000880107 001__ 880107
000880107 005__ 20230210112614.0
000880107 0247_ $$2CORDIS$$aG:(EU-Grant)861153$$d861153
000880107 0247_ $$2CORDIS$$aG:(EU-Call)H2020-MSCA-ITN-2019$$dH2020-MSCA-ITN-2019
000880107 0247_ $$2originalID$$acorda__h2020::861153
000880107 035__ $$aG:(EU-Grant)861153
000880107 150__ $$aMaterials for Neuromorphic Circuits$$y2019-11-01 - 2024-04-30
000880107 371__ $$aUniversity of London - University College London$$bUCL$$dUnited Kingdom$$ehttp://www.ucl.ac.uk/$$vCORDIS
000880107 371__ $$aUniversity of Zurich$$bUZH$$dSwitzerland$$ehttp://www.uzh.ch/index_en.html$$vCORDIS
000880107 371__ $$aQueen's University Belfast$$bQUB$$dUnited Kingdom$$ehttp://www.qub.ac.uk/$$vCORDIS
000880107 371__ $$aUniversity of Twente$$bUniversity of Twente$$dNetherlands$$ehttp://www.utwente.nl/en/$$vCORDIS
000880107 371__ $$aAGENCIA ESTATAL CONSEJO SUPERIOR DEINVESTIGACIONES CIENTIFICAS$$bCSIC$$dSpain$$ehttp://www.csic.es$$vCORDIS
000880107 371__ $$aForschungszentrum Jülich$$bForschungszentrum Jülich$$dGermany$$ehttps://www.ptj.de/$$vCORDIS
000880107 371__ $$aBielefeld University$$bBielefeld University$$dGermany$$ehttp://www.uni-bielefeld.de/(en)/$$vCORDIS
000880107 371__ $$aÉcole Polytechnique Fédérale de Lausanne$$bEPFL$$dSwitzerland$$ehttp://www.epfl.ch/index.en.html$$vCORDIS
000880107 371__ $$aUniversity of Picardie Jules Verne$$bUPJV$$dFrance$$ehttps://www.u-picardie.fr/$$vCORDIS
000880107 371__ $$aIBM Research GmbH$$bIBM$$dSwitzerland$$ehttp://www.zurich.ibm.com$$vCORDIS
000880107 371__ $$aTHE CHANCELLOR MASTERS AND SCHOLARSOF THE UNIVERSITY OF CAMBRIDGE$$dUnited Kingdom$$ehttp://www.cam.ac.uk$$vCORDIS
000880107 371__ $$aUniversity of Groningen$$bUniversity of Groningen$$dNetherlands$$ehttp://www.rug.nl/$$vCORDIS
000880107 372__ $$aH2020-MSCA-ITN-2019$$s2019-11-01$$t2024-04-30
000880107 450__ $$aMANIC$$wd$$y2019-11-01 - 2024-04-30
000880107 5101_ $$0I:(DE-588b)5098525-5$$2CORDIS$$aEuropean Union
000880107 680__ $$aLarge efforts are invested into developing computing platforms that will be able to emulate the low power consumption, flexibility of connectivity or programming efficiency of the human brain. The most common approach so far is based on a feedback loop that includes neuroscientists, computer scientists and circuit engineers. Recent successes in this direction motivate the scientific community to start working on the next big challenge: using materials that emulate neural networks. For that, new players are needed: material scientists, who look into alternatives to silicon in order to develop basic device units, more fitting to the needs of cognitive-type processing than current transistors. We notice that recent progress in chemistry and materials sciences (atomically controlled materials) and nanotechnology (diversity of tools to probe the nanometer scale) brings exciting possibilities for novel approaches in the area of neuromorphic computing. Clearly, the type of materials, physical responses and spatial dimensions considered in the design of neuromorphic systems will crucially determine their utilization, properties and cost, and consequently their societal and economic impact. Therefore, it is urgent that chemists and materials scientists also join forces in the development of the future neuromorphic computer. MANIC aims to offer complementary expertise to current approaches by recruiting fifteen Early Stage Researchers (ESRs) and providing them with the best possible research, academic and professional training, to prepare them for the challenge of developing advanced materials with memory, plasticity and self-organization that will perform better than the current solutions to emulate neural networks and, eventually, learn.
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