙125.00 |
Mappa
Word cloud
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Obiettivo del progetto (Objective)
'COGnitive NETwork (COGNET) is a new technology platform for materials, sensor and device design that exploits unique and hitherto unrecognised properties of random nanowire (NW) networks. These networks—comprised of metallic or semiconducting NWs connected to each other via junctions with controllably random property distributions—lead to new and unexpected levels of connectivity that are inherently scale dependent, creating opportunities for entirely new kinds of self-organised materials and devices. We propose to establish the ground rules for manipulating connectivity in NW networks. By choosing appropriate NWs and incorporating junctions with the approprate properties COGNET will enable the fabrication of (i) intelligent materials, (ii) neural networks and (iii) memory devices. Sequenced voltage pulse and back-gating techniques will in turn address and manipulate specific junctions or sets of junctions to demonstrate even higher density memory and in the case of neural networks, the possibility synaptic plasticity and self-learning.'