Stochastic hybrid 3D matrix: learning and adaptation of electrical properties (Articolo in rivista)

Type
Label
  • Stochastic hybrid 3D matrix: learning and adaptation of electrical properties (Articolo in rivista) (literal)
Anno
  • 2012-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1039/C2JM35064E (literal)
Alternative label
  • Victor Erokhin ab, Tatiana Berzina ab, Konstantin Gorshkov b, Paolo Camorani b, Andrea Pucci c, Lucia Ricci c, Giacomo Ruggeri c, Rodrigo Sigala d and Almut Schüz d (2012)
    Stochastic hybrid 3D matrix: learning and adaptation of electrical properties
    in Journal of materials chemistry (Print)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Victor Erokhin ab, Tatiana Berzina ab, Konstantin Gorshkov b, Paolo Camorani b, Andrea Pucci c, Lucia Ricci c, Giacomo Ruggeri c, Rodrigo Sigala d and Almut Schüz d (literal)
Pagina inizio
  • 22881 (literal)
Pagina fine
  • 22887 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://pubs.rsc.org/en/Content/ArticleLanding/2012/JM/c2jm35064e (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 22 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 7 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 43 (literal)
Note
  • Scopu (literal)
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • a CNR-IMEM, Parco Area delle Scienze 37/A, 43124 Parma, Italy; b Department of Physics, University of Parma, Viale Usberti 7A 43124, Parma, Italy.; c Department of Chemistry and Industrial Chemistry, University of Pisa, Via Risorgimento 35, Pisa 56126, Italy; d Department of Physiology of Cognitive Processes, Max Planck Inst. for Biological Cybernetics, Spemannstr. 38, Tuebingen 72076, Germany. (literal)
Titolo
  • Stochastic hybrid 3D matrix: learning and adaptation of electrical properties (literal)
Abstract
  • Memristive devices are electronic elements with memory properties. This feature marks them out as possible candidates for mimicking synapse properties. Development of systems capable of performing simple brain operations demands a high level of integration of elements and their 3D organization into networks. Here, we demonstrate the formation and electrical properties of stochastic polymeric matrices. Several features of the network revealed similarities with those of the nervous system. In particular, applying different training protocols, we obtained two kinds of learning comparable to the \"baby\" and \"adult\" learning in animals and humans. To mimic \"adult\" learning, multi-task training was applied simultaneously resulting in the formation of few parallel pathways for a given task, modifiable by successive training. To mimic \"baby\" learning (imprinting), single task training was applied at one time, resulting in the formation of multiple parallel signal pathways, scarcely influenced by successive training. (literal)
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